With the costs of acquiring a user at USD 14,772, it makes sense to optimize your product stickiness strategies to ensure that when products fall into your users’ hands, they stay there, and you learn why they enjoy them. 

One of the most competitive markets is mobile technology applications. According to Statista, the average churn rate (customers stopping using apps) stands at 70% over three months, reaching its highest point at 97.7% for casual games. 

Merely attracting users to try your product isn’t the ultimate goal; ensuring consistent usage requires a well-defined product adoption strategy and excellent customer engagement for the perfect sticky product. 

Product-led growth hinges on cultivating product stickiness, ensuring users consistently return due to the engaging and enjoyable fulfillment of their needs.

While the average person installs eighty mobile apps—they typically use only nine daily. Therefore, you need to ensure that your products become part of every user’s daily nine for them to become sticky.

This article explores the concept of product stickiness and provides insights into crafting sticky products. It covers the definition of product stickiness, strategies for creating such products, methods for measuring product stickiness, and the utilization of data related to product stickiness.

What is product stickiness?

Product stickiness refers to the frequency with which users revisit your product due to its engaging and enjoyable nature in fulfilling their requirements.

Stickiness fuels growth by enhancing customer retention, offering account expansion opportunities, and elevating customer lifetime value. 

The ratio of Daily Active Users to Monthly Active Users (DAUs/MAUs) stands out as one of the pivotal product stickiness metrics employed to gauge the stickiness of your product.

How to make sticky products

Once you have understood product stickiness, it is time to implement it. 

The first step to making your products sticky is to evaluate your product’s usage rate through customer activity data analysis.

Step 1: Evaluate your product’s usage rate

A product stickiness score isn’t a mystical shortcut to make users instantly adore your product overnight. At its essence, it’s simply a metric revealing user engagement by assessing daily and monthly active usage after collecting customer product activity data.

Before delving further, it’s crucial to comprehend your product usage and define daily and monthly active usage:

• Daily Active Users (DAUs): This metric gauges the number of users actively using your product within 24 hours.
• Monthly Active Users (MAUs): This signifies the count of unique users undertaking a significant action in your product within the last 30 days.

You might question, “Can’t I determine my product’s engaged users by measuring DAUs and MAUs?” The answer is both yes and no.

While active user metrics showcase users performing measurable actions daily, weekly, or monthly, without comparing these metrics against your product benchmarks, you can’t gauge your product engagement levels or leverage that data effectively.

Product stickiness reveals your daily active users as a fraction of your monthly active users, providing a more critical perspective on your user base. 

It highlights your most active users, identifies potential churn, and empowers you to:

• Develop tailored promotional campaigns for users with lower stickiness scores prone to churn (for growth engineers).
• Segment users based on stickiness scores, directing re-engagement efforts towards those with low stickiness ratios (for marketers).
• Understand and target your most engaged users, reshaping marketing campaigns to appeal to similar audiences (for marketers).
• Calculate your product’s adoption rate, indicating how likely new users will continue using it.

Your product stickiness ratio transforms MAUs and DAUs from mere information into actionable product adoption metrics. 

This metric empowers product managers and growth engineers to craft a well-rounded product strategy as you begin calculating product stickiness.

Step 2: Identify the most active users

Your product stickiness score unveils which active users exhibit the highest engagement levels, presenting a potent growth strategy as you analyze user behavior. 

Rather than escalating your user acquisition budget and deploying various tactics to win over prospects, it’s more effective to target customers who are inherently inclined to use your product.

This process might feel perplexing at first, but bear with us. Product stickiness highlights your most engaged users. You can explore their use cases and demographics using product analytics, including age, location, acquisition channel, and pricing tier.

Analyzing these data points allows you to foresee the future, identifying customers more likely to respond positively to your marketing campaigns, promotional offers, and ads.

If Salesforce allocates a substantial part of its budget to target early-stage startups, the return on investment would be minimal, with a high likelihood of these new users eventually churning.

In contrast, based on its experience and stickiness rate analysis, Salesforce understands that its ideal customer is a large enterprise with tens or hundreds of Sales Development Representatives (SDRs). 

These users find Salesforce helpful and are more likely to remain committed to the product in the long run. Product stickiness empowers you to discern your ideal users, enabling a focused allocation of marketing and growth resources tailored to them.

Step 3: Uncover methods to enhance your product’s stickiness

Product stickiness is a constructive feedback loop for your product—assessing it unveils recurring patterns within your user base. Closely examining these patterns provides insights into building a superior product or attracting ideal users.

Consider this scenario: If your product stickiness analysis indicates that the most engaged users belong to teams of 11 to 50 individuals, allocating less of your budget to promote your product to solopreneurs and single users might be prudent.

Product stickiness harnesses the power of product analytics, user behavior, and demographic patterns among your ideal users to guide enhancements to your brand, tailor your product, and shape the overall experience. 

The aim is to cater to users who are more likely to develop a genuine affinity for your product.

Step 4: Review and improve product stickiness 

It’s crucial to note that product stickiness alone isn’t a standalone growth strategy. It serves as a qualitative metric, indicating the proportion of highly engaged users. 

The real impact on your product’s growth trajectory stems from how you leverage that data.

To amplify product stickiness, you must test diverse variations of your experiences of customer onboarding with DAP and in-app product messaging. 

Start posing questions like:

• Which onboarding flow yields a higher number of engaged users?
• Does our in-app guidance contribute to enhanced user activation?
• Among re-engagement email campaigns, alerts, and notifications, which effectively reactivates inactive users?
• Which user segment is more prone to churn or evolve into power users?
• Where do friction points emerge in our user flows?

The essence of testing lies in uncovering commonalities among your most active users, crafting campaigns targeting those specific data points, and refining strategies to acquire and retain users efficiently.

DAP design fosters a culture of testing, enabling the creation of multiple versions of product tours, help docs, pop-ups, or automatic localization of content to onboard users in their preferred language.

Step 5: Gather in-app user insights and NPS surveys

While behavior analytics unveils patterns in user interactions with your product, its scope has limits.

Product and behavior analytics showcase how users engage with existing features, offering insights for optimization. However, they fail to reveal which features users desire from scratch or wish to eliminate.

Ensure a seamless process by proactively seeking direct inquiries instead of leaving it to chance, recognizing the indispensability of user feedback.

A DAP facilitates the distribution of open-ended feedback surveys, NPS surveys, and other feedback collection methods. 

These surveys gauge users’ likelihood to recommend your product, accompanied by follow-up responses allowing them to express their reasons for loving or not loving your product.

Seamlessly embed these surveys in pop-ups, with extensive testing options to discern audience preferences and amass valuable product feedback.

In essence, a DAP aids product managers in comprehending user interactions with their products. Yet, this is only half of what the platform offers. 

With a DAP, you can conduct A/B tests on multiple campaigns, design patterns, or UI elements, discerning preferences. 

Doing so empowers your product team to construct a data-driven growth strategy, crafting a sticky and endearing product that users consistently return to.

How do you measure product stickiness?

User count plays a pivotal role in establishing a product’s stickiness ratio, necessitating additional studies even if they seem straightforward to compute.

To calculate the product stickiness ratio, divide daily active users by monthly active users. This measurement is indispensable for assessing the stickiness ratio’s significance.

The product stickiness rate is a crucial product metric, offering insight into the product or service’s value. It depicts the frequency with which customers return to your product or service.

The product stickiness ratio formula empowers the product manager to gauge the necessary improvements over time. It also aids in understanding the substantial differences among user types and their usage patterns.

Even changes in user behavior over a predetermined period can be monitored.

Initially, the total number of users logged in during the last 30 days must be divided by the average daily or weekly login rate to compute the average daily or weekly login rate.

Daily recalculations provide the product stickiness ratio, representing a moving average of the customer retention rate over time.

Determining user retention and attrition solely based on this rate necessitates conducting cohort analysis to segment consumers according to their use cases.

Moreover, if customers use your app sparingly, the stickiness rate may not accurately reflect their engagement. Therefore, additional metrics such as customer lifetime value, churn rate, and retention rate become crucial for a comprehensive product evaluation.

How do you utilize product stickiness data?

The DAU/MAU ratio is a valuable metric for assessing another critical aspect of a product—its health. While the quantity of users, gauged by DAU or MAU, provides information, it fails to convey the quality of your user base.

For product leaders, the stickiness ratio proves instrumental, offering insights into the frequency of user interactions, variations in usage among different user types (personas), and their evolution over time.

IMAGE – PLEASE DELETE

A DAU/MAU ratio of 0.6 indicates that 60% of monthly visitors also engage with the product daily, signifying good product health. 

It’s essential to note that a low DAU/MAU ratio doesn’t necessarily imply unhealthy product conditions, especially in cases involving multiple products.

The ratio may be low due to a combination of infrequent and dedicated users. In such instances, the focus should shift towards strategies to enhance the engagement of deeply involved users.

Remember that experience beats numbers

In the realm of product stickiness, it is imperative to consistently prioritize user experience over relying solely on metrics within your customer success teams. 

While metrics such as DAU/MAU ratios undoubtedly offer valuable insights, the true essence of sustaining user engagement lies in meticulously crafting an immersive and valuable customer journey. Quantitative metrics alone may fall short of capturing the qualitative nuances that underpin user loyalty and retention.

Shift the focus towards creating an experience that resonates deeper, fostering genuine connections between users and your product. The aim is to develop an understanding that encourages long-term user returns and prompts users to advocate for your product within their networks due to the inherent quality of their interactions.

Upon achieving this holistic approach to user experience, the desired usage metrics will naturally and promptly follow suit, reflecting the success of your strategy in cultivating lasting user engagement.

The post What is product stickiness, and how to make sticky products? appeared first on Digital Adoption.

The plus side is the powerful abilities it grants to organizations for automation and customer success. Still, the downside is generative AI risks, which you must consider before AI digital adoption.

In January 2023, ChatGPT, the generative AI tool, became the most popular IT strategic trend and recorded five million users in the first week of release.

It is essential to ask whether these users and subsequent users of generative AI tools considered the risks before making this tool part of their daily workflow. 

To help you understand the top ten generative AI risks and learn how to stay safe, we will explore the following topics:

• How significant are generative AI risks?
• What are the top 10 generative AI risks?
• How do you stay safe from generative AI risks?
• What use cases show the risks of generative AI?
• How will generative AI impact the business world in the future?

How significant are generative AI risks?

Significant generative AI risks depend on a few factors, but it mainly relates to how the user interacts with the tool, their knowledge, and what data they input. 

When establishing the significance of these risks in your organization, ask yourself these questions:

• How familiar is our organization with the usage terms of the generative AI tool we are considering?
• Do we enter any sensitive employee or customer data into generative AI tools?
• Are we trained on how to use generative AI safely and avoid risks, and have we looked at artificial intelligence statistics so we understand its capabilities?
• Do we have a usage policy on using generative AI and dealing with potential risks?
• What special considerations should we make based on our field, such as ChatGPT insurance or ChatGPT sales?

Consider these questions to establish the significance of the risks of generative AI to your organization. 

What are the top 10 generative AI risks (with examples)?

Generative AI: The Big Risks (and Some of the Best Uses)

There are ten generative AI risks. See these risks below, with examples to illustrate what they look like, allowing you to prepare for them as you encounter them.

1. Data and security leakage

Inputting sensitive third-party or internal company information into ChatGPT integrates it into the chatbot’s data model, exposing it to others with relevant queries. 

Doing so poses the risk of data leakage and could violate an organization’s data retention policies.

Example

Avoid sharing details about an upcoming product, including confidential specifications and marketing strategies, that your team is assisting a customer in launching with ChatGPT. This precaution helps mitigate the potential for data leakage and security breaches.

2. Intellectual property complexities

Deciphering ownership of code or text generated by ChatGPT can be complex. As per the terms of service, the output is the responsibility of the input provider. 

However, complications may arise if the output includes legally protected data from other inputs that may not follow AI compliance practices.

Copyright problems may also emerge if generative AI is used to create written material derived from copyrighted property.

Example

Consider a scenario where a user requests ChatGPT to create marketing material, resulting in an output that includes copyrighted content from external sources. 

These outputs lack proper attribution or permission, posing a potential risk of infringing upon the intellectual property rights of the original content creators. 

Such actions could result in legal consequences and damage the company’s reputation.

3. Limitations on AI development

Some generative AI’s terms of service explicitly forbid developers from using it to build other AI systems. 

Such usage could hinder future AI development initiatives, primarily if the company operates in that domain.

Example

Imagine a company specializing in voice recognition technology aiming to enhance its current system by integrating ChatGPT’s natural language processing capabilities. 

The explicit prohibition in ChatGPT’s terms of service challenges realizing this enhancement within the specified restrictions.

4. Open-source license compliance

Suppose some generative AI utilizes open-source libraries and incorporates that code into products. In that case, there’s potential for violating Open Source Software (OSS) licenses like GPL, leading to legal complications for the organization.

Example

For example, suppose a company employs ChatGPT to generate code for a software product, and the origin of the GPT training data is unclear. In that case, there’s a risk of violating terms in open-source licenses associated with that code. 

This action may result in legal complexities, including accusations of license infringement and the potential for legal action from the open-source community.

5. Confidentiality and liability concerns

Revealing confidential customer or partner information may violate contracts and legal obligations. 

If ChatGPT’s security is compromised, exposing confidential content poses risks, endangering the organization’s reputation and leading to legal liabilities.

Another risk involves staff using ChatGPT without proper training or IT approval employing shadow IT or shadow AI practices. This action makes monitoring and regulating the AI tool’s usage challenging.

Example

Imagine a healthcare organization using ChatGPT for patient inquiries. 

Sharing confidential patient details, like medical records, with ChatGPT may violate legal obligations and infringe on patient privacy rights under laws like HIPAA in the United States.

6. Research bias

AI system biases can stem from various sources, including skewed training data, flawed algorithms, and unconscious human biases. These biases may yield discriminatory or unjust outcomes, harming users and undermining trust in AI solutions. 

AI systems like ChatGPT may manifest inaccurate information due to demographic, confirmation, and sampling biases.

Example

For instance, if historical business data carries gender bias, ChatGPT might inadvertently perpetuate these biases in responses. 

This bias can distort representations of research output and result in low-quality research regarding an enterprise’s recruitment and retention history.

In chatbot applications, ChatGPT may provide inaccurate information to customers.

7. Unclear international law privacy and compliance

Leveraging generative AI’s capabilities, malicious actors can exploit it to craft malware, create content for phishing attacks and scams, and execute cyber assaults with dark web-sourced data. 

Example

For example, ChatGPT could collaborate with bots to generate deceptive fake news articles and misleading content, deceiving readers.

8. Crime-as-a-service (CaaS)

Generative AI could accelerate the creation of no-cost malware, making crime-as-a-service more convenient and profitable. 

Example

For instance, cybercriminals might misuse ChatGPT to generate abundant spam messages, overwhelming email systems and disrupting communication networks.

9. Model vulnerability

Model security is crucial as AI faces vulnerabilities, including adversarial attacks.

Example

Attackers manipulate inputs to yield incorrect outputs, risking significant consequences. Designing and developing secure models is vital to resist such threats.

10. Explainability

AI systems, at times, obscure decision processes, complicating understanding. Transparency absence breeds mistrust, challenging technology credibility.

Example

When you use a chatbot to streamline your customer service but don’t know why ChatGPT leads customers to specific undesirable answers, it makes it difficult to understand how to redirect how this tool guides customers. 

How do you stay safe from generative AI risks?

Follow these strategies to stay safe from generative AI risks. 

To ensure safety using generative AI, follow these five strategies, starting by regularly reviewing the privacy policy and terms of use.

1. Carefully examine generative AI privacy policy and terms of use 

Carefully review ChatGPT’s privacy policy and terms of use before sharing personal information with generative AI tools.

Bookmark these documents for regular reference, as they might change without prior notice.

Refer below for a list of privacy policies and terms of use for the most popular generative AI tools:

• BARD generative AI additional terms of service
• PaLM generative AI additional terms of service 
• Claude terms and conditions
• ChatGPT privacy policy 

2. Deactivate chat history and model training

Safeguard your ChatGPT conversations by turning off chat history and model training in your ChatGPT account.

Opt out of using your data for OpenAI’s model training by clicking the three dots at the bottom, going to Settings > Data controls, and switching off “Chat history & training.”

Remember that even with opting out, your chats are stored on OpenAI’s servers for 30 days, accessible to staff for abuse monitoring.

3. Validate ChatGPT’s responses before considering them as facts

ChatGPT may unintentionally produce inaccurate information, commonly called “hallucination.”

If you plan to depend on ChatGPT’s answers for critical matters, validate its information with authentic citations through thorough research.

What use cases show the risks of generative AI?

It is helpful to examine risk use cases for generative AI to understand how these risks work in practice, as it will help you combat these risks and stay safe. 

Use case 1: Data scientists

Generative AI allows data scientists to produce and share data to train models without risking personal information. 

Risks for this use case

Generative AI introduces data poisoning, deobfuscation, and adversarial machine learning threats. 

The associated risk relates to the reverse-engineered synthetic data generation model, allowing adversaries to identify the source data used.

Use case 2: IT

Programmers use large language models (LLMs), a form of generative AI, to find errors in code and automatically generate documentation. 

Risks for this use case

This step introduces data exfiltration, data leakage, and data integrity threats, while documentation produced can risk revealing important system details that a company wouldn’t usually disclose.

Use case 3: Marketing

AI text generators allow marketers to produce rough copy drafts for campaigns instantaneously. 

Risks for this use case

Doing so introduces data leakage, data exfiltration, and competitive intelligence threats. Risks include public relations/client issues related to the release of text due to poor oversight and governance processes before release.

How will generative AI impact the business world in the future?

Generative AI is a relatively new technology, but it has become popular due to its cutting-edge capabilities, which will evolve quickly. 

Consider how generative AI will impact the future to remain competitive. 

LLM plugin ecosystems will enhance capabilities

Generative AI providers, such as OpenAI, are introducing plugins to enhance the core capabilities of LLMs, tailoring them to specific tasks. 

The plugin ecosystems facilitate AI integration into workflows, streamlining the deployment of AI-based solutions for diverse applications.

Emergence of open-source LLMs

Open-source LLMs are on the rise, offering alternatives to the initial proprietary models from OpenAI and others. 

For CIOs, it means increased control over data and AI operations, but it necessitates heightened expertise in model management, maintenance, governance, and hardware infrastructure.

From generalized models to specialized

The initial surge of generative AI introduced versatile models proficient across various tasks but faced challenges in specific domains. 

As generative AI focuses on specific industries, it will create purpose-specific models, enhancing proficiency in fields like banking, insurance, and HR by aligning with the unique language of these specialized areas.

Stay ahead of generative AI risks with proactive measures

Proactive measures are paramount to stay ahead of generative AI risks. 

These measures involve thorough reviews of privacy policies, deactivating chat history, and verifying responses for accuracy. 

Embracing transparency, avoiding sensitive information input, and understanding model ownership contribute to a robust defense. 

Stay vigilant, navigating the evolving landscape with informed precaution, and you will reduce the negative impact of generative AI risks while reaping their benefits.

The post What Are The Top 10 Generative AI Risks (+ How To Stay Safe)? appeared first on Digital Adoption.

It’s a real challenge. So it’s no surprise that lead generation teams have been joining forces with AI for years, as a vintage McKinsey report from way back in 2016 shows.  

However, the rise of generative AI has changed everything. Applying AI to sales opens doors to many use cases throughout the customer journey. And lead generation is no exception. Lead generation relies on big databases, repetitive tasks, and routine conversations. 

Generative AI is a natural ally of lead generation. If that’s news to you, this article will give you a rundown of how it works. 

We’ll start by providing a concise explanation of AI in lead generation. Next, we’ll introduce key techniques for leveraging AI in lead gen. We’ll then present an overview of the market’s available AI-powered tools for lead gen. 

Lastly, we’ll explore the significant risks associated with AI lead generation.

AI Lead Generation: The Basics

Lead generation is the process of identifying potential customers for a product or service. 

With AI Lead generation, the sales team applies the power of generative AI to their lead generation activities. AI tools can enhance the full process, from lead capture to outreach to the reactivation of old contact databases. 

AI has been used in sales and lead generation for some time. AI-powered analytics have helped companies use big data sets to discover the most likely prospects for years. AI tools have a track record of supporting efficient, effective, and reliable lead-generation activities. 

When we bring generative AI into the picture through ChatGPT, Bard, and 3rd party software, the benefits of AI for lead generation start to make a huge difference. 

Generative AI means that many tasks can be scaled rapidly and efficiently. Even if you spend monthly subscriptions for the right tools, AI tools can quickly deliver a startling ROI.

Right now, thought leaders in sales are keeping quiet about the limitations of AI-based lead generation. 

After all, everyone’s still in the first wave of hype about this new technology. But the challenges are easy to spot. Sales fundamentally rely on human empathy and connection in a way that the best AI bot cannot emulate. Intuition, networking, and decision-making capabilities are not AI’s strong suit.

Some of the features of AI lead generation 

Lead generation strategies include a wide range of activities. Lead gen professionals must find a target audience to generate leads, qualify leads (with lead scoring tactics), contact qualified leads, undertake lead nurturing tasks, and more. 

Sales and marketing teams may already have lead-generation strategies in place. However, generative AI can enter the process at every stage. Let’s take a look at some important possibilities for AI lead generation. 

Social media scraping

AI can help collect data from social media platforms like LinkedIn. 

With the right prompts, you can use AI to filter LinkedIn contacts by searching their profiles for specific and relevant terms. This helps identify potential leads that align with your criteria, saving both time and effort.

AI can also be employed to extract precise data from a large pool of profiles swiftly. This automated approach is a game-changer, as it accomplishes tasks that could take hours, days, or even weeks in just an instant. 

It’s important to note that while AI streamlines the process, compliance with platform rules is crucial to avoid any potential violations.

Segment leads quickly and effectively

A 2023 McKinsey report on AI in Sales explains that segmentation is one of the core capabilities of AI. With advanced AI algorithms, marketing and sales teams can ” identify segments with unique traits that may have been overlooked in existing customer data.”

Swift processing and deciphering massive datasets unveil valuable insights, identifying distinctive buying patterns and behaviors. 

This data-driven approach enables sales professionals to pinpoint precisely when and how to engage potential customers, tailoring their strategies accurately. 

With Generative AI as their ally, sales teams are better equipped to deliver personalized and compelling sales pitches that resonate with their audience’s unique needs and preferences, ultimately driving higher conversion rates and sales success.

Cold outreach

Once you’ve gathered your contact lists, modern generative AI lead generation tools can take your outreach to the next level. Personalization is the key for any business owner looking to generate leads, especially high-quality ones.

Text-based outreach is made effortless with generative AI. You can use chatbots to craft personalized messages by setting simple parameters. Sending these messages to your leads is as simple as a few clicks.

Another noteworthy advancement lies in AI-generated videos for personalized outreach. Creating a hundred unique videos has become fairly straightforward, enabling you to engage with your leads in a more personalized and impactful manner.

In one surprising statistic, a report from Gartner predicts that 30% of outbound marketing messages will be AI-generated by 2025.” 

Looking at the latest tech, it’s certainly possible. 

Lead generation Chatbots for initial contact

Chatbots are going to revolutionize the whole of the customer journey. But they are especially valuable when it comes to lead generation.

Bots act as initial filters for potential customers, especially cold leads. When a cold lead interacts with your website, the chatbot can swiftly gauge their interest and intent. 

The chatbot determines if the lead aligns with your ideal customer profile through specific questions and response analysis, aiding lead qualification. Should the lead meet the criteria, a seamless handover to a sales representative for further engagement is possible.

This streamlines the lead qualification process and ensures that your sales team spends their time on the most promising leads, ultimately boosting sales efficiency and conversions.

Chatbots are likely to be a major business sales tool in the future. This may seem a bit strange. But we’re not so far away from this reality. 

Reviving a dead database

While AI is gradually becoming integral to the entire lead generation process, it currently holds a unique role for older databases of potential leads.

With a “dead” database in hand, lead generators need some serious power to sift through the results: AI-powered engines are uniquely capable of this task. 

First, they identify segments within the database that show surprising promise. AI aids in swiftly identifying these segments and enables the automation of messages. 

Leveraging AI can help uncover overlooked or dormant opportunities and possibilities. This revitalization process allows you to tap into the full potential of your existing database and engage with high-quality leads effectively.

Database revival uses all the strengths of AI to create great results. But a human being must still operate the tools. 

Experienced lead generation experts, such as Dan Wardrope and Jamie Woods, are demonstrating their expertise and showing us how it’s done. You can see their valuable insights in action here.

AI lead generation tools to make your sales funnel move 

How will you start generating leads with artificial intelligence? The simplest way is with one of many third-party tools. 

When other AI lead-generation efforts fail, they prove to be highly valuable. For instance, scenarios like onboarding new staff, providing specialized training, and implementing prompt engineering from within may yield different results. 

If you’re looking for rapid results, these tools could help you reach your target audience: 

1. Instantly AI: Instantly AI offers artificial intelligence solutions to help businesses automate their processes and improve efficiency.
2. LeadZen: LeadZen is a lead generation company that uses AI to provide high-quality business leads.
3. Lyne AI: Lyne AI develops chatbots for businesses, helping them automate customer interactions and deliver excellent customer service.
4. Smartwriter: Smartwriter is an AI-powered writing assistant that can generate content across various formats, including emails, blog posts, and social media posts.
5. Copilot AI: Copilot AI leverages artificial intelligence to automate sales outreach, helping businesses connect with their target audiences more effectively.
6. High Level: High Level is an all-in-one marketing platform for businesses, combining CRM, email marketing, and other functionalities.
7. Gong.IO: Gong.IO uses AI to analyze sales calls and meetings, providing insights to help sales teams close deals more efficiently.
8. Enrich with AI: Clearbit’s Enrichment tool uses AI to provide businesses with comprehensive data about their customers, helping them make informed decisions.
9. Reachout with AI: Reachout with AI is a platform that uses artificial intelligence to automate and personalize email outreach.
10. bHuman.ai: bHuman.ai is an AI-powered platform that helps businesses build more human-like digital experiences.
11. Task Genie: Task Genie is an AI-powered task management tool that helps teams streamline workflows and improve productivity.
12. Getlead.ai: Getlead.ai is a lead generation platform that uses artificial intelligence to help businesses find and connect with their ideal customers.
13. Chatbros.ai: Chatbros.ai is a chatbot development platform that allows businesses to automate their customer service and improve customer engagement.

There are plenty of obvious advantages to using 3rd party software. 

These tools often bring expertise, advanced algorithms, and extensive data resources that may surpass in-house capabilities. 

Tapping into external AI solutions allows businesses to access more accurate lead insights, faster data processing, and innovative lead generation techniques. This enhances their efficiency in identifying and engaging high-quality prospects.

The risks (and limits) of AI lead generation

Like any business application of AI, AI lead generation mixes opportunities with AI risks. 

These risks of AI, in general, are well-documented. But what about the specific challenges when it comes to AI? 

Before you rush head-first into adding AI to your lead generation processes, consider these weak points: 

• Data scraping is prohibited by the terms of service on many social media platforms, including LinkedIn. If you use ChatGPT or Bard for invasive purposes – you could end up in big trouble with the LinkedIn police!
• Personalization still isn’t perfect. Of course, the developers of AI sales tools boast that their tools produce “real-life” content. But will customers be fooled? Tread carefully if you are using AI for customization.
• Privacy is a major issue. We still don’t know how AI engines use, retain, and re-package data. Mishandling or data breaches can lead to major legal and reputational issues.
• Costs and Investment. Implementing AI solutions can be costly, and businesses may not see a return on investment if the technology is not used effectively—the AI-powered tools listed above all charge hefty monthly subscription fees. 

Is AI the future of the lead generation process? 

Investing in an advanced AI lead generation tool can significantly propel your sales strategy forward.

After all, how many companies DON’T need to generate quality leads regularly? AI brings major efficiencies over traditional methods. 

This doesn’t mean you need to replace all of your sales staff overnight, far from it. 

As we’ve seen, AI tools can be most useful when taking over ONE part of the lead generation process at a time. You don’t need to convert every lead generation task to AI overnight to feel the benefits. 

Reviving a dormant database using AI hacks alone would be sufficient in and of itself.

More and more tools are being rolled out specifically to support lead generation. As the months roll by, finding an application that works well for your business needs will become easier and easier. 

The post AI Lead Generation: A Beginner’s Guide appeared first on Digital Adoption.

So, while AI adoption has quickly grown across many industries, the sector-specific pathways to AI maturity are unique. 

Globally, the financial services industries have emerged as the most significant early adopters of AI solutions. Healthcare and retail are hot on their tails.

In this article, we’ll dive into the realities of AI adoption in several of these key industries. We will:

• Examine the large-scale factors that make a difference to AI adoption rates;
• Summarize the barriers to AI adoption across industries;
• Explain examples, risks, and future plans for AI in the business operations of three key industries (finance, healthcare, and retail).

Since the advent of generative AI in late 2022, businesses have had to quickly get ready for an AI digital adoption process. Now that we have detailed research from international foundations, we’re starting to get a clearer picture of how AI adoption might work in the years to come.

And by the way, although we will mention “AI” in general, this article will focus on the new kid on the block generative AI  – in other words, AI in the age of ChatGPT and Bard.

AI adoption rates: what makes a difference?

As we move towards the end of 2023, why don’t more businesses use AI solutions? 

After all, in many standard white-collar workplaces, AI has a clear support role for the workforce. And 2023 research has shown that the costs of AI software and hardware are dropping dramatically. 

Although there are numerous technical risks and barriers, AI adoption depends on all kinds of factors. For example:

AI adoption can vary according to different business functions

Whether that’s HR, marketing, sales, or logistics. As March 2023 research from Statista shows, AI adoption rates are uneven across business functions with sectors, and across sectors.  

The term “AI” is a wide umbrella term

There are many types of artificial intelligence (and many types of generative artificial intelligence) and their adoption rates are not even. As US Census Bureau researchers demonstrated in a 2023 working paper,  different types of tech are adopted at radically different rates. If you want to be sure about AI adoption, you must be clear about the specific AI tools and AI technologies you are interested in.

AI Adoption varies significantly depending on job role.

42% of HR leaders surveyed in Gartner in 2023 leaders expected entry-level positions to be significantly impacted by AI. The same thing has, of course, happened before. The arrival of desktop word processing didn’t change things for CEOs. But in time, it resigned the secretarial “typing pool” that processed so many documents into history. AI adoption will be different at all levels of the company. 

AI adoption rates vary by country, continent, and geographic region. 

This connects with the relative cost of labor, the levels of digital transformation and digital maturity, and by industry. 

These factors remind us that the idea of “AI adoption” is far more complicated than we might want. In this article, we’ll keep things simple by focussing only on a sector analysis. We believe this is a useful choice, as in globalized industries, sector-specific demands are likely to be a driving force for more extensive AI adoption.

Cross-sector barriers to AI Adoption

Some of the barriers to AI adoption are present across all industries.

After the past year of global excitement about ChatGPT, extensive research has revealed many of the challenges of adopting AI in different businesses.

Looking at 2023 research from Statista, KPMG’s Generative AI Survey from 2023, and McKinsey’s “State of AI” report from 2023, some of the common barriers are:

• Recruiting specialist AI experts  

• Cost of AI implementation
• Lack of tools or platforms to develop models or AI algorithms
• Data complexity and related data privacy issues
• Project complexity
• Regulation and compliance
• Updating legacy software
• Customer expectations
• Workforce impact, including redundancies
• AI Strategy with ROI 
• Risk management
• Shadow AI

With these in mind, let’s take a look at the opportunities, risks, and future of AI applications in financial services, healthcare, and retail.

AI adoption in financial services

Financial services has been using AI more extensively than most other sectors. To understand the lay of the land, we’re lucky to have thorough 2023 research published by the International Monetary Fund (IMF). 

Examples of AI adoption in financial services

Generative AI has significant real-world applications for financial services business. 

Companies are showing how it can enhance fraud detection, improve investment decisions, automate document processing, and power in-house software development. 

These are proven, not speculative, use cases, with global companies harnessing generative AI to optimize their processes.

Risks

Amid the rapid AI adoption in finance, the sector grapples with well-acknowledged risks. 

These include issues of bias, privacy breaches, opacity, fragility, and cybersecurity threats. The sensitive nature of financial data magnifies these concerns, impacting wider financial security. 

Legitimate fears of Shadow AI have led numerous institutions to ban ChatGPT and similar AI tools.

The future

The future of the financial sector’s AI use promises innovation but carries limitations. 

Well-known problems with AI like hallucinations and context comprehension are especially significant. Vigilant monitoring is vital to ensure alignment with industry standards. 

Continuous oversight is crucial to strike a balance between AI benefits and risk mitigation, maintaining precision, transparency, and security in financial services.

AI adoption in healthcare

The healthcare industry already has a clear track record of digital adoption. AI tools are just the next step in the industry’s progress towards better outcomes. 

But for healthcare, the challenges of change management are especially precarious. Healthcare needs extremely careful change management – after all, a mistake could lead to very serious consequences. 

So it’s no surprise that a leading 2023 HBR article says that AI adoption “won’t be easy” in healthcare. But how can AI support healthcare? what are the risks? Let’s take a look.  

Examples of AI adoption in healthcare

AI tools have some very interesting possibilities in the healthcare sector. In the background, AI can accelerate scientific discovery; and on the client side, it can support healthcare professionals to make informed decisions. 

It can sometimes improve medical advice given to patients. And “under the bonnet”, it can alleviate the onerous burden of administrative tasks and paperwork. 

Risks

In healthcare, the transition to AI-driven solutions comes with high risks. The phenomenon of “switchover disruption” is not just an inconvenience – it’s about safeguarding patient lives and data. 

Moreover, doctors, nurses, and other support staff may themselves resistant to any new technology that might threaten their patients. 

As a result, shadow AI must be treated very carefully. Uncertain risks such as AI hallucinations and privacy breaches can have life-or-death consequences, depending on their position within the healthcare system.

The future

Since healthcare faces similar technical challenges to finance, it’s very important for companies to monitor their AI systems carefully. 

Companies in this position could use a digital adoption platform that is capable of handling AI. However, AI adoption in healthcare must also navigate the resistance to change in a compassionate field. 

This is a “human” challenge – not a technical one. Leaders must give staff a clear narrative about AI’s role, and actively reassure patients that their safety and privacy are their highest concerns. 

AI Adoption in Retail

In retail, adopting AI will change business processes and customer experience. There’s no “life or death” in retail. It’s just a matter of profit, efficiency, and good business. 

All businesses will be keen to implement AI in at least one function, consumer retailers are currently approaching AI adoption with caution. 

The US’s National Retail Federation has produced a particularly useful report on how the retail sector is handling it. 

Examples

In 2023, fully implemented case studies for the use of AI in retail are still emerging. The sector-changing plans are still speculative. 

These prospects include many possibilities. AI-driven inventory tracking across the supply chain, personalized customer styling recommendations, and generating AI models tailor-made for specific applications, are all on the horizon of global retail. 

AI also proves its worth in predicting in-store foot traffic, facilitating customer engagement through responses to reviews, enabling dynamic pricing strategies, and tailoring marketing communications on a customer-by-customer basis. 

Risks

In retail, as in the entertainment industry, the shift from creative human work to AI-generated output has sparked significant criticism. 

A critical concern revolves around the potential for mass unemployment if human roles are supplanted by AI counterparts. 

The sector leaders must strike a delicate balance. On the one hand, they should leverage AI’s efficiency – and on the other, safeguard employment among the global workforce. That’s a big challenge! 

The future

The future of AI in retail hinges on identifying the “right” opportunities, especially when existing systems function smoothly. Many businesses are taking a cautious approach, waiting to see how AI can seamlessly integrate into their business models. 

The most promising applications often emerge in multi-site businesses, requiring substantial infrastructure investment. Initiatives like the NRF working group are poised to bolster the adoption of industry-wide AI applications, providing a supportive ecosystem for the retail sector’s AI-driven transformation. 

As the industry navigates this transformative path, collaboration and innovation will be key to harnessing AI’s potential for enhanced customer experiences and operational efficiency.

AI adoption – what do we do now? 

In this article, we’ve focussed on the ins and outs of different industries- rather than AI adoption statistics, the range of digital technologies available, or the demands of natural language processing. Those are topics for another day.

There’s no doubt that many of the challenges and opportunities for generative “AI adoption” cut across businesses and sectors. That’s why so many third-party app developers are making good money from AI-driven software. 

But as soon as you go deeper, there are many factors that make a difference to AI adoption practices. As we’ve seen, the opportunities and challenges are different across sectors – but the specific business units, locations, and job roles will all make a difference too. 

As a result, tech-savvy business leaders should not be adopting AI solutions without the appropriate level of diligence. After all, we still haven’t got a fully mature ecosystem of AI tools. At the end of 2022, a Gartner analysis suggested that Generative AI was just a phase at the start of a “peak of inflated expectations” in AI’s hype cycle. 

So. 2023 is not the time to sit still – but it’s also not the time to go crazy. Now’s the time to prepare, listen, adapt, and implement when appropriate. And then, as the right solutions come, you will be able to implement them for maximum business results.

The post AI Adoption: A sector-by-sector guide appeared first on Digital Adoption.

Yet, caution is warranted. The excitement around AI’s potential must be balanced with a thorough understanding of associated risks, particularly in AI risk management and regulatory compliance. 

This is precisely where an AI Acceptable Use Policy (AI AUP) becomes essential.

Alongside other risk management strategies, an AI AUP supports long-term stability for generative AI in your organization. 

This article will provide a comprehensive overview that: 

• Explains the different meanings of an AI Acceptable Use Policy 
• Examines the key characteristics of any AI Acceptable Use Policy
• Gives an overview of the businesses that will need an AI Acceptable Use Policy
• Points out some further reading about AI ethics. 

Generative AI makes us stop and ask many new questions about how we deploy business IT. A usage policy is just one of the key steps you need to take to make sure that AI does you more good than harm.    

What is an AI Acceptable Use Policy?

An AI Acceptable Use Policy (AI AUP) is a set of guidelines that outline the responsible and ethical use of artificial intelligence within a specific context. 

Typically an AI AUP addresses issues such as data privacy, transparency, fairness, and compliance with relevant laws and regulations. 

The policy supports best practices in AI, ensuring that the organizational use aligns with ethical and legal standards.

Introducing an AI use policy is a guiding framework for many of the risks associated with AI. 

A policy safeguards sensitive data, promotes transparency, and mitigates biases in AI decision-making. It also assigns accountability and responsibility, fostering trust among stakeholders. 

Organizations with an AI AUP protect themselves from potential legal and reputational risks and demonstrate their commitment to using AI for the benefit of the organization, its stakeholders, and society at large.

The key characteristics of an AI Acceptable Use Policy

Each business has the power to decide on what “acceptable use” means for them. 

Your uses of technology, customer needs, and staff experience may all make a difference to the type of responsible AI use you expect. 

So, what will be part of your AI acceptable use policy? Some of the key aspects are likely to be: 

• Compliance with Laws and Regulations

• Data Privacy and Security
• Transparency
• Fairness and Bias Mitigation
• Accountability
• Use Case Guidelines
• Ethical Considerations
• Monitoring and Auditing
• Training and Awareness
• Data Quality and Accuracy

But acceptable use policies are actually quite fickle things. 

Many use policies emphasize privacy, transparency, and accountability (these are all important matters) – according to a 2023 meta-analysis of global policies. 

However, not so many valued truthfulness, intellectual property, or children’s rights (which are much more important – even if they are harder to police).

Which businesses need an AI Acceptable Use Policy? 

Introducing an AI AUP should be a priority for a wide range of businesses, particularly those that heavily rely on or are influenced by artificial intelligence. 

Here are some types of businesses that should prioritize the establishment of such policies:

Tech Companies and AI Providers

Tech companies, especially those providing AI services, should lead the way in implementing AI AUPs. These policies not only guide their internal operations but also set industry standards. 

Healthcare and Financial Institutions

Businesses in the healthcare and financial sectors handle sensitive and highly regulated data. It is critical in these industries to safeguard patient health information and financial data, maintain compliance with strict regulations like HIPAA and GDPR, and ensure the ethical application of AI in decision-making processes.

Large Corporations 

Large corporations with diverse operations and extensive data handling processes should establish AI AUP as a matter of priority. These policies can provide a framework for responsible AI usage, preventing unauthorized AI applications, and ensuring transparency, accountability, and compliance across all departments. They also serve to mitigate risks related to data breaches and operational disruptions.

Startups and Innovative Enterprises

Even smaller businesses and startups should prioritize the implementation of AI AUPs as they integrate AI into their operations. These policies help set clear boundaries for AI usage from the outset, preventing ethical and regulatory pitfalls as they grow and expand their AI-driven initiatives.

Education and Research Institutions

Educational institutions and research organizations should introduce AI AUPs to guide the responsible use of AI in teaching and research. These policies can help ensure that AI technologies are used ethically and that data privacy is protected when conducting research or deploying AI in educational settings.

In summary, AI Acceptable Use Policies are essential for a wide spectrum of businesses, but they are particularly critical for tech companies, healthcare and financial institutions, large corporations, startups, and educational institutions.

What are the business use cases for an AI Acceptable Use Policy?

At present, you’ll find there are three business-specific use cases for an AI usage policy. They are as follows:

AI Providers

Companies offering AI services, like ChatGPT, Google Bard, and Meta’s Llama, recognize the critical need for use policies. 

These policies establish clear terms of use and ethical guidelines, such as those found in OpenAI’s usage policy, Google’s generative AI terms of service, and Meta’s Llama acceptable use policy. 

They help maintain accountability and promote responsible AI usage, ensuring transparency and compliance with legal and ethical standards.

Third-party app developers

Prominent entities like Salesforce have introduced AI-acceptable use policies for their product development. 

This proactive approach assures customers that their data will be handled with care and consideration. 

Salesforce’s commitment to its August 2023 policy demonstrates a commitment to responsible AI practices, enhancing trust and accountability.

End-user corporate users

As businesses increasingly integrate AI into their operations, employees often explore AI applications independently, sometimes without proper regulation. 

This has prompted the need for AI-acceptable use policies. 

To avoid issues like unauthorized AI usage and data leaks, some companies have taken drastic measures, as seen when Samsung temporarily banned unauthorized AI applications following an internal data breach in May 2023. 

Implementing an AI-acceptable use policy is a proactive solution to monitor and regulate AI usage within organizations, ensuring ethical, compliant, and secure AI practices.

AI Acceptable use and the ethics of AI tools

AI is a diverse landscape of emerging technology – and your company may need a more nuanced approach to the problems. 

Fortunately, the field of AI ethics is helping businesses and governments to really understand the challenges.

The following resources will help to discover more about the use of AI technology. 

• Some organizations are shedding light on the broader considerations of morality and ethics in AI. Organizations such as the Center for AI Safety have taken a pioneering role in developing comprehensive guidelines to address the ethical dimensions of AI. These guidelines tackle the fundamental questions that arise from the use and development of AI, establishing a foundation for responsible AI deployment.

• AI use is not identical across every business. So, domain-specific AI guidelines can help to focus your attention on the critical issues in your industry. Whether it’s AI in college admissions, legal practice, healthcare, or talent development, these applications come with their own set of ethical dilemmas. Industry bodies are starting to provide valuable insights into the evolving ethical considerations surrounding AI in these specialized contexts.

• Some organizations are keeping a close eye on how AI policy is developing as a business sub-discipline. For example, Algorithm Watch keeps a global inventory of AI ethics guidelines. Whatever your business, these resources can offer you make comparisons about how companies negotiate the inherent risks of AI. 

AI Usage policies and the future of digital trust 

In the journey to use AI responsibly, having an AI Acceptable Use Policy is essential, but it may not be the first thing you do. As we’ve seen, you must think carefully about the ethical challenges and opportunities that AI brings, even when those are not yet clear. By doing all of this, organizations can continue to build digital trust, which is crucial in using AI safely.

The post AI acceptable use policy: minimizing harm (before it happens) appeared first on Digital Adoption.

These are the systems that have been running your critical operations, possibly for decades. 

However, in today’s fast-paced digital world, the term “legacy” often equates to outdated and inefficient— and almost everyone knows it.

A survey by InfoSys in 2023 found less than 1% of surveyed business executives said their organizations had yet to start on legacy application modernization.

Why? Because modern, efficient technology applications drive business growth.

In this article, we’ll teach you how to navigate the waters of legacy application modernization. This guide will help you revitalize your aging software and transform your business processes for the better.

What is legacy application modernization?

Legacy application modernization is the process of updating and transforming outdated software applications to align with current technologies and business strategies. 

This can involve moving to cloud-based platforms, rewriting the application’s codebase, updating user interfaces, or integrating with modern systems and APIs to enhance functionality, performance, and interoperability. 

The goal is to retain valuable legacy applications’ functionality while reducing maintenance costs, improving user experience, and ensuring compliance with current standards and practices. 

Modernization allows organizations to stay competitive and agile in a rapidly evolving technological landscape.

7-step legacy application modernization process

While digitizing existing processes can help you kickstart your digital transformation, the process of modernizing legacy applications isn’t just about keeping up with technology trends; it’s a strategic move to enhance agility, efficiency, and competitiveness in a market that waits for no one.

But how do you start this journey? 

If you’ve already tried modernizing your legacy apps, you might have found yourself grappling with the complexity of your existing systems and facing the daunting task of updating them without disrupting your business. 

That’s where our definitive 7-step guide comes into play. 

Step 1: Assessment and planning

Before you dive into modernizing your legacy applications, you need a clear understanding of what you have, what you need, and how to prioritize your actions. 

Carrying out a digital transformation assessment can help you out a lot through this process.

Start by taking inventory of all the existing applications within your business. Assess each application’s functionality, usage, and business value. 

Ask yourself: 

• Does this application still meet our business needs? 
• Can it integrate with new technologies? 
• What risks does it pose to our business, and what opportunities could we seize by updating it?

With this information, you can plan your modernization roadmap. 

This involves deciding which applications to retire, which to update, and which to replace. Prioritize your applications not just by their current state, but by their strategic value to your business. 

Some might need immediate attention because they pose security risks or incur high maintenance costs. Others might be candidates for a phased approach, updated in increments to minimize disruption to your operations.

Your plan should also outline the scope and scale of the modernization project. Set realistic timelines, allocate resources, and prepare for potential challenges. 

You’ll also need to engage stakeholders across your organization to ensure that the modernization efforts align with broader business objectives. 

By the end of this step, you should have a solid foundation for your modernization initiative— one that’s informed by careful assessment and strategic planning.

Step 2: Defining modernization objectives

Once you have assessed your legacy applications and have a plan in hand, the next crucial step is to define your modernization objectives. 

This step sets the stage for the desired outcome of your modernization efforts. You need to pinpoint exactly what you want to achieve through this process. 

Is it to increase operational efficiency? 

Enhance security? 

Improve customer experience? 

Reduce costs? 

Or perhaps it’s a combination of these objectives.

For each objective, establish clear and measurable key performance indicators (KPIs). 

These metrics will allow you to measure and track digital adoption and gauge the success of your legacy application modernization initiative. 

It’s also important that you align your modernization goals with your overall business strategy. This ensures every update contributes to the broader vision and direction of your company.

Consider the end-users of your applications during this process. Understand how modernization will impact their day-to-day activities and what improvements they expect to see. Your objectives should address their needs if you want to ensure the updated system provides tangible benefits.

You also need to factor in compliance, security, and operational requirements at this stage. 

Identify any industry-specific regulations that your updated applications must adhere to, and incorporate the necessary controls into your objectives. 

By setting comprehensive, strategic, and well-informed objectives, you create a targeted roadmap for the modernization process that is likely to result in a successful outcome.

Step 3: Choosing the modernization strategy

Selecting the right modernization strategy is pivotal. 

It’s not a one-size-fits-all situation; the strategy you choose must align with your objectives, technical requirements, and business constraints. 

Here are 5 common approaches you should consider:

• Rehosting: Often referred to as “lift and shift,” this strategy involves moving an application to a new hardware environment or cloud platform without redesigning the app.
• Refactoring: This involves making improvements to the code so it fits better with the new environment, which often means cloud optimization.
• Rearchitecting: Changing the application’s fundamental architecture to align it with modern requirements, often transforming monolithic applications into microservices.
• Rebuilding: Rewriting the application from scratch while preserving its scope and specifications.
• Replacing: Substituting the legacy application with a completely new software, typically off-the-shelf solutions.

Weigh the pros and cons of each strategy carefully:

• Rehosting might be quick and cost-effective but may not yield the same long-term benefits as more intensive methods.
• Refactoring and rearchitecting offer more substantial improvements at the cost of more extensive work and potential downtime.
• Rebuilding or replacing can be transformative, providing the opportunity to leverage the latest technologies and practices, albeit with significant investment in time and resources.

Your choice should hinge on several factors, including: 

• The expected lifespan of the application
• The need for scalability
• The ability to support future business needs
• Your budget

For example, if an application is critical and needs to be scalable, rearchitecting to a microservices architecture might be the right choice. Conversely, for an application that’s less critical and nearing the end of its utility, rehosting might be sufficient.

Document the chosen strategy for each application in your plan. 

Ensure every stakeholder understands the path and reasons behind it to secure buy-in and set appropriate expectations for the project’s outcomes.

Step 4: Technology stack selection

With a strategy in hand, it’s time to choose the technology stack that will bring your modernized applications to life. 

The technologies you select will underpin your applications for years to come and need to support the objectives you’ve laid out.

Here are seven things to think about when choosing your technology stack:

1. Compatibility: Your new stack should integrate seamlessly with your existing technology environment or with the components you plan to retain.
2. Scalability: Look for technologies that can scale with your business needs. As your business grows, your applications should be able to accommodate increased load without performance issues.
3. Sustainability: Choose technologies that are supported by a robust community or vendor, ensuring that they will be maintained and updated over time, safeguarding your investment.
4. Flexibility: Your stack should allow for adjustments and expansions in the future. Avoid overly rigid systems that can become your next legacy headache.
5. Performance: Assess the performance benchmarks of your potential stack, ensuring it meets the demands of your applications.
6. Cost-efficiency: Consider the total cost of ownership, not just the upfront expenses. Some technologies may offer lower initial costs but could be more expensive to maintain over time.
7. Expertise: Evaluate your team’s expertise and the availability of skilled professionals in the market. You may need to provide training for your staff or hire new talent.

Here’s how you might decide on your technology stack:

• If you’re rehosting, your primary concern may be compatibility with cloud environments.
• With refactoring or rearchitecting, look for stacks that offer the flexibility and performance improvements you need.
• If you’re rebuilding, you have the opportunity to select cutting-edge technologies that align with modern development practices like DevOps and continuous integration/continuous delivery (CI/CD).
• When replacing, the focus might be on selecting a vendor solution that best fits your business processes with the least customization required.

Remember, the choices you make now will affect your application’s relevancy, maintainability, and effectiveness long into the future. 

Take the time to research, test, and validate your technology choices against your modernization objectives before making a final decision.

Step 5: Migration and Implementation

The migration and implementation phase is where your planning and decisions translate into action. 

This single step can be complex and demands a meticulous approach to avoid significant disruption to your business operations.

For that reason, we’ve broken it down into eight sub-steps:

1. Develop a detailed migration plan: Outline every aspect of the migration process, including timelines, milestones, roles, and responsibilities. A phased approach often works best, migrating one application or system component at a time to minimize risk.
2. Data integrity: Data is often the lifeblood of your operations, so plan for its integrity carefully during the migration. This includes data mapping, cleansing, and validation processes to ensure that no data is lost or corrupted.
3. Security considerations: Data security and application security are paramount. Ensure that security measures are in place to protect sensitive information during and after the migration.
4. Testing: Rigorous testing before, during, and after migration is essential. You’ll want to perform unit testing, system testing, and integration testing to ensure that the applications perform as expected in the new environment.
5. Communication: Keep all stakeholders informed throughout the migration process. Regular updates can help manage expectations and reduce resistance to change.
6. Contingency planning: Have a rollback plan in case something goes wrong. It’s crucial to be able to revert to the old system if necessary, without data loss or significant downtime.
7. Training: Your end-users and IT staff will need training on the new systems. Ensure that training programs are in place so that everyone is up to speed with the new processes and technologies.
8. Support: Plan for immediate post-migration support to address any issues quickly and to help users adapt to the new system.

For each of these steps, adopting an iterative approach can help. By implementing in stages, you can adjust plans based on feedback and results from each phase. 

This reduces the likelihood of major setbacks, as issues can be dealt with when they are still small and manageable.

When the rubber hits the road in this phase, remember that the ultimate goal is to ensure a smooth transition that maintains business continuity and leverages the full potential of your modernized applications.

Step 6: Monitoring and optimization

You’ve migrated legacy applications and implemented new ones, but your job isn’t over yet.

The real test begins when your modernized applications go live. 

Monitoring their performance and optimizing them for efficiency and effectiveness is a continuous process. Here are 8 things you must do:

1. Measure against monitoring metrics: You’ve already set up key performance indicators (KPIs) based on your objectives. These might include system performance, user satisfaction, or financial metrics like return on investment (ROI).
2. Use monitoring tools: Implement monitoring tools to keep track of the system’s performance. This covers tracking uptime, response times, error rates, and usage patterns.
3. Gather user feedback: Your users are a key source of insights. Collect their feedback to understand their experience and identify areas for improvement.
4. Iterative optimization: Make continuous improvements based on monitoring data and user feedback. This may involve adjusting configurations, optimizing code, or adding features.
5. Performance tuning: Adjust resources and settings based on performance data to ensure that your applications are functioning efficiently.
6. Security updates: Regularly update your applications with the latest security patches and stay aware of emerging threats.
7. Cost management: Keep an eye on operational costs and optimize resource use for cost efficiency, especially in cloud-based environments.
8. Disaster recovery planning: Continuously improve your disaster recovery plans with the insights you gain from ongoing monitoring.

Active monitoring and optimization ensure that your applications meet initial expectations and evolve with your business needs, maintaining their strategic value and supporting your business as it grows and changes.

Step 7: Embracing change for long-term success

The final step in legacy application modernization is to sustain the momentum of change for lasting success. 

This involves a continuous commitment to your team’s growth, fostering a culture of adaptability, and establishing robust support systems for your modernized applications.

Training staff on new systems and processes

Empower your team through employee training after any business transformation. The new systems and processes might be a significant departure from what your staff is accustomed to.

• Develop comprehensive training programs to bring all users up to speed with the new systems.
• Provide regular workshops and refresher courses to ensure ongoing proficiency.
• Consider mentoring or coaching to support staff through the technical transition.

Managing the cultural shift within the organization

Modernizing applications often necessitates a cultural transformation. Employees need to adapt to new workflows, which can be challenging.

• Communicate the benefits of the new system clearly and frequently to build support.
• Encourage a mindset of continuous improvement where feedback is sought and valued.
• Recognize and reward adaptability and initiative in learning the new system.

Ensuring continuous support and maintenance

Modernized applications require continuous attention to operate smoothly and evolve with your business.

• Set up a dedicated support team to address any technical issues promptly.
• Implement a maintenance schedule that includes regular updates and security checks.
• Plan for long-term support from your vendors or partners to ensure your modernized applications are always running the latest software and features.

By focusing on these areas, you ensure that the investment in modernizing your legacy applications translates into sustainable, long-term value for your organization. 

It’s not just about new software; it’s about ushering in a new era of efficiency, agility, and growth for your business.

Seizing the future with legacy application modernization

The journey of legacy application modernization is comprehensive and multifaceted. 

By following our 7-step plan, you have not only upgraded your technological assets but also positioned your business for future growth and innovation. 

Your modernized applications are now designed not just for the present but are also scalable for future demands. The work doesn’t stop here; technology and markets are ever-evolving, and staying ahead means maintaining the pace of continuous improvement.

By successfully modernizing your legacy applications, you have crossed a crucial bridge in digital transformation. This endeavor goes beyond technology—it’s a fundamental shift in how your business operates and thrives in a digital-first world. 

As you move forward, remember that the true measure of your modernization’s success will be how well it enables your business to adapt to future changes and seize new opportunities. 

Your modernized applications are not just a new foundation for your business operations; they are a launchpad for your next chapter of innovation and growth.

The post Legacy application modernization: The definitive 7-step guide appeared first on Digital Adoption.

Companies using this strategy operate internationally, utilizing resources and competencies from their worldwide network to enhance digital innovation and IT efficiency. At the same time, they adapt their offerings to align with regional preferences, norms, and regulations, ensuring relevancy in each market they serve.

If you are employed by or considering joining a transnational company, it is beneficial to grasp the concept and its mechanisms. And, if you are a small business owner looking to expand globally, implementing a transnational strategy can give you the competitive advantage needed to succeed in international markets.

This article will dive deeper into transnational strategies and explore why it is a necessary tool for businesses in today’s globalized world.

What is a Transnational Strategy?

A transnational strategy is a global expansion approach used by businesses to balance both global integration and local responsiveness. It involves coordinating global activities and operations across international borders, leveraging a company’s worldwide resources, competencies, and understanding of local markets.

Transnational strategies enable companies to function across multiple geographical borders while catering effectively to local preferences and norms. They aim to create a unified international business that adapts and responds to diverse market conditions.

A transnational strategy’s essence lies in its dual focus: global integration and local customization. It allows businesses to leverage their global presence for maximum benefit while respecting and capitalizing on local market nuances.

The result is a competitive edge on the global stage, achieved by harmonizing international scale with local market resonance.

Why is a Transnational Strategy Important?

Understanding the importance of a transnational strategy is pivotal, as it masterfully intertwines universal efficiency with local market adaptability, creating a robust framework for enhanced international business success.

Here’s why having a transnational strategy is important:

1. Global Efficiency: A transnational approach allows businesses to leverage their global presence for cost efficiencies. They can source materials from the cheapest or most available locations, manufacture in low-cost regions, and utilize economies of scale.
2. Local Responsiveness: This strategy enables companies to adapt to local market conditions, tastes, and cultural nuances. It allows them to offer customized products or services that meet the specific needs and preferences of different regional markets.
3. Resource Utilization: Companies can tap into the best resources – human, material, or knowledge-based – from across their global network, leading to improved products or services.
4. Risk Diversification: Operating in multiple markets allows companies to spread their risks. If one market encounters economic instability or decline, the impact is mitigated by the company’s presence in other, more stable or growing markets.
5. Innovation and Learning: Exposure to diverse markets promotes learning and innovation. The insights gained from operating in various regions can be shared and implemented across the company, fostering continual improvement and innovation.

What Are The Different Types of International Strategies?

It’s important to note that no one-size-fits-all approach to an international strategy exists.

Each company must evaluate its unique business objectives, resources, and capabilities when determining the most suitable type of international strategy for its goals.

Having said that, there are four different types of international strategies commonly used by businesses globally, with transnational being one of them:

Global Strategy

A global strategy is a company aiming to create and sell products or services standardized across all markets.

This approach is most effective for businesses that offer products or services with universal demand, such as technology or consumer goods. The primary advantage of a global strategy is the potential to achieve economies of scale and scope, which can lead to significant cost reductions.

It also allows companies to take advantage of market linkages and coordinate their strategies across different regions. However, a key challenge is the lack of flexibility to cater to local tastes and preferences.

Transnational Strategy

A transnational strategy seeks to strike a balance between global efficiencies and local responsiveness.

Companies using this strategy often have a dispersed yet interdependent network of operations and subsidiaries across numerous countries. They centralize some functions to reap the benefits of economies of scale and decentralize others to adapt to local market conditions.

This approach allows businesses to enjoy both cost advantages and differentiated products/services. However, managing the complexity of this strategy can be challenging due to conflicting pressures for cost efficiency and local adaptation.

Multidomestic Strategy

A multi-domestic strategy involves tailoring products or services to suit the needs and tastes of local markets.

This approach is common in industries where cultural factors, such as food and beverage or media, significantly influence consumer behavior. The main advantage of a multi-domestic strategy is its high responsiveness to local market conditions, which can lead to a better customer experience transformation and an increased market share.

However, this strategy can result in higher costs due to the lack of standardization and the need for multiple supply chains and manufacturing processes.

International Strategy

An international strategy involves leveraging domestic-based core competencies in international markets.

Companies using this strategy typically export products or services developed at home to foreign markets. The focus is on transferring and adapting the company’s existing knowledge and capabilities from the home country to overseas markets.

While this strategy allows businesses to use their strong domestic bases, it may limit their ability to adapt to local market conditions and preferences, potentially affecting their competitive position in those markets.

What Are The Benefits of a Transnational Strategy?

Exploring transnational strategies reveals benefits like global efficiency, risk diversification, and innovation, providing businesses with a competitive edge worldwide.

Let’s take a closer look:

Balanced Efficiency and Responsiveness

A transnational strategy provides the flexibility to balance global efficiency with local responsiveness. 

This means companies can centralize some functions to benefit from economies of scale and scope, reducing costs and increasing efficiency. 

At the same time, they can decentralize other functions to adapt to specific local market conditions and customer preferences. 

This dual focus allows businesses to optimize their operations while still effectively meeting the needs of diverse markets.

Competitive Advantage 

Integrating resources and capabilities across various international markets allows companies to develop unique competencies, setting them apart from competitors. 

This could include superior knowledge of local markets, unique product adaptations, or more efficient supply chains. 

These advantages are often difficult for competitors to replicate, especially those operating solely in domestic markets or using a one-size-fits-all global approach.

Risk Diversification

A transnational strategy mitigates risk by spreading business operations and investments across multiple countries. 

If one market experiences an economic downturn, political instability, or other adverse conditions, the impact on the overall business can be offset by the performance of units in other, more stable markets.

This diversification reduces the potential damage of localized risks and enhances the company’s resilience.

Access to New Markets

Transnational strategy allows businesses to enter and compete in new markets. This can increase market share, revenue growth, and brand recognition. 

Additionally, it enables companies to cater to a larger and more diverse customer base, which can drive innovation and adaptation in products or services to meet varying customer needs and preferences.

Learning and Innovation

Operating in multiple countries exposes companies to different market conditions, consumer behaviors, and business practices. 

This exposure can foster cross-border learning and innovation. 

Companies can gather insights and best practices from each market and apply them across their global operations, leading to continuous improvement, innovation, and a stronger competitive position. 

This strategy can also encourage sharing of knowledge and skills among employees from different backgrounds and cultures, further enhancing the company’s learning culture.

What Are The Challenges of a Transnational Strategy?

With all the potential benefits, it’s essential to consider the challenges of a transnational strategy.

Some of these include:

Cultural Differences

One of the significant challenges of a transnational strategy is dealing with cultural differences.

When a company expands its operations across various countries, it encounters diverse cultures, languages, and business practices. 

This diversity can lead to misunderstandings, miscommunication, and potential conflicts. For instance, a marketing campaign that works well in one country may not resonate in another due to cultural nuances. 

Therefore, businesses must invest time and resources into understanding and respecting these cultural differences to build strong relationships with local customers, employees, and partners.

Regulatory Complexity

Different countries have their unique sets of laws and regulations that govern business operations. 

These can range from labor laws and tax regulations to environmental policies and data protection rules. 

Navigating this regulatory complexity can be a daunting task for businesses employing a transnational strategy. 

Non-compliance can result in hefty fines, legal issues, and damage to the company’s reputation. 

Therefore, businesses must ensure they have a deep understanding of the local legal landscape and stay updated on any changes to avoid potential pitfalls.

Coordination and Control

Implementing a transnational strategy involves managing operations across various regions, which requires a high level of coordination and control. 

Ensuring consistency in product quality, customer service, and brand image across all locations can be challenging. 

There can be logistical hurdles in coordinating supply chains, managing international teams, and synchronizing processes. 

Furthermore, controlling operations across borders can become difficult due to time zone differences, communication barriers, and varied operational standards. 

Businesses need robust systems and processes in place to effectively manage these challenges and maintain operational efficiency.

What Are Some Examples of a Transnational Strategy?

We will delve deeper into the world of transnational business strategies as we explore how five global powerhouses, Unilever, Procter & Gamble, Coca-Cola, and Starbucks, have successfully merged global aspirations with local relevance.

Unilever

Unilever, a renowned multinational consumer goods company, is widely recognized as an exemplary transnational corporation. With its extensive presence in numerous countries worldwide, Unilever has honed its expertise in catering to diverse regional tastes and preferences.

Through a portfolio of carefully tailored brands, Unilever has successfully captured the essence of local markets while upholding global standards. This harmonious blend of adaptability and consistency showcases Unilever’s remarkable implementation of a transnational strategy, setting it apart as a true industry leader.

Procter & Gamble (P&G)

As one of the world’s largest consumer goods companies, P&G has established a strong presence across international borders with its diverse range of products. 

From groundbreaking health care and personal care solutions to reliable household cleaning products, P&G has meticulously tailored its offerings to cater to the unique needs and preferences of customers worldwide.

P&G’s continuous adaptation of its products and marketing strategies has led to remarkable success, serving as a testament to the prowess of its transnational strategy.

Through a careful blend of innovation, adaptability, and market insight, P&G has solidified its position as a global leader in the consumer goods industry.

Coca-Cola

Operating in more than 200 countries, Coca-Cola has perfected the art of balancing global and local strategies by leveraging its deep understanding of diverse cultures.

While the brand maintains a strong global identity, it also adapts its products to cater to local tastes, preferences, and traditions.

Embracing this nuanced approach allows Coca-Cola to maintain its relevancy and a deep connection with the unique needs and desires of consumers in each market.

This commitment to understanding and embracing local nuances has made Coca-Cola a prime example of a highly successful transnational strategy, solidifying its position as a global leader in the beverage industry.

Starbucks

Starbucks‘ successful international expansion exemplifies a well-executed transnational strategy. 

By maintaining a consistent global brand identity, Starbucks has created a sense of familiarity and trust among its customers worldwide. However, what sets Starbucks apart is its ability to adapt and cater to local preferences.

For example, in Japan, where matcha is a popular flavor, Starbucks offers matcha-flavored drinks that cater to the local taste buds. Similarly, in China, where red bean is a traditional ingredient, Starbucks has introduced red bean frappuccinos to capture the local market.

This strategic blend of global and local approaches has allowed Starbucks to establish itself as a beloved global brand and foster a deep connection with customers by offering a taste of familiarity while celebrating local flavors. 

This attention to detail and commitment to delivering a personalized experience has been key to Starbucks’ continued success in the competitive coffee industry.

What’s Next For Transnational Businesses?

Transnational businesses are poised to shape the next wave of globalization.

Their focus is on leveraging entrepreneurial growth to expand their global influence and increase their share in worldwide GDP, exports, and employment once the current crisis dissipates.

To achieve this, transnational companies are:

• Adopting lean expansion strategies, focusing on well-defined product strategies and reinvestment cycles. 
• Collaborating with other businesses across borders is also seen as a key strategy for internationalization.
• Utlizing digital adoption platforms as well as tools that provide data-driven insights into local market preferences, behaviors, and trends. This allows for customized digital experiences created based on local insights while still maintaining a consistent global brand image
• Strategizing to navigate the reshaping of global value chains due to rising demand and new industry capabilities in the developing world.

It’s evident that businesses with a global footprint are gearing up for a dynamic future. 

They’re striking a harmonious balance between their expansive global presence and the essence of local relevance, positioning themselves to tap into unparalleled growth opportunities in the ever-evolving global landscape.

The post What Is A Transnational Strategy & Why Do You Need One? appeared first on Digital Adoption.

Start by conducting a thorough assessment of your current software license usage. Next, enter negotiations with your software vendors armed with confidence and data. 

Embedded within these overarching actions are a multitude of smaller, detailed tasks that, if executed effectively, can lead to substantial cost savings in the forthcoming year.

Many companies overspend on software licenses. So, reducing costs on software license optimization can save your company serious amounts of money. 

Research from Nexthink reveals some shocking home truths about software license use. A survey of thousands of devices revealed that 50% of licenses are unused, costing up to $44 million.

On the plus side, gross overspending on software licensing costs means you have many opportunities to improve your situation. Cost savings are just one dimension of a software license management project. But reducing costs is the kind of language that executives understand well, so it’s worth your time. 

This article is a guide to reducing software costs in your business. We’ll start by understanding what cutting software licensing costs means. Then, we’ll look at seven ways to do this one by one. 

It takes smarts to use software licenses well, so let’s get stuck in. 

Why you need to reduce software license costs: the background  

Software can be either a capital expense or an operational expense. In today’s SaaS-oriented model, software usually requires month-by-month subscriptions or licensing arrangements. As such, the software is usually an operational expense, with some exceptions for crucial desktop applications. 

The licensing model for software use has many bonuses for businesses. It offers flexibility, low up-front costs, and easy access to updates. However, when companies start to grow their digital ecosystems, digital licensing becomes difficult to manage. 

Even in a simple business, employees may use dozens of small applications. If business units are continually making new decisions about software, it’s difficult for IT managers to keep up.  

IT leaders are not just worrying about costs, even though the financial costs of unused software are huge. Good licensing practices bring in wider issues that you need to think about. 

For example, McKinsey’s research confirms that effective license management can have a positive impact on cybersecurity.

So, reducing software license costs makes a difference to every part of your business.

The best ways to reduce and optimize software license costs

This section will look at seven ways to start working on your software license cost optimization program.

The strategies for reducing software costs use various skills, including data gathering, negotiating tactics, and collaborative conversations about technology. To produce effective results, you need a combination of specialized knowledge and “soft” skills. 

Take a look at the steps below and see what potential savings you can make. 

1. Gather detailed data to renew your licenses

The first step is to gather clear software usage data and use that data to negotiate with your software vendor. This won’t bring immediate cost savings. But if you understand your users perfectly, there’s a good chance of driving down the costs of license renewal.

You can use a Digital Adoption Platform, Software Asset Management tools, or traditional surveys to understand how employees use the software. 

You will get information like:

• User Count
• User Activity
• User Feedback
• User Support Tickets
• User Profiles

Altogether, data-gathering will help you to discover redundant software, unnecessary costs, and unused licenses.

2. Consolidate your existing license contracts

Consolidating your existing software licenses involves streamlining current agreements to eliminate redundancy.

The starting point is clear data, including a comprehensive inventory of your organization’s software licenses and agreements. Identify overlapping or duplicate licenses and determine if any software can be replaced with a single, more cost-effective solution. By consolidating licenses, you can simplify management, reduce administrative overhead, and negotiate more favorable terms with fewer vendors.

This approach not only cuts costs but also enhances license management efficiency, minimizes the risk of non-compliance, and allows for better allocation of resources, ultimately optimizing your software ecosystem for improved productivity and cost savings.

3. Harmonize cross-organization software tool usage

Everyone has a favorite word processor, CRM, or note-taking package. And some businesses like to cater to everyone’s personal preferences. However, there are better ways to achieve a streamlined licensing ecosystem. If products from Apple, Microsoft, and Google are all doing the same thing, why are you paying for all three?

When you’ve got a clear inventory of software usage, you can understand where you have overlapping software functions. Then, you can make an informed choice about which licenses to keep and which licenses to eliminate.

Streamlining your software choices can reduce license costs, improve operational efficiency, and simplify training and support needs. Harmonization has other benefits. It fosters a more cohesive and productive work environment where employees can focus on mastering a smaller set of tools, enhancing collaboration, and optimizing workflows.

4. Stand firm in software licensing negotiations

In contract negotiation, you must leverage your position and clout to secure more favorable terms for your software licensing agreements.

You can use various points of leverage, such as the length of your commitment and the number of users. If you’re a major customer, you can push for contract restructuring, including adjusting payment terms, changing payment schedules, or imposing price caps that protect your budget.

This process can reduce software license costs substantially. After all, your organization will only pay for what it needs and secure advantageous terms. This approach is useful for organizations with substantial purchasing power, as it allows them to assertively negotiate contracts that align with their financial and operational objectives, ultimately achieving significant cost savings.

5. Look at the whole picture

Access to the software is actually just one part of the cost of a license.

A holistic view of software costs includes many other factors – like maintenance, warranty, and support fees.

You may be able to negotiate reductions in some of these costs when you renew subscriptions. And if you have “shelfware” – software that is not in active use – you may be able to suspend support costs for a period.

This process can help you substantially reduce software maintenance expenses, ensuring you only pay for the software that actively contributes to your operations. This approach is invaluable for optimizing your software budget, eliminating unnecessary costs, and maintaining a lean and cost-efficient software ecosystem.

6. Tread carefully with the cloud

Cloud computing opens up many options for companies – access, expansion, and updates are now very easy to navigate. However, if you are not careful, cloud costs can get completely out of control.

Cost opacity, scalability, and complex pricing models can all lead to expensive “accidents.” If all employees can change the settings, someone might crank up the storage usage, or if you’re holding on to legacy projects in the cloud, you might be needlessly throwing thousands of dollars down the drain every month.

Cloud cost optimization is a specialist discipline in its own right. If you can hire an expert professional to support this area of your business, you will certainly reduce your software costs.

7. Implement software asset management tools

We started this list by talking about data gathering, so it’s only right that we’ll end with data gathering. 

Implementing a digital adoption platform (DAP) tool and software asset management (SAM) tools greatly reduces software licensing costs. These tools provide a comprehensive understanding of an organization’s software landscape. These comprehensive tools can reach even isolated pockets of software that often go unnoticed in traditional auditing methods.

DAP tools offer insights into software use, enabling optimization and increased efficiency, while SAM tools help track and manage licenses more effectively. Together, they empower businesses to make data-driven decisions, eliminate unnecessary expenses, and ensure compliance, ultimately leading to significant cost savings and streamlined software operations.

Reduce the burden of software licenses, one step at a time

As you can see from this article, a focus on software licenses will reward your time. It will help you get your software spending under control, mitigate compliance risks, and build collaborative working practices. 

However, software license issues are only one aspect of business efficiency. Ideally, optimizing software licenses should only happen within a strategy for IT cost optimization. And in turn, that’s just one part of overall operational cost reduction.

The post How to Reduce Software License Costs: An Ultimate Guide appeared first on Digital Adoption.

User onboarding is a pivotal step in digital adoption. It guides and educates new users on effectively using a digital product or service, enhancing their overall experience and engagement.

This process is not just about introducing the product to the users. It’s a comprehensive journey that includes their initial experience with the application, actively guiding them to discover new aspects and ultimately assisting them until they become loyal customers.

According to a recent report from Precursive, 82% of enterprise organizations rate their onboarding strategy as a key driver of value, demonstrating the importance of getting it right from the start.

A compelling user onboarding experience is one that not only instructs the user on how to use the product but also aids in driving business value, directing them toward the realization of the product’s potential in fulfilling their needs.

This process commences at the start of the customer journey, capturing users’ loyalty in those initial interactions with the product. These early experiences leave a lasting impression, emphasizing the significance of effective user onboarding.

A well-executed user onboarding strategy can be the difference between a one-time user and a loyal customer, making it a vital component in the realm of customer service and product management.

This article will provide a comprehensive guide to user onboarding, from defining the process and understanding key terms to exploring best practices and tips for executing it effectively. 

We will also examine how software such as Digital Adoption Platforms can enhance this process and provide users with an intuitive and efficient experience.

Let us begin by exploring the concept of user onboarding and its components in detail.

What is User Onboarding?

User onboarding is the systematic process of guiding and educating new users to proficiently use and find value in a product, service, or application.

The onboarding process begins when a user engages with the software and extends well beyond the initial stages. It is a comprehensive experience designed to improve user understanding, encourage active participation, and facilitate long-term customer loyalty.

Onboarding empowers users to make the most of the product or service by effectively guiding them through the key features and functionalities, which results in a satisfying and fulfilling interaction.

A well-crafted user onboarding strategy sets the stage and ensures that users feel supported, informed, and confident in their decision to use the software or service.

Why Is User Onboarding Important?

User onboarding is a crucial component in the user experience landscape that significantly influences a product’s success by shaping initial impressions, accelerating product adoption, and enhancing user retention.

Let’s take a look at the key reasons why user onboarding is so important:

Importance of User Onboarding

First Impressions Matter

User onboarding is the first interaction a user has with a product or service. It’s the equivalent of a first impression in a meeting—vital and lasting. Users may abandon the product if the onboarding process is confusing or overwhelming. A well-designed onboarding experience, on the other hand, can make users feel welcomed and supported, increasing the likelihood they’ll continue using the product.

Facilitates Product Adoption

Onboarding plays a critical role in facilitating product adoption. By guiding users through the product’s key features and how to use them, onboarding helps them understand its value proposition. This understanding can significantly speed up the product adoption process, as users are more likely to continue using a product they find valuable and easy to use.

Improves User Retention

User retention is another critical area where onboarding has a significant impact. Users who understand how to use and find value in a product are less likely to churn. When businesses equip users with essential resources and support throughout the onboarding process, they can see a significant improvement in user retention rates through appropriate software.

What Are Examples of User Onboarding?

The user onboarding process can take various forms, including interactive tutorials, progressive onboarding, personalized experiences, checklists, email drip campaigns, in-app messaging, and video guides.

Let’s explore user onboarding examples in more detial: 

1. Interactive Tutorials: Interactive tutorials are a practical and engaging form of user onboarding. These tutorials actively involve the user in learning how to use the product or service by guiding them through different tasks. For example, a project management tool may provide a tutorial that shows users how to create a new project, add team members, assign tasks, and monitor progress. The hands-on nature of interactive tutorials helps users quickly grasp the product’s functionality, making it an effective onboarding strategy.
2. Progressive Onboarding: Progressive onboarding is a technique that gradually introduces users to more complex features as they become familiar with the basics. This method prevents information overload and allows users to learn at their own pace. For instance, a photo editing app might start by showing users how to crop and filter images. Once they master these basic edits, the app can then introduce more advanced tools like color correction and layering. This type of onboarding ensures users are not overwhelmed and can confidently use the product.
3. Personalized Onboarding: Personalized onboarding tailors the user experience based on individual needs or preferences. It often involves asking users some questions at the beginning to understand their goals or preferences. For example, a music streaming service might ask new users about their favorite genres or artists during sign-up. Based on this information, the service can then provide personalized song recommendations, creating a unique and tailored user experience from the outset.
4. Checklist Onboarding: Checklist onboarding provides users with a list of actions to complete when they first start using a product or service. This method gives users a clear path to follow and helps them understand the product’s key features. For example, a social networking site might provide a checklist for new users to upload a profile picture, add friends, and post a status update. By ticking off items on the checklist, users gain a sense of achievement and become more familiar with the platform.
5. Email Drip Campaigns: Email drip campaigns are automated emails sent to users over a period of time. They provide tips, best practices, and additional resources to help users get the most out of a product or service. For instance, a SaaS company might send an email on the first day explaining how to set up an account, followed by subsequent emails outlining the main features and benefits. This method keeps users engaged and informed even after signing up, enhancing their understanding and usage of the product.
6. In-App Messaging: In-app messaging involves sending messages or notifications directly within the product or platform to guide users. These messages can take various forms, including tooltips, pop-ups, or push notifications. For example, a fitness app might send reminders to log daily workouts or tips for achieving fitness goals. Providing guidance directly within the app reduces friction and helps users make the most of the product.
7. Video Guides: Video guides are instructional videos that show users how to use a product or service. They can be particularly useful for complex products that require detailed explanations. For example, a video editing software company might provide video tutorials showing how to cut, splice, and add effects to videos. By visually demonstrating the process, video guides can make it easier for users to understand and use the product effectively.

How Do You Measure User Onboarding Effectiveness?

Measuring the effectiveness of user onboarding holds significant importance. It enhances the user experience and boosts retention rates, contributing to the long-term success of your product or service.

Key performance indicators, user feedback, and product usage metrics are reliable tools to understand the success of your onboarding process and implement necessary enhancements.

Key Performance Indicators (KPIs)

To measure the effectiveness of user onboarding, consider the following KPIs:

• User Retention Rate: This measures the percentage of users who return to your product after their first visit. A high retention rate indicates a successful onboarding process.
• Time to Value (TTV): This measures how quickly new users reach their first ‘success’ with your product. A shorter TTV suggests effective onboarding.

User Feedback

• Surveys and Interviews: Ask users directly about their onboarding experience. Their responses can provide valuable insights into what’s working and what isn’t.
• Engagement: Measure user engagement with your product. Increasingly longer sessions can suggest successful onboarding.
• Feature Usage: Track how users interact with the features in your product, as this will provide insight into how users are engaging with the product and whether they are finding value from it.

Product Usage Metrics

• Feature Adoption: Monitor which features new users engage with and how often. This can indicate whether users understand the value of your product.
• Activity Levels: Keep track of how frequently and for how long users interact with your product. Increasing activity levels can signal effective onboarding.

Remember, onboarding aims to teach users how to use your product, demonstrate its value, and encourage continued usage.

Steps to Successful Onboarding

In the world of user onboarding, there exists a captivating progression that culminates in a moment of insight. This is when new users distinctly comprehend a product’s inherent worth and practicality, seeing through its marketing narrative’s promises.

This progression isn’t serendipitous but is a meticulously planned sequence of steps. These are designed to escort users from initial engagement with the product to their enlightening realization of its capabilities.

Each step is integral, steering users towards the pivotal instance where they understand the product’s potential and its ability to cater to their needs or resolve their issues:

1. User-friendly Sign-up Process: The first step to successful user onboarding is designing a simple and intuitive sign-up process. This includes minimizing the number of steps in the registration process, providing clear instructions, or offering a social media login option for quicker access.
2. Effective Product Walkthrough: Once users have signed up, guide them through a comprehensive yet concise product tour. Highlight key features and functionalities, ensuring users understand how to navigate your product or service. Use tooltips, video tutorials, or interactive guides for effective demonstration.
3. Ongoing Support and Engagement: Post initial onboarding, keep users engaged with regular updates, personalized tips, and helpful content based on usage patterns. Also, ensure you have a responsive support system to promptly address any queries or issues. Regular check-ins and feedback mechanisms can also improve the user experience.

How Are DAPs Revolutionizing The Onboarding Process?

Navigating new software can be a daunting task. However, Digital Adoption Platforms (DAPs) are changing the game. Offering interactive guides, personalized learning, real-time support, and user insights simplifies the onboarding process, making it an intuitive and enjoyable journey.

Interactive Learning Experience

• Step-by-step guidance: DAPs break down complex processes into manageable steps, making navigation a breeze.
• Hands-on learning: Users gain practical experience, reducing dependence on external help.
• User empowerment: DAPs foster self-confidence and autonomy by guiding users through the system.

Personalization at Its Best

• Tailored learning: AI technology enables DAPs to customize the learning process according to each user’s needs.
• Enhanced engagement: Personalised learning increases user interest and involvement.
• Efficient adoption: Customisation speeds up the time taken to master the software.

Instant Support

• Real-time assistance: DAPs provide immediate help when users encounter difficulties.
• Query resolution: Prompt support ensures minimal disruption to the learning process.
• Consistent momentum: Instant assistance maintains the pace of the onboarding process.

Valuable User Insights

• Behavioral tracking: DAPs monitor user behavior to identify areas of struggle.
• Insight-driven improvements: Analysis of user behavior informs enhancements to the onboarding process.
• Continuous optimization: The cycle of feedback and improvement boosts overall user satisfaction.

In the digital era, DAPs facilitate faster software adoption and reshape the relationship between users and technology. They streamline the learning process, boost productivity, and create a path toward a future where tech-savviness is the norm.

What Is In-App User Onboarding?

In-app user onboarding is critical to digital transformation, ensuring users can effectively engage with new digital tools.

For instance, Slack, a popular team collaboration tool, excels in in-app user onboarding. To familiarize new users with its platform, Slack uses a bot to guide them through the app’s features. The bot explains how to send messages, create channels, and use integrations, providing a hands-on learning experience that encourages digital adoption.

Another example is Microsoft Teams. Microsoft Teams leverages interactive learning and tooltips to facilitate digital transformation in workplaces. These helpful features assist new users by demonstrating how to schedule meetings, share files, and collaborate on documents seamlessly in real-time.

These examples demonstrate that effective in-app user onboarding can accelerate digital adoption, making it easier for users to transition to new digital platforms as part of a company’s digital transformation strategy.

Best Practises For User Onboarding

In the dynamic world of technology, mastering the subtleties of user onboarding is crucial for businesses. It’s not just about getting users on board; it’s about crafting a personalized journey that considers unique user roles, differentiates between new users and accounts, adapts to various learning styles, and treats onboarding as a distinct yet integral part of the user experience.

That being said, here are some essential best practices to keep in mind as you design your user onboarding program:

• Bespoke Onboarding: Personalise onboarding experiences for each unique user segment. Tailor the app’s process to cater to different personas or job roles. This speeds up the journey towards individual ‘a-ha’ moments and provides the necessary context to keep users engaged.
• New Users vs. New Accounts: Distinguish between new users and new accounts. For B2B products, new users joining an existing account can have a streamlined onboarding, focusing on familiarising them with the current account activity rather than starting from scratch.
• Adapt to Learning Styles: Cater to diverse learning methods. Consider modularised onboarding content that allows users to explore topics at their own pace. Use tools like progress bars or completion percentages, especially for complex, sequence-dependent onboarding. Incorporate varied communication modes like video demos and illustrated walkthroughs, and use gamification to stimulate motivation.
• Onboarding as a UX Challenge: Treat onboarding as a distinct yet integral part of the user experience. Approach and evaluate the design using conventional UX practices such as customer journey mapping and user testing. This helps verify assumptions about user interaction with onboarding flows and measure the effectiveness of the onboarding process.

What’s Next For User Onboarding?

As we look towards the future of user onboarding, one thing becomes abundantly clear – it is a process that will continue to evolve and adapt.

Personalization will sit at the heart of this evolution, with businesses investing more in creating bespoke onboarding experiences that resonate with individual user roles. The distinction between new users and accounts will become more pronounced, leading to more streamlined onboarding processes.

Artificial Intelligence (AI) stands at the forefront of the user onboarding revolution, promising a personalized, dynamic experience. This technology is not merely about guiding new users through a platform’s features. It’s about understanding their unique needs and learning styles and adapting in real-time to create a deeper connection.

AI Digital Adoption is being significantly propelled by the instrumental role of AI. With its ability to discern usage patterns, recommend pertinent features, and offer real-time assistance, AI ensures users are fully harnessing the platform’s capabilities.

In a world where customer retention is as important as acquisition, the role of user onboarding as a critical facet of the overall user experience will only grow stronger. 

The post What is User Onboarding? Definition and Examples appeared first on Digital Adoption.

Generative AI creates new, realistic content using raw data from existing NLP machine learning models and algorithms. It generates outputs in response to specific prompts based on the patterns and structures it identifies within the data it’s trained on.

Generative AI models, having been trained on massive data sets through deep learning and deep neural networks, can engage in conversations, respond to inquiries, author stories, generate source code, and conjure images and videos tailored to any description—all initiated by concise text inputs or “prompts.”

Research from McKinsey highlights that generative AI could add between $2.6 trillion and $4.4 trillion in value to the world’s economy by improving productivity. 

So it’s no wonder this term has been creating a stir in the tech world, mainly due to the escalating popularity of generative AI applications such as OpenAI’s conversational assistant ChatGPT and the AI image creator DALL-E.

This article will explore the many facets of generative AI and its many applications.

By the end of this article, you will know:

• Understanding Generative AI and Its Mechanisms
• Benefits of Generative AI for Businesses
• Diverse Applications of Generative AI
• Prime Examples of Generative AI Models
• Limitations of Generative AI
• Risks in Implementing Generative AI
• Generative AI’s Role in Digital Adoption

What Is Generative AI?

Generative AI relates to artificial intelligence systems that forge human-resembling content—images, videos, music, and text—from the ground up.

Predominantly, generative AI finds its application in large language models (LLMs), utilizing deep learning algorithms to scrutinize extensive textual data. 

This enables them to comprehend the structure of human language and to produce unique content ‘inspired’ by the original sources it has analyzed.

Why Is Generative AI Important? 

Generative AI is important due to its transformative potential in various domains. 

Here’s why:

• Content Creation: It can produce original content like articles, music, and images, thereby automating and streamlining the creative process.
• Data Augmentation: Generative AI can create synthetic data for training machine learning models, especially when real data is scarce or sensitive.
• Personalization: It can customize content based on individual preferences, enhancing user experience in areas like eCommerce, marketing, and entertainment.
• Simulation: In industries like automotive and aerospace, generative AI can simulate different scenarios for testing and validation, reducing costs and risks.
• Innovation: By generating new ideas, designs, and strategies, it can drive innovation across sectors.

However, it’s crucial to use generative AI responsibly due to potential risks such as deepfakes and misuse of synthetic data. Despite these challenges, the benefits of generative AI make it an essential tool in the digital age.

How Does Generative AI Work?

Generative AI starts with a prompt, which can take various forms such as text, image, video, design, or musical notes—essentially any input the AI system can interpret. 

After this, different AI algorithms generate new content in reaction to the prompt, which may encompass essays, problem solutions, or convincing forgeries crafted from an individual’s pictures or audio.

Innovators in generative AI are cultivating enhanced user experiences, enabling users to articulate requests in straightforward language. 

After receiving an initial response, users can fine-tune the results, offering human feedback regarding the style, tone, and other aspects they desire to be reflected in the generated content.

What Are The Benefits Of Generative AI?

Although generative AI may not yet be able to replace humans fully, it does enhance the capabilities of your employees, assisting them in automating monotonous, repetitive tasks, managing and validating substantial data volumes, and addressing recurring queries and tickets. 

Here are some advantages you might anticipate upon integrating generative AI into your organization:

• Facilitating the automation of manual content writing processes.
• Diminishing the efforts required to respond to emails.
• Enhancing the addressing of specific technical inquiries.
• Generating lifelike portrayals of individuals.
• Condensing complex data into a straightforward, comprehensible narrative.
• Streamlining the creation of content adhering to a particular style.
• Personalizing the customer experience.

Use Cases For Generative AI

Generative AI can be utilized across numerous scenarios to create almost any type of content. 

Some of the practical applications for generative AI are:

• Employing chatbots for customer service and technical assistance.
• Utilizing deepfakes to emulate individuals or even specific personas.
• Enhancing dubbing in movies and educational materials across diverse languages.
• Composing email replies, dating profiles, resumes, and academic papers.
• Generating realistic images when adhering to a distinct style.
• Elevating product demonstration videos.
• Proposing novel drug compounds for experimentation.
• Crafting designs for physical products and architectural structures.
• Refining new chip designs.
• Composing music is characterized by a particular style or tone.

What Are The Best Examples Of Generative AI Tools?

Some of the renowned generative AI tools include:

• GPT, Jasper, AI-Writer, and Lex to generate text.
• Explore Dall-E 2, Midjourney, and Stable Diffusion for image generation.
• Consider Amper, Dadabots, and MuseNet in the realm of music generation.
• CodeStarter, Codex, GitHub Copilot, and Tabnine are worthwhile to create code.
• Descript, Listnr, and Podcast.ai are available for voice synthesis.
• Companies like Synopsys, Cadence, Google, and Nvidia are prominent in AI chip design.

The Impact of ChatGPT on Generative AI

ChatGPT, developed by OpenAI, has had a profound impact on the field of generative AI.

As a large-scale, language-based model, it has demonstrated remarkable proficiency in generating human-like text. 

Here’s its impact:

• Benchmark for Quality: ChatGPT’s ability to understand context and generate coherent responses has set a high standard for AI-generated text quality.
• Versatility: It’s used in various applications, from drafting emails to writing code, showcasing the versatility of generative AI.
• Accessibility: It has made generative AI more accessible to non-technical users, encouraging widespread adoption.
• Research Advancements: The success of ChatGPT has spurred further research in AI, pushing the boundaries of what generative models can achieve.

Despite concerns over misuse, ChatGPT’s impact underscores the transformative potential of generative AI in communication, productivity, and beyond.

Limitations Of Generative AI

While generative AI holds transformative potential, it grapples with significant limitations, including accuracy issues and the potential propagation of misinformation.

Here are the limitations of generative AI: 

• Generative AI models process vast amounts of internet content to make predictions and generate outputs based on prompts.
• The accuracy of these responses is not guaranteed, despite seeming plausible.
• Responses may reflect biases from the model’s training data, with no clear method to determine this.
• The role of generative AI in spreading misinformation has raised significant concerns due to these factors.
• These models lack the ability to verify the accuracy of their generated content.
• We often have little insight into the source of the information and how algorithms manipulate it to produce content.

Risks Of Generative AI

ChatGPT and similar tools are trained on extensive quantities of publicly accessible data. 

They are not engineered to comply with the General Data Protection Regulation (GDPR) and other copyright laws, hence the critical importance of meticulously monitoring your enterprise’s utilization of these platforms.

Important risks worth scrutinizing in terms of generative AI include:

Transparency Deficit

Generative AI and ChatGPT models exhibit unpredictability; even the entities that developed them might not fully comprehend their functionality. These models need to be trained on fuller, diverse datasets. 

Accuracy Concerns

Generative AI systems can occasionally generate inaccurate or entirely fabricated answers. Evaluating all outputs for accuracy, relevance, and utility is essential before relying on or disseminating information.

Intellectual Property (IP) and Copyright

There are no confirmable data governance and protection guarantees concerning confidential enterprise data. 

Users should assume that any data or queries submitted into ChatGPT and its counterparts might become public information. Enterprises should establish controls to prevent accidental exposure to IP.

Cybersecurity and Fraud

Enterprises should brace for the possibility of malevolent actors using generative AI systems for cyber and fraud attacks, such as deploying deep fakes for social engineering schemes targeting personnel, and ensure that counteractive controls are established. 

Engage with your cyber-insurance provider to ascertain the extent to which your existing policy envelops AI-related breaches.

Additionally, data shared with a generative AI-powered chatbot could be unintentionally shared with third parties, advancing breaches or violations.  

Sustainability

Generative AI consumes substantial electrical power. Opt for vendors that minimize power usage and employ high-quality renewable energy to diminish the impact on your sustainability objectives.

Can Generative AI Help With Digital Adoption?

Large machine learning models are at the core of developing artificial intelligence that aids users in adopting business software. 

A digital adoption platform can enhance the customization of predictions and recommendations for each individual user by training the AI system with user-specific data.

This AI-powered digital adoption platform provides real-time interactive guidance, proactive action tips, intelligent reminders, and automated user support directly within their applications.

Generative AI-powered self-help features can minimize users’ time accessing personalized knowledge base articles, external links, and other documentation. 

Generative AI can also help organizations track, visualize, and analyze applications, software processes, and data pertinent to enterprise-level digital adoption. 

This data is vital for IT and business leaders to understand software license usage, adoption, and engagement metrics for all mission-critical applications. Through data analysis, enterprises can obliterate productivity impediments and foster enhanced efficiency.

What’s Next For Generative AI?

As we navigate the frontier of artificial intelligence, the next phase for generative AI promises a future of enhanced productivity, improved communication, streamlined business processes, and rapid advancements in sectors like automotive design.

These are our data-driven predictions on what’s next for generative AI: 

1. Labor Productivity Boost: Generative AI can automate routine tasks and processes, freeing human workers to focus on more strategic, higher-value activities. This could lead to significant increases in productivity across various sectors, such as manufacturing, healthcare, and retail. The economic benefits could be substantial, contributing to growth and competitiveness.
   
2. Interactive AI: The evolution of generative AI could result in interactive AI systems that can carry out complex tasks set by users. These systems could collaborate with other software and humans, leading to more efficient workflows and problem-solving capabilities. For example, an interactive AI system could help manage a project by coordinating tasks among team members and tracking progress.
   
3. Real-time Translation and Audio Dubbing: Generative AI has the potential to revolutionize communication and entertainment sectors with applications like real-time translation and audio dubbing. Imagine watching a foreign film with real-time dubbed voiceovers in your language, made possible by generative AI.
   
4. Automated Summarization: With the vast amounts of data generated today, businesses need efficient ways to digest and understand it. Generative AI could automatically summarize complex information, making it easier for decision-makers to gain insights and make informed decisions. For instance, a news organization could use generative AI to create concise summaries of long reports or articles.
   
5. Enterprise Automation: Businesses are always looking for ways to improve efficiency and reduce costs. Generative AI could automate more processes, from customer support to supply chain management. By handling routine tasks, organizations could achieve more with fewer resources, potentially opening up new business opportunities as costs decrease.
   
6. Automotive Design: Generative AI is set to disrupt the automotive industry by speeding up the design process. It could transform simple sketches into detailed 3D designs, reducing the time it takes to prototype new vehicle models. This could lead to faster innovation cycles and more creative design possibilities.
   
7. Rapid Development: The field of generative AI is advancing rapidly. Just as we’ve seen significant progress from GPT-2 to ChatGPT within two years, we can expect similar leaps in the capabilities of generative AI systems in the near future. These advancements could unlock new applications and use cases that we can’t even imagine today.

Frequently Asked Questions

Is generative AI the same as machine learning?

Machine learning is a subdivision of AI that instructs a system to formulate predictions by leveraging the data it has been trained on. For instance, DALL-E can generate an image in response to a given prompt by interpreting the underlying meaning of that prompt.

Thus, while generative AI is a machine-learning framework, it’s essential to note that not all machine-learning frameworks qualify as generative AI.

What are generative AI systems?

Generative AI, although a topic of discussion for years and arguably since the development of ELIZA (a chatbot simulating a therapist) at MIT in 1966, has recently witnessed a blossoming of innovations as a result of progress made in terms of AI and machine learning.

ChatGPT, known for its strikingly human-like text generation, is almost certainly on your radar. DALL-E and Stable Diffusion have also garnered attention for their capabilities to generate vivid and lifelike images from textual prompts. 

Moreover, Google has introduced an enhanced version of Bard, utilizing its most advanced Large Language Model, PaLM 2, enabling Bard to respond to user queries.

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