The software market does not have a full offering of AI digital adoption solutions because AI is an emerging technology for digital adoption (as it is in all industries). 

Nonetheless, AI in digital adoption will likely help businesses monitor shadow AI, provide training support for AI applications, and operate with AI-driven engines. 

AI could support some of the most important aims of digital adoption planning. These aims include improving efficiency by automating repetitive processes, improving customer experience, and facilitating better collaboration. 

As these goals are so important for any data-driven business, it makes sense to explore AI as a key part of digital adoption. This article will introduce the topic by: 

• Giving an overview of what AI means for digital adoption;  
• Explaining why AI in digital adoption will be a success story; 
• Introducing the major benefits of AI for digital adoption. 

If you’re wondering whether AI could make a difference to your digital transformation plans, read on to find out how.

What is AI in digital adoption? 

AI digital adoption is using, supervising, and managing Artificial Intelligence (AI) in a Digital Adoption program. AI is especially important for Digital Adoption platforms, which this article will pay special attention to. 

In 2023, AI already has an important place in digital adoption. Currently, “AI” has become almost synonymous with “generative AI” – ChatGPT, Bard, and all the third-party apps they power. However, different forms of AI have been used in digital adoption for many years. For example, the Digital Adoption Platform WalkMe has been integrating AI-based predictive analytics since 2018.

The use of generative AI in digital adoption is still an emerging technology. We don’t even have a standard set of AI-based products – yet. 

However, software companies like WalkMe, Workday, and Whatfix are all starting to introduce introducing AI services. 

We can expect to see generative AI in digital adoption platforms to include: 

1. Detecting, monitoring, and evaluating shadow AI practices in a company. At this stage, many employees use AI in an unauthorized capacity. In other words, IT leadership cannot easily see how AI is being implemented, and the results are highly risky. Digital Adoption platforms will soon be able to monitor these systems closely.  
2. Adopting AI processes effectively (also known as AI adoption) Like any form of digital transformation, technology must be well-implemented through training, workflow support, and contextual help systems. 
3. Using generative AI to improve general digital adoption. AI can enhance automation practices and undertake basic analysis of user activity. The tech stacks of most large companies are huge – producing oodles of data every day. 
4. Generative-AI-based chatbots and search tools to enhance the customer experience of end products. The implications of AI for customer service cover all industries. In digital adoption, a chatbot can direct users to the ideal answers to their questions. 

Today, there are very few off-the-shelf AI tools. But in the coming months and years, they will likely become a “must-have” for digital transformation projects. 

Why AI digital adoption could survive the hype 

AI is still going through a wild journey of hype. We see astonishing claims, high business valuations, and the potential for billions of dollars of global growth. Although we don’t think AI in digital adoption will change the world, it is clearly here to stay.

Of course, some observers promise that AI will unlock the gates of digital transformation. In August 2023, for example, Dr TJ Jiang of AvePoint claimed, “The best way to accelerate digital transformation is to implement low-code and no-code applications and generative AI right away.” However, for a DAP platform, these must be carefully considered.

On the other hand, an increasing number of people are raising questions about AI’s future value. Gary Marcus has pointed out that ChatGPT’s unreliability for basic information is already causing problems in the legal and research communities. “The whole generative AI economy… could see a massive, gut-wrenching correction” in the near future. 

The “AI bubble” could burst sometime soon. However, we believe AI has a clear future value for digital adoption. Even if the armies of prompt engineers and over-valued AI startups have no future – AI can help IT leaders reach digital maturity more effectively.     

Even if the AI industry explodes: 

• IT leaders will need to have a way to track shadow AI use. 
• DAPs still need to use every method available to improve customer experiences. 
• Users will still need help to use AI solutions effectively.  

Regarding digital adoption in AI, we are not looking for a stand-in for “artificial general intelligence.” AI can serve the goals of business users without approaching the risks that many other sectors will encounter.

The benefits of AI digital adoption 

Before we round up, let’s take a moment to think about the potential benefits of digital adoption. 

The fundamental benefits are very similar to other specific business areas like insurance, sales, and education. 

But, AI can play a specific (and crucial) role in a digital adoption program for several reasons. Let’s take a quick look.

• Personalized Learning and Support: AI can provide personalized learning paths and support to users based on their skill levels, preferences, and learning styles. This tailored approach can help users better understand and adapt to new digital tools.
• Real-time Assistance: AI-powered chatbots or virtual assistants can offer real-time assistance to users, instantly addressing their queries and concerns. This reduces frustration and improves the overall user experience during the adoption process.
• Data-Driven Insights: AI can analyze user interactions and behaviors to provide insights into their challenges and progress. This data-driven approach enables program managers to make informed decisions and adjustments to improve the adoption process.
• Predictive Analytics: AI can predict potential roadblocks or challenges users might face during adoption. Program managers can proactively provide solutions or resources to overcome these issues by anticipating them.
• Continuous Monitoring and Feedback: AI can continuously monitor user engagement and performance with digital tools. This allows program managers to identify areas where users might be struggling and offer timely guidance or training to bridge the gaps.
• Automation of Routine Tasks: AI can automate repetitive and time-consuming tasks, freeing human resources to focus on more strategic aspects of the adoption program. This can include user onboarding, tracking progress, and sending reminders.
• Adaptive Training and Content Delivery: AI can adapt training materials and content delivery based on user feedback and performance. This ensures users receive the most relevant and effective resources throughout their learning journey.
• Scalability and Consistency: AI-driven solutions can be scaled up easily to accommodate a large number of users without compromising the quality of support. This ensures consistent and standardized assistance across the program.
• Natural Language Processing (NLP): AI-powered NLP technology can help users interact with digital tools using natural language, making learning more intuitive and user-friendly.
• Feedback Loop Improvement: AI can gather user feedback about their experiences and challenges, enabling continuous improvement of the adoption program.
• Remote Accessibility: In remote work scenarios, AI can provide remote users with immediate access to guidance and support, regardless of their location or time zone.
• Adapting to Change: AI can help organizations adapt to changes in digital tools and technologies by providing agile and dynamic support to users as new tools are introduced.

Incorporating AI into a digital adoption program can enhance the overall experience for users, increase the speed and effectiveness of learning, and ultimately lead to higher rates of successful adoption of new digital tools and technologies.

AI capabilities will support digital adoption

Working with AI is still new. And if anyone claims to be an expert in the field, you should carefully examine their qualifications. Very few people understand how AI can impact business growth.

Nonetheless, it’s gradually becoming clear that AI technology will be a part of the business models of tomorrow. McKinsey’s research shows successful companies invested as much as 10% of their IT budgets in AI in 2019-2020.

As we’ve seen in this article, AI could greatly impact how every digital adoption platform works: for AI adoption, monitoring shadow AI, and supporting enterprises’ training programs.  

The post How to prepare for AI digital adoption appeared first on Digital Adoption.

But could you optimize your business processes using robotic process automation software (RPA) instead? Or a combination of both? 

RPA and AI are similar, but not when you dive deeper into each and align these technologies to your business goals and processes to see which is best for your needs. 

The AI market is likely to reach USD 407 billion by 2027, so now is the best time to invest in AI or RPA to get the competitive edge you need to succeed. 

To help you understand RPA vs. AI, we will explore the following topics:

• Intelligent automation (IA)
• RPA vs AI: What’s the difference?
• Top 3 pros and cons of RPA vs AI
• How to choose RPA or AI for your organization

Intelligent automation (IA)

RPA and AI are both on a sliding scale called the intelligent automation scale, which covers every type of automotive technology designed to automate tasks for humans. 

GRAPH

You can use the intelligent automation graph to help you identify how your business needs align with either RPA or AI and drive your decision to invest in one of these technologies.

RPA vs AI: What’s the difference?

Many similarities between RPA and AI make it challenging to understand the differences as they both have capabilities to improve business workflow automation. However, knowing these differences is crucial to understanding how to use each. Let’s begin by looking at each individually. 

Robotic process automation (RPA) is a cutting-edge software technology that simplifies the creation of software robots, their deployment, and management using rules-based tasks for intelligence process automation.  

These robots mimic human actions when interacting with digital systems and software, bringing efficiency and effectiveness to various processes.

Artificial intelligence (AI) refers to using computers to perform tasks that typically need human intelligence. AI can accomplish feats beyond human capabilities by processing vast amounts of data.

TABLE

Let’s look at those terms in a little more detail to help nail down the difference:

RPA 

Robotic process automation (RPA) is a software tool that emulates human actions repetitively. 

It automates tasks based on predefined rules, although it lacks intelligent capabilities, and organizations can use it within business process automation (BPA) practices to automate tasks.

RPA has significantly impacted large enterprises by accurately conducting structured data entry and processing without manual intervention and avoiding the organizational problems caused by unstructured data storage. 

The ultimate goal is to develop RPA further to streamline and automate mundane and repetitive business processes.

AI 

Artificial Intelligence (AI) refers to the capability of machines to simulate human intelligence, enabling them to think and act in ways that resemble human behavior.

Artificial intelligence (AI) entails strategic thinking akin to a chess game. It’s helpful to view it as a form of cognitive prowess and mental strategizing.

AI is a fusion of NLP (natural language processing) and ML (machine learning). It empowers users to create rule-based engines for automating repetitive tasks more efficiently.

AI functions as decision-making software, facilitating the automation of decision-making processes without requiring human intervention. Using AI in this way empowers organizations to streamline operations, enhance efficiency, and expedite outcomes.

The differences between RPA and AI

At its core, RPA is primarily focused on execution, while AI focuses on cognition and knowledge acquisition. Think of RPA as the heart of your organization that keeps rhythm and AI as the brain that creates thoughts. 

Top 3 pros and cons of RPA vs AI

Consider these pros and cons of RPA versus AI carefully before investing in either technology. 

RPA: Top 3 Pros

The three main pros to using RPA are as follows. 

1. Efficiency

A finely-tuned RPA can autonomously handle any repetitive task that would typically fall within the realm of human responsibility. This ability to automate repetitive tasks ensures efficiency while maintaining the essence of the original task.

2. Freeing up employees

When employees focus on a task suitable for automation through RPA, they waste an opportunity to optimize their time on potentially more complex tasks that RPA cannot do. RPA can free up employees to focus on more complex tasks. 

3. Speed

A PA robot excels at performing repetitive tasks quickly and tirelessly, surpassing human capabilities. If you become hindered by a time-consuming task that hampers productivity, RPA offers an effective solution to address this challenge.

RPA: Top 3 Cons

The three main cons of using RPA include the following points.

1. Long-term sustainability

Implementing RPA can sometimes divert attention from the crucial long-term efforts required for digitalization and improving the efficiency of processes and administrative tasks. 

There is a potential risk of prioritizing quick fixes over establishing the correct foundations.

2. Implementation

While RPA can provide value in individual tasks, it’s essential to acknowledge the time and cost involved in its setup. Cutting corners on RPA implementation is ill-advised, as misalignment can result in errors. 

3. Error magnification

RPA robots cannot identify glaring errors that a human would quickly notice. In cases where your data contains issues, RPA robots will simply proceed without flagging them, potentially amplifying a mistake you could have otherwise detected.

AI: Top 3 Pros 

There are several pros to using AI in your organization, including the following:

1. Automation

AI can automate various tasks that humans presently perform, which is one of its primary advantages. 

Consider data entry, data analysis, and customer service, just to name a few examples. By implementing automation in these processes, businesses can save valuable time and enhance efficiency. 

2. Decision Making

Artificial Intelligence (AI) has the ability to rapidly and accurately analyze massive volumes of data. 

This makes it an invaluable tool for decision-making tasks, as it can uncover intricate patterns and trends that may elude human perception. 

By providing valuable insights, AI empowers businesses to make informed decisions and gain a competitive edge in their respective industries.

3. Efficiency

Artificial intelligence (AI) surpasses human capabilities in speed and accuracy in task completion. This efficiency enhances productivity, reduces costs, and boosts customer satisfaction. 

For instance, the automation of supply chain management through AI streamlines inventory and logistics, saving precious time and resources.

AI: Top 3 Cons

There are three top cons to AI to consider: 

1. Job Displacement

One of the greatest apprehensions surrounding AI is its potential to replace human beings. With the progress of automation, there is a decrease in human job opportunities, leading to economic and social upheaval. 

While AI may generate new employment prospects in specific sectors, it can also lead to job redundancies, particularly in industries heavily reliant on manual labor.

2. Bias

The training AI receives directly impacts its objectivity. If the data used for training contains biases, the resulting AI will also exhibit biases. This situation can lead to discriminatory and unfair treatment of certain groups. 

For instance, if an AI algorithm receives training on biased data discriminating against specific racial or ethnic groups, its recommendations or decisions may perpetuate these biases. 

3. Security

Artificial intelligence is vulnerable to hacking and cyberattacks, posing a significant risk to confidential data. The potential consequences for individuals and businesses are grave. 

For instance, if an AI system is compromised, it could expose private information or allow for substantial financial losses.

How to choose RPA or AI for your organization

On the surface, RPA and AI appear to be very similar, but their use cases are very different. Therefore, it is essential to consider the business goals you want to meet to ensure you choose the right technology before investing. 

1. Identify the problem

Given the wide range of choices available, it’s unsurprising that business leaders find it challenging to select suitable options when deciding between RPA and AI for their companies. 

Although it may be tempting for them to follow the crowd and choose what’s currently popular, it is wise to disregard the latest trends and make informed decisions based on your own circumstances.

Another error to avoid is purchasing technology based on brand familiarity or a recommendation from another company.

Before considering available options in the market, firms must identify the underlying causes of business challenges, emphasizing the expert. 

Failing to do so may result in the misallocation of IT budgets towards technologies that fail to address the issues and introduce new complications.

2. Map your technology stack

The best first step for your tech stack is to audit your digital infrastructure. This evaluation will gauge each tool’s efficiency, identify those no longer suitable for their intended purpose, and help you decide if AI or RPA is the best addition to your existing tech stack.

You will probably encounter numerous manual processes and outdated technology that no longer benefits the business. 

Once you have revealed the actual state of your current situation, you can then initiate a discussion about what is necessary and what you can discard.

The next crucial step after mapping the tech stack is ensuring effective integration, integrity, and data governance concerning RPA or AI.

Your company’s capability to manage the diverse operational data it generates directly impacts the success of its digital transformation strategy.

Companies must prioritize consistent and reliable data flow across all departments while assessing its credibility. 

You can leverage this valuable resource to proactively monitor the health of technology stacks and extract actionable insights that inform decision-making.

3. Purchasing

Companies can explore the tech market and make informed decisions after identifying the underlying issues, organizing their technology infrastructure, and establishing a solid data governance framework.

Many boards may hesitate to implement drastic changes, particularly when adopting new technologies like RPA or AI, especially during economic uncertainty. 

As a result, they might be reluctant to allocate substantial digital technology investments into your transformation.

If that’s the situation, it might be worthwhile to begin on a smaller scale and concentrate on a manageable process that requires improvement. 

While there is nothing inherently wrong with testing the waters in this manner, it’s crucial to think big and strategize for the long term, as emphasized by Lupton.

According to Lupton, your requirements will inevitably change as your firm evolves. Therefore, selecting scalable technology to grow alongside the business is crucial. Ensuring adaptability and scalability is essential for long-term success.

Choose the right partner for your RPA vs AI journey

Digital transformation is an ongoing journey, not just a single project. Having the right partner by your side will enable you to derive the utmost value from the chosen digital technology throughout its entire lifespan until the time comes to explore new options again.

An experienced partner will allow you to identify what you need out of new technology investments and whether to choose RPA, AI, or both, to streamline your transformation efforts for higher digital resilience and business success. 

The post Everything you need to know about RPA vs AI appeared first on Digital Adoption.

Today, we stand at the cusp of a new era – AI as a Service (AIaaS). This novel concept is transforming the landscape of modern businesses, democratizing access to advanced machine-learning models and algorithms.

AIaaS is a cloud-based model that enables companies to leverage AI capabilities without investing in expensive hardware, specialized talent, or time-consuming development processes. It’s like renting an AI powerhouse on demand, which can be customized and scaled according to business needs. 

This paradigm shift is empowering even small enterprises to harness the power of AI, once a privilege of tech giants.

The potential applications are vast and varied, from predictive analytics and customer segmentation to natural language processing and image recognition. With AIaaS, businesses can make data-driven decisions, optimize operational resilience, and create personalized customer experiences like never before.

This unprecedented access to AI is not just revolutionizing business operations; it’s reshaping entire industries. Healthcare, insurance, banking, publishing – no sector is immune to this wave of change.

As we delve into the world of AIaaS, we will explore its transformative potential, its implications, and how businesses can adapt to this promising field. 

What is AI as a Service (AIaaS)?

AI as a Service (AIaaS) refers to the provision of artificial intelligence (AI) outsourcing, enabling individuals and companies to access AI capabilities without substantial investment in digital infrastructure or technical expertise. It is a cloud-based model that offers machine learning algorithms and AI computing through APIs, web portals, or software applications.

Users can harness advanced AI tools for data analysis, predictive modeling, natural language processing (NLP), and more. AIaaS allows rapid adoption and scalability of AI technologies, fostering innovation and efficiency in diverse sectors.

How does AI as a Service work?

AI as a Service (AIaaS) is a rapidly evolving sector in the tech industry. This model allows businesses to leverage artificial intelligence capabilities without needing extensive in-house expertise or high upfront costs.

AIaaS refers to the outsourcing of artificial intelligence functionalities to cloud-based platforms. It’s a business model where companies provide AI tools as services over the internet, similar to Software as a Service (SaaS), Platform as a Service (PaaS), or Infrastructure as a Service (IaaS).

How AIaaS Works

1. Data Collection and Preparation: The first step in AIaaS is gathering and preparing data. This involves cleaning, sorting, and structuring data sets in a format suitable for machine learning training.
2. Model Training and Validation: The cleaned data is then used to train machine learning models. The models are validated and tested to ensure accuracy and reliability.
3. Deployment: Once the models are trained and validated, they are deployed on the cloud. Users can access these AI services via APIs or web interfaces.
4. Continuous Learning and Improvement: AIaaS systems are designed to continuously learn and improve over time. They adapt to new data and feedback, enhancing their performance and capabilities.

Key Components of AIaaS

As we continue our discussion, it’s crucial to understand the fundamental components that form the backbone of AIaaS. These elements work together, creating a system capable of making accurate predictions, learning from experiences, and offering scalable solutions.

Below we explore these key components that will enhance your understanding of how AIaaS functions effectively.

• Data: The foundation of any AI system is data. Data sets to train machine learning models, enabling them to make accurate predictions and decisions.
• Machine Learning Models: These are the algorithms that learn from the data. They are essentially the ‘brain’ of an AI system.
• Cloud Infrastructure: AIaaS leverages cloud computing for storage and processing. This allows for scalability and accessibility, providing businesses with flexible AI solutions.

What are the benefits of using AIaaS platforms?

While the potential applications of AIaaS are vast and varied, there are certain key benefits to using these platforms.

We’ve listed a few of the key advantages below:

1. Cost-Effective Solution: AI as a Service (AIaaS) is a cost-effective solution that bypasses the need for large-scale investments in hardware and software. Traditionally, implementing AI technology requires substantial financial resources to procure the necessary equipment and develop systems from scratch. 

With AIaaS, businesses can leverage advanced AI technology on a subscription basis, reducing upfront costs and transforming them into predictable operational expenses.

2. Scalability: AIaaS can scale AI operations according to fluctuating business demands. This scalability is vital as AI workloads can vary significantly, depending upon the tasks’ complexity and the data volume. 

AIaaS allows businesses to easily scale up or down their AI usage, ensuring optimal resource allocation and cost efficiency.

3. Access to Expertise: AIaaS offers businesses access to an extensive pool of AI specialists. These experts deeply understand AI algorithms, machine learning models, and neural networks. 

Their expertise can provide valuable insights, help troubleshoot issues, and guide businesses in maximizing the benefits derived from AI technologies.

4. Rapid Implementation: AIaaS providers offer pre-trained models and ready-to-use AI solutions that can be swiftly integrated into existing business processes. 

This rapid deployment capability reduces time to market, enabling businesses to leverage AI’s benefits in a shorter timeframe.

5. Continuous Updates: AI is a rapidly evolving field, constantly emerging with new advancements and improvements. AIaaS ensures that businesses are always at the forefront of these developments. 

Providers regularly update their AI models and algorithms, ensuring clients can access the latest and most effective AI solutions.

6. Enhanced Security: Security is critical when dealing with sensitive data and AI. Many AIaaS providers incorporate robust security measures into their platforms, including end-to-end encryption, secure data storage, and strict access controls. These features help protect against data breaches and ensure compliance with data protection regulations.
7. Democratization of AI: AIaaS democratizes access to AI technology, making it available to companies of all sizes, not just large corporations with substantial resources. 

This democratization fosters innovation by enabling smaller firms to experiment with AI, develop novel applications, and compete on a more level playing field.

8. Focus on Core Business: By outsourcing AI-related tasks to AIaaS providers, businesses can free up internal resources to focus on their core competencies. Rather than diverting time and effort towards managing complex AI operations, they can concentrate on improving their products, services, and overall customer experience. This strategic focus can enhance competitiveness and drive business growth.

What are the challenges of using AIaaS platforms?

If businesses want to reap the full benefits of AIaaS, they must first address a few potential challenges. If these problems are not properly managed, they can limit the effectiveness of AIaaS platforms and cost organizations precious resources.

With that in mind, we’ve listed a few of the primary challenges associated with AIaaS below:

1. High Costs: While AI as a Service (AIaaS) can be cost-effective in the long run, the initial investment can be substantial. 

This financial hurdle includes the subscription fee and the costs associated with manpower for deployment and maintenance, hardware upgrades for accommodating AI workloads, and data storage and security measures expenses. Potential hidden costs, such as those for ongoing technical support and fine-tuning the AI model, can add to the overall financial burden.

2. Insufficient or Low-Quality Data: AI systems thrive on high-quality, relevant data for training. The effectiveness of AIaaS solutions is directly proportional to the quality and quantity of available data. 

If data is scarce, poorly structured, or irrelevant, the system’s learning ability will be compromised, leading to inaccurate predictions or flawed decision-making. Therefore, businesses must invest considerable resources in data collection, cleaning, and structuring to ensure the efficiency of their AIaaS solutions.

3. Reduction in Transparency: AIaaS often operates as a black box, where the inner workings of the AI models are not visible to the end-users. This lack of transparency can lead to trust issues, as organizations may find it difficult to understand how the AI made a particular decision. 

Furthermore, the opaque nature of these systems can make troubleshooting and fine-tuning more challenging, requiring reliance on the service provider for problem resolution.

4. Data Governance: With AIaaS, managing large volumes of data becomes a significant concern. 

Data governance involves ensuring compliance with data protection regulations, maintaining data confidentiality, integrity, and availability, and ensuring ethical use of data. Inadequate data governance can lead to legal penalties and damage the organization’s reputation.

5. Long-Term Costs: The long-term costs of AIaaS can accumulate over time. While the initial outlay may seem manageable, ongoing expenses can add up. 

These include recurring subscription fees, costs associated with scaling up the service to meet growing business needs, additional data storage and enhanced security measures, and potential costs for integrating new AI functionalities or upgrading existing ones.

6. Integration Challenges: Integrating AIaaS solutions into existing business systems and workflows can be complex. There may be compatibility issues between the AIaaS platform and the existing IT infrastructure. 

Overcoming these integration challenges may require technical expertise, custom software development, and significant time, adding to the overall cost of AIaaS adoption.

7. Regulatory Challenges: AI technology is advancing rapidly, outstripping the development of regulatory frameworks. Compliance with existing and emerging regulations related to AI use, data privacy, and cybersecurity is a major challenge. 

Navigating this complex regulatory landscape requires a thorough understanding of the laws and standards applicable in different jurisdictions and sectors.

8. Dependency on Service Providers: Adopting AIaaS creates a dependency on the service provider. This dependency can pose a risk if the provider ceases operations, changes its terms of service, or significantly increases prices. 

Organizations must have contingency plans to mitigate these risks, including maintaining in-house AI capabilities or having agreements with multiple AIaaS providers.

What are the various types of AI as a Service?

Depending on their specific needs, different types of AI as a Service (AIaaS) are available to organizations. Some of the most popular AIaaS solutions are outlined below:

Machine Learning as a Service (MLaaS)

• Definition: MLaaS offers machine learning tools as part of cloud computing services. These tools include data visualization, APIs, face recognition, natural language processing, predictive analytics, and more.
• Benefits: It allows businesses to utilize machine learning algorithms without requiring extensive data science knowledge.
• Examples: Microsoft Azure’s Machine Learning Studio, Google Cloud Machine Learning Engine, and IBM Watson Machine Learning.

AI APIs and SDKs

• Definition: AI APIs and SDKs are protocols and tools for building AI-powered software applications.
• Benefits: They provide developers with pre-trained models and the capability to customize them for specific tasks, thus saving development time and resources.
• Examples: Google Cloud Vision API, Microsoft Azure Cognitive Services, and IBM Watson APIs.

Data Labeling as a Service

• Definition: This service annotates, categorizes, or tags data to train and improve machine learning models.
• Benefits: It ensures high-quality, accurate data for training AI and ML models, leading to better performance and accuracy.
• Examples: Amazon SageMaker Ground Truth, Google Cloud AI data labeling service, and Microsoft Azure Data Labeling.

AI Chatbots as a Service

• Definition: AI chatbots as a service provide conversational interfaces that can understand and respond to human inputs, typically used in customer service.
• Benefits: They can handle multiple customer queries simultaneously, provide 24/7 support, and deliver consistent customer experiences.
• Examples: IBM Watson Assistant, Google Dialogflow, and Microsoft Azure Bot Service.

AI as a Service vendors

Vendors play an important role in making AIaaS solutions available to organizations. It is important to research the features of each vendor before selecting a solution that best meets your needs.

We’ve outlined the key features of each vendor’s AIaaS offerings below:

1. Amazon Web Services (AWS)

Amazon Web Services (AWS) is a leader in providing cloud services and has a wide array of AIaaS solutions. The platform offers a plethora of AI services, including machine learning, chatbots, forecasting, recommendation systems, and more. AWS’s AI services are designed to be user-friendly and accessible, with pre-trained AI services that require no machine learning expertise.

Developers can use these services to build, train, and deploy machine learning models quickly and at scale.

2. Microsoft Azure

Microsoft Azure is another frontrunner in the AIaaS market. Its AI services empower developers by providing pre-built models and services to enhance applications with natural language processing, speech, vision, and decision-making capabilities.

Azure also offers a Machine Learning platform for building, training, and deploying models using the tools and frameworks of your choice. Azure’s AI services are designed to help businesses solve complex challenges and improve customer experiences.

3. Google Cloud

Google Cloud provides robust AIaaS solutions designed to help businesses leverage the power of AI and machine learning.

The platform offers pre-trained vision, speech, translation, and more models. Google Cloud’s AutoML allows businesses to create custom models with minimal machine learning expertise required. Google Cloud’s AI Platform is a unified tool for machine learning professionals to build, run, and manage models at scale.

4. IBM Watson

IBM Watson is renowned for its cognitive computing capabilities. Watson’s AIaaS offerings include Watson Assistant for building conversational interfaces, Watson Discovery for uncovering patterns and insights from unstructured data, and Watson Machine Learning for building, training, and deploying models at scale.

Watson’s AI services are designed to help businesses automate processes, improve decision-making, and create engaging customer experiences.

What does the future hold for AI as a Service?

The landscape of Artificial Intelligence as a Service (AIaaS) is projected to undergo a significant metamorphosis in the coming years.

Skyquest’s latest research shows that the global AIaaS market is expected to reach a staggering USD 187.98 billion by 2030, registering a Compound Annual Growth Rate (CAGR) of 48.2% between 2023 and 2023. This growth trajectory is primarily driven by the extensive adoption of AIaaS across diverse sectors such as healthcare, retail, transportation, and security.

In the realm of customer service, AIaaS is anticipated to facilitate more efficient and gratifying interactions. AI-powered solutions can expedite problem resolution, thereby enhancing user experience. Furthermore, the shift from customer service to customer engagement via AI capabilities can result in cost reduction and revenue augmentation.

Moreover, AIaaS is set to revolutionize various other sectors. In healthcare, it could enable predictive analytics for improved patient care. In finance, automated risk assessment and fraud detection would be possible. In manufacturing, AIaaS could optimize production processes, and in transportation, it could enable autonomous vehicles.

The future of AIaaS appears to be remarkably promising. It signifies a transformative phase in the digital era, poised to redefine operational efficiency across industries. While it’s important to remember these trends are subject to change, current projections based on market trends still forecast substantial growth for AIaaS.

The post How AI As A Service Is Reimagining Modern Business appeared first on Digital Adoption.

It promises to unlock the true potential of artificial intelligence by allowing businesses to craft entirely new, unique digital experiences with unprecedented speed and accuracy. 

By using generative AI, your sales teams can take advantage of automated insights and predictions that free them up to explore deeper, more meaningful customer relationships. This technology could have a huge impact on how we all handle sales operations.

It might sound like a thing of the future, but the reality is that generative AI is here. Major players in the sales industry are already developing their own generative AI solutions.

Microsoft now offers Viva Sales, while Salesforce is working on EinsteinGPT.

These solutions are in their infancy, but they’re here to stay. And jumping on the bandwagon right now presents a chance for you to eke out a competitive edge.

But if you’re considering this digital adoption, you probably want to know exactly what’s in it for you.

In this article, we’ll take a closer look at generative AI and how it could revolutionize the way you conduct your sales operations, and what challenges early adopters might expect to encounter.

What is Generative AI and how does it work?

Generative AI is a type of artificial intelligence that uses machine learning algorithms to generate new, unique digital content. 

Unlike traditional AI, which relies on pre-existing datasets for analysis and decision-making, generative AI creates entirely new data points from scratch. 

This gives it the ability to create personalized experiences tailored to individual customers and their preferences. 

The generative AI algorithms are incredibly powerful, as they can analyze huge amounts of data quickly and accurately. 

In addition to generating unique content, these algorithms can identify patterns in customer behavior that could be highly valuable for sales teams.

How Generative AI could be a game-changer for Sales

Generative AI has the potential to revolutionize the way sales teams operate. 

Here’s a look at some of the most exciting and impactful ways Generative AI could be used for Sales:

Automating administrative tasks

Every sales operations team should have an automation strategy.

Generative AI can automate mundane, time-consuming tasks such as data entry and customer segmentation. 

This frees up your team’s valuable time to focus on more creative, human-centered activities.

This is far and away the most common use case for generative AI in Sales. Gartner predicts that as much as 30% of outbound marketing messages will be AI-generated by 2025.

“If deployed correctly, AI can automate almost half of administrative sales work,” says Ilona Hansen, Senior Director Analyst at Gartner. “It’s a perfect addition to any B2B sales organization.”

Hyper-personalized customer experience

With generative AI, you can provide a unique and highly personalized customer experience. 

This could include product recommendations tailored to their preferences or customized messaging that speaks directly to their needs.

Generative AI has natural language processing capabilities. This allows the AI to understand the context of customer queries and even detect emotions— potentially alerting a sales rep if the customer seems dissatisfied. 

The modern customer wants hyper-personalized experiences, and leaning into the strengths of generative AI could give you a boost in customer engagement.

Identifying warm leads

Generative AI can quickly identify which prospects are most likely to convert into paying customers, allowing sales teams to focus their efforts on the people who need them the most.

It achieves this through the automatic creation and analysis of customer segments and personas. 

With this, your sales reps could achieve 80% of the sales and 20% of the work.

Powerful analytics for in-depth insight in real-time.

Generative AI algorithms can uncover trends in customer behavior that are otherwise difficult to detect. 

This enables sales teams to make data-driven decisions based on deep insights gathered from massive datasets.

The analytical capabilities of generative AI can be split into two camps: Predictive analytics and prescriptive analytics.

Predictive analytics finds correlations between data points, which could help with sales forecasting.

Prescriptive analytics uses a pre-defined sales methodology to suggest actions to move a prospect onto the next stage of the sales process.

And most importantly, this can all be done in real-time, providing sales reps with valuable insight even as they’re speaking with a prospect.

An AI model with company-specific data

Generative AI can create an AI model based on your business’s historical and current data. 

This allows you to quickly identify areas of opportunity or risk and spot potential long-term trends.

In general, everything a generative AI can do, it can do better with access to company-specific data.

Is Generative AI replacing sales teams?

The short answer is no. 

Generative AI will not replace sales teams but can enhance their productivity and performance. 

With the right strategy and team in place, generative AI could be a powerful tool to help increase conversions, optimize customer experience, and maximize ROI. 

It’s important to remember that generative AI should not be viewed as a replacement for your human team but rather as an extension. When used correctly, it can help sales teams work smarter and faster.

And if you’re still not so sure about that, let’s take a look at some of the common pitfalls and challenges.

Generative AI for Sales: Common pitfalls and challenges

While artificial intelligence sounds futuristic (and it definitely is) it’s important to remember that we live in the real world, and generative AI for Sales has some real-world drawbacks.

Data quality and relevance

The performance of AI models largely depends on the quality and relevance of the data they are trained on. 

Incomplete, outdated, or inaccurate data can lead to incorrect predictions and analyses. 

It’s crucial to have robust data collection, management, and cleansing processes in place.

Ethics and privacy

Handling customer data in an ethical and legally compliant way is paramount. 

This includes ensuring that data is collected with consent, stored securely, and used for its intended purpose only. 

Also, transparency about how you’re using AI is important.

Bias in AI models

AI models can be biased. They can also amplify existing biases in the data they are trained on. 

This could lead to unfair or discriminatory practices. 

You should take care to monitor for and mitigate any potential biases in AI models.

Dependency

It can be problematic to rely too much on generative AI. 

While generative AI can significantly aid the sales process, there’s no true replacement for human judgment, intuition, and interpersonal skills. 

You must strike a balance between AI and human involvement.

Implementation and maintenance

Implementing AI solutions requires time, resources, and technical expertise. 

You’ll need to regularly update and maintain AI models to ensure they continue to perform well as business needs and environments change.

If you’re looking to incorporate company-specific data, you’re going to need an even more specialist set of skills.

Interpretability

AI models, particularly deep learning models, can be “black boxes,” making it difficult to understand how they arrived at a particular decision or prediction. 

This lack of transparency can make errors hard to identify and correct, and can lead to your sales team not trusting their generative AI tools.

Integration with existing systems

Integrating AI solutions with existing sales software or CRM systems can be challenging. 

Not all systems are compatible; some may require significant customization to work together effectively.

Customer perception

Some customers may not respond positively to interactions that they know are driven by AI. 

It’s essential that you’re sensitive to customer preferences in Sales, and ensure that AI enhances rather than detracts from the customer experience.

Generative AI for sales: Is it too late to be an early adopter?

Generative AI is already being used by Sales teams to automate lead-generation tasks, create data-driven insights, and analyze customer behavior. 

Eventually, it could even be used to craft entirely new products and services that meet customers’ needs more effectively and quickly than ever before.

In 2022 Gartner revealed via their Hype Cycle for Emerging Technologies that generative AI was in the Innovation Trigger phase and would take 5 to 10 years to reach the peak of inflated expectations.

Put simply; we think Gartner missed the mark. 

Generative AI has exploded in popularity in this last year alone. You don’t have another five years to jump on the bandwagon. It is here, and your competitors know about it.

If you want to take advantage of everything Generative AI offers, now is the time to act.

The post Unlocking the true power of generative AI for sales appeared first on Digital Adoption.

AI has become a vital element of modern business, unlocking unprecedented opportunities for efficiency, productivity, and customer satisfaction. In contrast, Adaptive AI empowers machines to continuously learn and improve, like a human brain.

Digital transformation (DX) and digital adoption in business are two components becoming increasingly necessary for survival. As such, AI technology enables companies to optimize production processes and create more efficient operations with improved results.

Adaptive AI, however, is a new potent form of AI that leverages advanced algorithms and data-driven decision-making to respond to changing environments and learn independently. 

It enables organizations to capture valuable insights from ever-evolving streams of customer data and emerging trends in their industry domains. It is essential for organizations today to understand the potential of AI and determine how best to capitalize on its value quickly. 

In this article, we’ll discuss the key features of adaptive AI and its potential to revolutionize the world of intelligence and explore how it’s superior to traditional AI in learning, processing power, and decision-making. 

We’ll also look at examples of how adaptive AI is being used today and what the future may hold for this groundbreaking technology.

What is Adaptive AI? 

Artificial intelligence (AI) is a field of computer science that aims to create systems that can learn and respond intelligently to their environment. 

Adaptive AI creates systems that get smarter over time based on new information. It uses artificial neural networks (ANN), which comprise layers of interconnected neurons. These neurons are trained using large data sets. This means that the ANN can figure things out for itself without needing help from a programmer or operator.

Gartner recognized Adaptive AI as one of the top technology trends of 2023. The research firm predicts that businesses leveraging this technology by 2026 will have an advantage over their competitors of up to 25%.

Adaptive AI vs. traditional AI

There are two kinds of AI. Traditional AI is when you use rules and data to figure things out. Adaptive AI is when you use a computer to learn from lots and lots of data.

Traditional and adaptive AI have advantages and drawbacks depending on the task. Conventional AI is more reliable and predictable but needs more flexibility when it comes to adapting quickly to changing data inputs. At the same time, adaptive AI provides more dynamic responses but may only sometimes be accurate due to its reliance on historic patterns in data sets. 

Both forms of AI have great potential when applied correctly. They can provide robust solutions for many business needs depending on context – deciding which to adopt between the two usually depends upon available resources, time constraints, desired outcomes, etc.

The value of Adaptive AI

Adaptive AI allows organizations to quickly adjust and fine-tune their operating environment according to the latest data – making its potential value clear in a world where data-driven insights are becoming increasingly important.

Adaptive AI offers valuable opportunities to optimize processes and create new business models that drive breakthroughs in performance. Companies can use advanced and traditional machine learning (ML) algorithms to fine-tune their operations to meet specific goals. They can use real-time data-informed predictions to drive enhanced business decisions. 

Moreover, businesses can reduce costs by intelligently automating critical operations and augmenting existing staff with intelligent systems while unlocking greater predictive accuracy across all areas of their operations.

In the longer term, adaptive AI will open up enormous possibilities for businesses to develop more sophisticated strategies and unlock further efficiencies. 

Why should Adaptive AI matter to your business? 

Adaptive AI is a powerful tool for enabling successful business operations today. The ability to quickly identify patterns from data and make predictions based on previous outcomes is invaluable for businesses of all sizes. 

For example, adaptive AI can identify customer trends or segment target markets based on social media data, allowing businesses to understand their customers better and tailor their marketing strategies accordingly. Additionally, advanced predictive analytics tools powered by artificial intelligence can be invaluable in forecasting future outcomes and helping with long-term business planning.

As technology advances and businesses seek to drive productivity gains through automation and intelligent workflows, adaptive AI will become an indispensable tool for maintaining business longevity.

The three tenets of Adaptive AI

In 2023, organizations will be under immense pressure to change and adapt to survive. To do so, they must adopt an approach to AI characterized by three fundamental tenets: 

1. Robustness
2. Efficiency
3. Agility

Let’s explore these three tenets more closely.

Robustness

Robustness refers to the ability of an AI system to withstand changes in its environment and continue functioning effectively. 

This is crucial for organizations that want to implement AI-powered solutions, as it ensures that the system can adapt as needed and continue providing value. A robust AI system should function correctly even when environmental changes occur. This is important for organizations because the system can continue working even as conditions around it change.

Efficiency 

When organizations implement AI-powered solutions, they often look for an edge over their competitors, and an improvement in efficiency can provide just that. With AI-powered systems, it is possible to automate tasks and processes, thus freeing up valuable resources for more important activities.

AI-driven automation also increases production speeds by removing mundane data entry tasks, reducing overall cycle times. Businesses can create leaner operations with improved throughput by streamlining these processes and reducing manual labor. 

Because AI systems use vast data sets to make decisions, they are often more accurate than manual methods. This improves the accuracy of decision-making while also saving time and money.

Agility

AI-driven solutions are essential for achieving agility because they can process large amounts of data and adjust algorithms accordingly.

For example, if a customer begins buying more luxury items instead of necessities, an AI-powered system could identify this shift and recommend related products and services. This dynamic response to changing customer needs is essential for staying ahead in an increasingly crowded market. 

AI systems can also adjust their parameters without manual involvement or disruption, which gives companies greater flexibility when responding to sudden changes. 

How Adaptive AI is accelerating business agility

Adaptive AI is ushering in a new era of business operations, increasing the agility and responsiveness of businesses in a way that has never been seen before. By uncovering patterns in data, Adaptive AI provides invaluable insights to organizations that allow them to leap on opportunities with immediacy, capitalize on emerging trends without hesitation, and fine-tune their strategies or services at an unparalleled speed. 

Adaptive AI presents modern businesses with a toolkit to unlock opportunities that would typically be impossible to capture, helping them stay outmaneuver the competition and chart a successful course into a bright future. 

Adaptive AI can also give modern businesses a greater sense of accountability, as the technology quickly flags up anomalies or errors that may have previously gone unnoticed. Adaptive AI allows companies to reduce risk, stay within compliance, and deliver more reliable results by keeping watch over operations and processes. 

With the capacity to handle large volumes of data in seconds, this tool is invaluable for any organization seeking to maximize its performance and ensure the highest quality standards.

How is Adaptive AI optimizing data? 

Adaptive AI has been revolutionizing how data is optimized. It is a process that not only monitors and records new changes to input and output values along with their related qualities but also considers the events that may affect market behavior in real-time, allowing various dynamic and sophisticated solutions. 

Adaptive AI is optimizing data in these four scopes:

1. Multi-industry use 
2. Scalability
3. Data-driven forecasts 
4. Increased data security management 

Multi-industry use

The multi-industry applications of this technology are particularly impressive, with Adaptive AI being used for healthcare diagnostics and predictive analysis tasks such as cancer detection, retail marketing automation and personalization, inventory management for manufacturing companies, and even autonomous driving cars.

Scalability

The scalability of Adaptive AI is also a significant advantage for businesses that need to keep up with constantly changing customer preferences or market trends. With its ability to quickly identify patterns and learn from them over time, adaptive AI models ensure that companies remain competitive by rapidly adapting their strategies according to the latest insights.

Data-driven forecasts

Data-driven forecasts are another benefit of adopting an adaptive approach to data analysis — with advanced ML techniques creating highly sophisticated predictive models, which can then be leveraged for forecasting purposes. This could range from predicting customer behavior in the short-term, or assessing macroeconomic factors in the long run.

Increased data security management

As hackers become more sophisticated in stealing sensitive information from businesses and individuals alike, Adaptive AI can provide a valuable tool in combating such threats through automated intrusion detection systems (IDS). With adaptive IDS solutions capable of dynamically adjusting parameters based on incoming data streams, these technologies can quickly flag suspicious activity without relying on manual labor or antiquated rule-based detection approaches.

Adaptive innovations & real-world applications

Adaptive AI is an incredible innovation that has the potential to revolutionize many industries and is already being used in a wide range of sectors. 

1. Healthcare 
2. Retail 
3. Car Manufacturing 

Healthcare 

Healthcare is one of the primary areas benefiting from adaptive AI technology. From improved diagnosis accuracy to faster patient care, advanced machine learning algorithms are helping medical professionals get better results with less effort than ever before. 

In particular, deep learning techniques have been used for predictive analysis tasks like cancer detection and automatic image recognition applications such as CT scans. Additionally, adaptive AI can assist hospitals in optimizing scheduling and tracking equipment utilization rates more efficiently.

Retail

Retailers are also taking advantage of adaptive AI to gain the competitive upper hand. For example, e-commerce platforms can utilize AI models to personalize product recommendations based on customer preferences and past purchase patterns. This form of marketing automation allows retailers to target customers with highly relevant campaigns that are more likely to convert into sales. 

Furthermore, through leveraging data-driven insights about customers’ purchase decisions at scale, retail businesses can now create highly effective strategies for inventory management and pricing optimization without having to spend countless hours manually processing raw transactional data points.

Car Manufacturing 

Another vital sector where adaptive AI shines is car manufacturing. Automated robots controlled by machine learning agents have enabled car manufacturers worldwide to significantly reduce production costs while increasing reliability and precision in the assembly line process — something that was impossible using human labor alone. 

Similarly, automakers are using advanced computer vision algorithms for autonomous driving technology, which could soon lead us into a future where vehicles drive themselves safely with minimal risk.

In short, the possibilities offered by Adaptive AI technology are practically unlimited; however, modern businesses benefit greatly from its groundbreaking capabilities across many different industries and sectors.

Leveraging Adaptive AI for the future

Adaptive AI presents an exciting future of innovation and real-world advancements. Autonomous vehicles, efficient medical diagnoses and treatments, improved customer experiences through retail automation tools, and robust cybersecurity measures are some ways this technology can enhance our lives. 

Along with numerous advantages and opportunities, Adaptive AI also comes with its own challenges and risks. To make the most of this technology, it’s essential to ensure that all regulatory requirements are met and that potential negative outcomes are avoided or minimized. This means proper implementation and usage of adaptive AI requires careful planning and strategizing. It’s also important to consider privacy concerns, as the data collected by the system could pose a risk if not properly secured. 

However, despite the challenges that come with this technology, the overall potential benefits far outweigh any downsides when done right. The key is to ensure that we use this powerful tool responsibly for our collective benefit – as only then can we unlock its true potential for innovating our world.

The post Harnessing The Power of Continuous Learning Through Adaptive AI appeared first on Digital Adoption.