Artificial Intelligence: From Fascination to Execution & Value Realization

It is undeniable that AI is one of the most disruptive and transformative technologies of our time, with the potential to impact every sector and industry in the world. However, despite the growing awareness and fascination with AI, many organizations are still struggling to adopt AI effectively and realize its value. What are the reasons for this gap between AI hype and AI execution? How can organizations overcome the challenges and seize the opportunities of AI adoption? According to Deloitte, there has been some speculation that the shortfall in AI realization is due to an inability to translate theory into practice.

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Two years ago, I shared my thoughts on the rise of AI and its position in the S-Curve. I argued that AI was in the growth phase, where the early majority began to use it for various purposes and realized its value and potential. They also learned what AI is and how it can be applied.

However, earlier this year, we witnessed a dramatic shift in AI that made it skyrocket exponentially. This was due to the launch of ChatGPT and other generative AI models that simplified the use of AI for both companies and individuals. They made AI accessible to anyone with an internet-connected device, democratizing AI for everyone. This increased the awareness and the fear of AI, so people from non-technical backgrounds started to study and comprehend AI. They also used it in different ways to achieve their goals, such as writing emails, solving problems, reviewing, and creating various documents and research papers that enhanced our productivity in an unparalleled way.

I think that the late majority, especially in the enterprise sector, are still in the fascination phase. They are getting to know AI, its value, and some basic use cases that can enhance their daily operations and productivity. But they are not yet pursuing the execution and value realization to start investing in this radical change. According to Accenture, 63% of companies are AI Experimenters.

This is the most exciting topic and trend, but I also notice that the majority are in the peak of illusion that AI and generative AI are the solution for everything. This is what Gartner described in its hype cycle for AI 2023, that generative AI is at the peak of inflated expectations.

In this article, I will explore these questions and provide some insights and recommendations for organizations that want to accelerate their AI adoption journey. I will use the Organization Skill/Capability vs Will/Motivation matrix as a framework to categorize the organizations based on their skill/capability and will/motivation to adopt AI.

This matrix can help identify the state of AI adoption in organizations and the potential challenges and opportunities for each type of organization.

The matrix consists of four quadrants, each representing a different type of organization:

• Laggard (Low Will/Low Skill): Organizations that have a low motivation and a low skill to adopt AI. They are either unaware of the benefits of AI, or resistant to change their status quo.
• Hesitant (Low Will/High Skill): Organizations that have a high skill but a low motivation to adopt AI. They are either concerned about the ethical, legal, or social implications of AI, or reluctant to share their data and expertise with others.
• Aspirationals (High Will/Low Skill): Organizations that have a high motivation to adopt AI but lack the necessary skills or capabilities to do so effectively. They are either eager to learn from others, or willing to outsource or partner with experts.
• Exemplars (High Will/High Skill): Organizations that have a high motivation and a high skill to adopt AI. They are either visionary leaders, or agile innovators, who leverage AI to create value and competitive advantage.

I will discuss each type in detail and provide some examples of challenges and strategies for each quadrant. I will also share best practices and suggestions for taking action for each type. By reading this article, you will learn how to assess your own organization’s AI adoption status and how to improve it.

Laggards (Low Will/Low Skill)

Organizations that belong to this quadrant have low motivation and low skill to adopt AI. They are either unaware of the benefits of AI, or resistant to change their status quo. They may face several challenges that hinder their AI adoption, such as:

• Lack of understanding the AI capabilities and applications to their business: They may not know how AI can solve their problems, improve their processes, or create new opportunities. They may also have unrealistic expectations or misconceptions about what AI can and cannot do. For example, they may think that AI can replace human intelligence or judgment, or that AI can work without data or supervision. These misunderstandings can lead to low motivation, low confidence, or low satisfaction with AI adoption.
• Lack of AI skilled resources: They may not have enough talent or expertise to design, develop, deploy, and maintain AI solutions. They may also struggle to find, attract, or retain AI talent in the competitive market. AI is a multidisciplinary and fast-evolving field that requires a combination of technical, analytical, and domain-specific skills. AI talent is in high demand and short supply, and many organizations face a skills gap or a skills mismatch when it comes to AI adoption.
• Interorganizational challenges, such as fear of AI, fear of business disruption, or lack of trust and collaboration. They may perceive AI as a threat to their jobs, their culture, or their values. They may also have difficulties in communicating, coordinating, or integrating AI across different teams, departments, or functions. According to a survey by PwC, 30% of respondents said they’re concerned about their role being replaced by technology in the next three years, According to Pew research center, 62% believe artificial intelligence will have a major impact on jobholders overall in the next 20 years, but far fewer think it will greatly affect them personally. People are generally wary and uncertain of AI being used in hiring and assessing workers. O’REILLY in 2020 survey ?almost 22% of respondents identified a lack of institutional support as the most significant issue.
• Lack of business sponsorship to adopt AI: They may not have a clear vision, strategy, or roadmap for AI adoption. They may also lack the support, commitment, or leadership from the top management or the key stakeholders.
• Lack of foundational elements to enable AI, such as data, infrastructure, or governance. They may not have access to high-quality, relevant, or diverse data to train, test, or validate AI models. They may also lack the technical, operational, or organizational capabilities to implement, monitor, or scale AI solutions. They may also face legal, ethical, or regulatory challenges or risks related to AI. According to a survey of 116 businesses worldwide by Statista, many firms are finding it difficult to make progress in establishing innovative, data driven organizations. Around 60 percent said that they were driving innovation with data in 2023.
• Lack of investment or initial investment cost: They may not have enough financial resources or budget to invest in AI. They may also face high upfront costs or long payback periods for AI projects. According to a report by McKinsey, investments in AI are a relatively small fraction of companies’ overall spending on digital technologies. 58% of respondents say less than one-tenth of their companies’ digital budgets go toward AI.

To overcome these challenges and move forward, laggard organizations need to adopt the following strategies:

For the Skill/Capability:

• Training and skilling are the main way to remove the illusion around AI. They need to educate themselves and their employees about the potential benefits, limitations, and best practices of AI. They can use online courses, workshops, webinars, or podcasts to learn the basics of AI and its applications to their business. For example, they can use the AI Business School by Microsoft to learn how to define an AI strategy, enable an AI-ready culture, and implement AI responsibly.
• Awareness about the potential benefits of AI and responsible and ethical AI. They need to raise awareness and interest among their employees and customers about how AI can help them achieve their goals, solve their pain points, or create new values. They also need to address the ethical, social, or environmental implications of AI and ensure that their AI solutions are fair, transparent, accountable, and trustworthy. For example, they can use the AI Ethics Principles by OECD to guide their AI adoption and ensure that their AI solutions respect human dignity, rights, and values.
• Identify quick wins and pilot projects to retain and use the developed skills. They need to start small and simple, and focus on low-risk, high-reward, or high-impact AI use cases that can demonstrate the value and feasibility of AI. They can use existing data, tools, or platforms to prototype, test, or deploy AI solutions and measure their outcomes and feedback. For example, they can use the AI Builder by Power Platform to create and use AI models without writing code or having AI expertise.

For the Will/Motivation:

• Identifying the sponsorship and including the executives in awareness and training. They need to secure the buy-in and support from the top management and the key stakeholders for AI adoption. They need to communicate the vision, strategy, and roadmap for AI adoption and how it aligns with the business objectives and priorities. They also need to involve the executives in the awareness and training activities and showcase the success stories and best practices of AI adoption.
• Identify business objectives and motivations and areas of improvement using AI. They need to understand their current situation, challenges, and opportunities, and how AI can help them improve their performance, efficiency, or innovation. They need to define clear and realistic goals, metrics, and indicators for AI adoption and how they relate to their key performance indicators (KPIs) or return on investment (ROI). For example, they can use the AI Maturity Model by Microsoft to assess their current AI capabilities and identify their AI goals and gaps.
• Assessing the current IT landscape, this will help to identify what is possible or what is not in the current state as well as how to accelerate other areas for future development. They need to evaluate their existing data, infrastructure, and governance capabilities and identify the gaps, bottlenecks, or risks that may affect their AI adoption. They need to prioritize and address the most critical or urgent issues and plan for the future needs and requirements for AI adoption. For example, they can use the AI Assessment by IBM to measure their readiness and identify their strengths and weaknesses for AI adoption.
• Benchmark your organization with others in the same industry: They need to benchmark their AI adoption against their competitors or peers and learn from their successes and failures. They need to identify the best practices, standards, or trends in their industry and how they can adopt or adapt them to their context and needs. For example, they can use the AI Index by Stanford University to track and analyze the global AI landscape and compare their AI progress and performance with others. Also, Accenture developed AI capability model to see how this is relevant to the organization and industry.
• Apply change management practices to address resistance and culture issues and help with AI adoption. They need to manage the human and organizational aspects of AI adoption and ensure a smooth and successful transition. They need to engage, empower, and motivate their employees and customers to embrace AI and overcome their fears or resistance. They also need to foster a culture of collaboration, experimentation, and learning for AI adoption. For example, those companies can use the ADKAR Model by Prosci to guide the management process and help the people to adopt AI. Another great video from Prosci regarding Applying ADKAR model to AI adoption Challenges.

Hesitant (Low Will/High Skill)

Organizations that belong to this quadrant have a high skill but a low motivation to adopt AI. They have the necessary capabilities and resources to implement AI, but they lack the incentives or interest to do so. They may face several challenges that hinder their AI adoption, such as:

• Lack of alignment between AI and business goals. They may not have a clear understanding of how AI can support their business objectives and priorities. They may also have conflicting or competing interests or agendas among different teams, departments, or functions. For example, they may have a siloed or fragmented approach to AI, where each unit pursues its own AI initiatives without coordination or collaboration with others. This can lead to inefficiency, inconsistency, or duplication of efforts and resources.
• Lack of innovation or experimentation culture. They may not have a culture that encourages and supports AI adoption. They may be risk-averse, complacent, or satisfied with the status quo. They may also lack the resources, incentives, or rewards to innovate or experiment with AI. For example, they may have a rigid or bureaucratic organizational structure, where decisions are made by a few senior leaders, and where failures are not tolerated or learned from. This can lead to stagnation, resistance, or inertia to change or improve.
• Lack of customer or user focus. They may not have a customer-centric or user-centric mindset when it comes to AI adoption. They may not understand the needs, preferences, or expectations of their customers or users. They may also not involve or engage their customers or users in the design, development, or deployment of their AI solutions. For example, they may have a product-centric or technology-centric approach, where they focus on the features or functionalities of their AI solutions, rather than the value or benefits they provide to their customers or users. This can lead to dissatisfaction, frustration, or churn of their customers or users.

To overcome these challenges and move to the next quadrant, hesitant organizations need to adopt the following strategies:

For the Skill/Capability:

• Keep the knowledge momentum and use it to accelerate the adoption. They need to leverage their existing skills and capabilities and apply them to AI solutions. They need to update and refresh their knowledge and skills regularly and keep up with the latest developments and trends in AI. For example, they can use online courses, workshops, webinars, or podcasts to learn the advanced topics and techniques of AI and its applications to their business. This can help them to maintain their competitive edge and enhance their performance and efficiency.

For the Will/Motivation:

• Build competition and recognition elements. They need to create a sense of urgency and excitement for AI adoption. They need to benchmark their AI adoption against their competitors or peers and learn from their successes and failures. They also need to recognize and reward their AI teams and showcase their achievements and best practices. For example, they can use the AI Index by Stanford University to track and analyze the global AI landscape and compare their AI progress and performance with others. This can help them to motivate and inspire their AI teams and foster a culture of excellence and innovation.
• Identify how sponsors and executives would like to benefit from their current capabilities from skills, data, and others. They need to align their AI adoption with the business objectives and priorities of the top management and the key stakeholders. They need to communicate the value proposition and the business case for AI adoption and how it can help them achieve their goals, solve their pain points, or create new value.
• Link business objectives to potential quick wins with clear return on investment. They need to start small and simple, and focus on low-risk, high-reward AI use cases that can demonstrate the value and feasibility of AI. They need to use existing data, tools, or platforms to prototype, test, or deploy AI solutions and measure their outcomes and feedback.
• Identify what are the internal policies that impact the quick move to AI. They need to review and revise their existing policies and processes that may affect their AI adoption. They need to ensure that their policies and processes are agile, flexible, and supportive of AI adoption. They also need to address legal, ethical, or regulatory issues or risks related to AI. For example, they can use the AI Ethics Principles by OECD to guide their AI adoption and ensure that their AI solutions respect. Moreover, Harvard Business Review published an article in 2022 regarding the conversations that companies need to have regarding the ethics and AI

Aspirationals (High Will/Low Skill)

Organizations that belong to this quadrant have high motivation but a low skill to adopt AI. They have the desire and the drive to implement AI, but they lack the capabilities and resources to do so. They may face several challenges that hinder their AI adoption, such as:

• Lack of understanding the AI capabilities and applications to their business: They may not know how AI can solve their problems, improve their processes, or create new opportunities. They may also have unrealistic expectations or misconceptions about what AI can and cannot do.
• Lack of AI talent or expertise: They may not have enough people who can design, develop, deploy, and maintain AI solutions. They may also have difficulties in finding, attracting, or retaining AI talent in the competitive market. AI is a multidisciplinary and fast-evolving field that requires a combination of technical, analytical, and domain-specific skills. AI talent is in high demand and short supply, and many organizations face a skills gap or a skills mismatch when it comes to AI adoption.
• Lack of data quality or availability. They may not have access to high-quality, relevant, or diverse data to train, test, or validate AI models. They may also have challenges in collecting, storing, processing, or analyzing data for AI purposes. Data is the fuel for AI, and without sufficient and reliable data, AI solutions cannot function properly or deliver value. According to?Harvard Business Review, “poor quality data is enemy number one to the widespread, profitable use of machine learning.”
• Lack of technical infrastructure or scalability: They may not have the necessary hardware, software, or platforms to implement, monitor, or scale AI solutions. They may also have issues with compatibility, integration, or security of their AI systems. AI solutions require robust and flexible infrastructure that can handle large amounts of data, complex computations, and dynamic changes.
• Lack of trust or transparency: They may not have confidence or understanding of how their AI solutions work, behave, or perform. They may also have concerns about the ethical, social, or environmental implications of their AI solutions. AI solutions can be opaque, unpredictable, or biased, and they can have unintended or harmful consequences.

To overcome these challenges and move to the next quadrant, aspirational organizations need to adopt the following strategies:

For the Skill/Capability:

• Training and skilling are the main ways to acquire the knowledge and expertise needed for AI adoption. They need to educate themselves and their employees about the potential benefits, limitations, and best practices of AI. They can use online courses, workshops, webinars, or podcasts to learn the basics of AI and its applications to their business. For example, they can use the AI Business School by Microsoft to learn how to define an AI strategy, enable an AI-ready culture, and implement AI responsibly.
• Awareness about the potential benefits of AI and responsible and ethical AI. They need to raise awareness and interest among their employees and customers about how AI can help them achieve their goals, solve their pain points, or create new values. They also need to address the ethical, social, or environmental implications of AI and ensure that their AI solutions are fair, transparent, accountable, and trustworthy.
• Identify quick wins and pilot projects to retain and use the developed skills. They need to start small and simple, and focus on low-risk, high-reward, or high-impact AI use cases that can demonstrate the value and feasibility of AI. They can use existing data, tools, or platforms to prototype, test, or deploy AI solutions and measure their outcomes and feedback.

For the Will/Motivation:

• Identifying the sponsorship and including the executives in awareness and training. They need to secure the buy-in and support from the top management and the key stakeholders for AI adoption. They need to communicate the vision, strategy, and roadmap for AI adoption and how it aligns with the business objectives and priorities. They also need to involve the executives in the awareness and training activities and showcase the success stories and best practices of AI adoption.
• Identify business objectives and motivations and areas of improvement using AI. They need to understand their current situation, challenges, and opportunities, and how AI can help them improve their performance, efficiency, or innovation. They need to define clear and realistic goals, metrics, and indicators for AI adoption and how they relate to their key performance indicators (KPIs) or return on investment (ROI).
• Assessing the current IT landscape, this will help to identify what is possible or what is not in the current state as well as how to accelerate other areas for future development. They need to evaluate their existing data, infrastructure, and governance capabilities and identify the gaps, bottlenecks, or risks that may affect their AI adoption. They need to prioritize and address the most critical or urgent issues and plan for the future needs and requirements for AI adoption.
• Partnering with experts, outsourcing, or training. They need to leverage the skills and capabilities of external parties who can help them with the adoption of AI. They can partner with experts, consultants, or vendors who can provide them with AI solutions, services, or platforms. They can also outsource some of their AI tasks or projects to third-party providers who can deliver them faster, cheaper, or better. They can also invest in training their own employees or hiring new ones who have AI skills or experience.
• Creating focus groups with outcome key results, leveraging the partner ecosystem to start doing while learning. They need to create and empower small teams or groups who can work on specific AI use cases or projects and deliver tangible and measurable results. They need to leverage the partner ecosystem to provide them with guidance, support, or feedback on their AI initiatives. They need to adopt a learning-by-doing approach and experiment, iterate, and improve their AI solutions.
• Benchmark your organization with others in the same industry to learn from what others are doing and how this impacted their business. They need to benchmark their AI adoption against their competitors or peers and learn from their successes and failures. They need to identify the best practices, standards, or trends in their industry and how they can adopt or adapt them to their context and needs.

Exemplars (High Will/High Skill)

Organizations that belong to this quadrant have a high motivation and a high skill to adopt AI. They are visionary leaders or agile innovators who leverage AI to create value and competitive advantage. According to Accenture the art of AI maturity report, only 12% of companies are considered AI Achievers.

They face fewer and different kind of challenges than the other quadrants, but they still need to maintain and improve their AI adoption, such as:

• Keeping up with the rapid and dynamic changes in AI: They may need to constantly monitor and update their AI solutions to ensure their relevance, accuracy, and reliability. They may also need to explore and experiment with new AI techniques, tools, or platforms that can offer better performance, efficiency, or innovation. AI is a fast-evolving and competitive field, and staying ahead of the curve requires constant learning and improvement.
• Balancing the trade-offs between AI and human factors. They may need to find the optimal balance between AI and human roles, responsibilities, and relationships. They may also need to consider the ethical, social, or environmental impacts of their AI solutions and ensure that they respect human dignity, rights, and values. AI can augment and complement human capabilities, but it can also challenge and disrupt them. Finding the right balance requires careful design, evaluation, and communication.
• Scaling and sustaining AI adoption across the organization. They may need to scale and sustain their AI adoption across different teams, departments, or functions. They may also need to integrate and coordinate their AI solutions with other systems, processes, or actions. AI can create value and impact at different levels and dimensions of the organization, but it can also create complexity and inconsistency. Scaling and sustaining AI adoption requires robust and flexible infrastructure, governance, and culture.
• Balancing the benefits and risks of AI. They need to weigh the trade-offs and implications of AI for their business outcomes, customer satisfaction, and social responsibility. They need to manage the ethical, legal, or regulatory challenges or risks related to AI and ensure that their AI solutions are fair, transparent, accountable, and trustworthy. They need to respect the human values and rights of their employees, customers, and partners who may be affected by the AI solutions.

To maintain and improve their AI adoption and stay ahead of the competition, Exemplar organizations need to adopt the following strategies:

For the Skill/Capability:

• Keeping up with the rapid and dynamic changes in AI: They need to leverage their existing skills and capabilities and apply them to new AI solutions. They need to update and refresh their knowledge and skills regularly and keep up with the latest developments and trends in AI. They can also seek guidance or mentorship from experts, consultants, or partners when needed as well as sharing and leading the innovation using their existing capabilities.
• Experimenting and learning: They need to adopt a learning-by-doing approach and experiment, iterate, and improve their AI solutions. They need to test and validate their AI solutions using real data and feedback from their employees, customers, or partners. They need to learn from their successes and failures and share their insights and lessons learned with others. Share successful stories and measure and report the outcomes and impacts of the AI solutions and how they contributed to the key performance indicators (KPIs) or return on investment (ROI).
• Empowering AI teams: They need to create and empower cross-functional and diverse teams or groups who can work on specific AI use cases or projects and deliver tangible and measurable results. They need to provide them with the necessary resources, tools, and platforms to design, develop, deploy, and maintain AI solutions. They need to recognize and reward their AI teams and showcase their achievements and best practices.

For the Will/Motivation:

• Balancing the trade-offs between AI and human factors. They need to find the optimal balance between AI and human roles, responsibilities, and relationships. They need to design, evaluate, and communicate their AI solutions with the human factors in mind. They need to involve and consult with the relevant stakeholders and ensure that their AI solutions are fair, transparent, accountable, and trustworthy.

For both:

• Scaling and sustaining AI adoption across the organization. They need to scale and sustain their AI adoption across different teams, departments, or functions. They need to provide them with the necessary resources, tools, and platforms to design, develop, deploy, and maintain AI solutions. They need to recognize and reward their AI teams and showcase their achievements and best practices. They need to integrate and coordinate their AI solutions with other systems, processes, or actions.

In this article, I have explored the current status and future prospects of AI adoption in organizations. I have used the Organization Skill or Capability vs Will or motivation matrix as a framework to categorize the organizations based on their skill and will to adopt AI. I have discussed the challenges and opportunities for each type of organization and provided some strategies and recommendations to accelerate their AI adoption journey.

There are other important models to assess the readiness and status of AI Adoption, for example, BOE model, which integrates 3 factors: external pressure, organizational readiness, and perceived benefits and FACC model which also integrated 4 factors, Functionality, Availability, Complexity, and Cost.

AI is a powerful and disruptive technology that can transform every sector and industry in the world. However, AI adoption is not a one-size-fits-all solution, and it requires careful planning, execution, and evaluation. Organizations need to assess their current situation, challenges, and opportunities, and align their AI adoption with their business objectives and priorities. They also need to address the ethical, legal, and social implications of AI and ensure that their AI solutions are fair, transparent, accountable, and trustworthy.

AI adoption is not a destination, but a journey. Organizations need to keep learning, experimenting, and improving their AI solutions and capabilities. They also need to collaborate and communicate with their stakeholders, partners, and customers to ensure the success and sustainability of their AI initiatives. By doing so, they can reap the benefits of AI and create value and competitive advantage for themselves and society.

Also publish in melsatar.blog and Medium.