Artificial intelligence (AI) is a game-changing technology that can unlock staggering amounts of value for businesses across industries. Its ability to analyze vast amounts of data, uncover patterns, and automate decision-making processes can help companies drive efficiency, innovation, and profitability. However, while AI has shown its remarkable ability to drive value for many organizations, others wrestle with fully realizing its business potential.
Dr. Michael Proksch, Chief Scientist at AccelerED, has spent more than decade exploring AI value creation and has identified the obstacles and strategies that successful AI achievers use to drive business growth. With experience at Fortune 500 companies across Europe, Asia, and the U.S., Dr. Proksch has translated AI potential into tangible success, providing tailored solutions that offer competitive advantages and support long-term growth.
This noted AI expert can now add “author” to his résumé. Dr. Proksch’s book, “The Secrets of AI Value Creation: A Practical Guide to Business Value Creation with Artificial Intelligence from Strategy to Execution,” co-authored with Dr. Wilhelm Bielert, CIO of PremierTech and Nisha Paliwal, MVP for Data and AI at Capital One, offers leaders actionable insights for overcoming AI adoption challenges and maximizing its business impact.
Dr. Proksch spoke with Ben Blanquera, Vice President at Rackspace Technology and a member of the CDO Magazine Global Editorial Board, about his new book and achieving AI success, touching on key elements, including technology, data, and the psychological barriers to AI adoption.
Playbook Takeaways
A holistic approach to AI is required: No single factor in AI value creation works in isolation. Companies must address all key factors at once and explore how they intersect and influence each other.
AI solutions must be aligned with practical business use cases: For an AI solution to truly create value, it must give an organization a competitive edge, often by taking an approach that differs from the industry's standard practices.
Understanding the psychology behind AI adoption: AI solutions must be developed around employees and designed with their day-to-day challenges in mind.
Data quality is critical for functional AI solutions: To drive real value, data must be actionable and capable of addressing a specific, ongoing problem.
According to Dr. Proksch, authoring a definitive book on AI solutions and value creation addresses a significant gap in the AI discussion. Although many books cover areas such as analytics, cloud computing, and technology, none offer a holistic perspective that bring all these elements together. He and his co-authors realized that to effectively harness AI for value creation, a comprehensive approach was necessary—one that seamlessly integrated insights from multiple disciplines into a cohesive strategy.
Reflecting on his early career experiences, when technology and analytics were still in their formative stages and there were no advanced dashboard technologies, distributed computing, or cloud solutions available, Dr. Proksch says, “We had to build everything from scratch, and while we created functional solutions, they often fell short of delivering the value we anticipated. This gap led me to question what we might be missing in our approach to leveraging AI effectively.”
While technology and analytics are crucial, he says, they are only part of the equation. Integrating business considerations, addressing data gaps, and incorporating psychological factors are equally important. This holistic approach has been missing in many organizations, with departments often working in isolation, focusing on individual projects rather than a unified strategy.
The question arose: how can I make a change? Dr. Proksch credits his ability to find the answer to the guidance of two key mentors—Nisha Paliwal and Dr. Wilhelm Bielert—who became co-authors of the book. Each of them brought a unique background. While Dr. Proksch was always focused on building high-value AI solutions holistically, Nisha Paliwal was brilliant in data management and technology, while Dr. Wilhelm Bielert exceled in change management, enterprise organization, and integration. Dr. Proksch says, “We held different pieces of the puzzle, which ultimately formed the foundation for the book.”
Dr. Proksch says the key to unlocking real value with AI lies in identifying use cases that provide a competitive edge, rather than falling back on generic industry solutions. He emphasizes that achieving AI success involves more than just implementing technology—it also requires careful consideration of business domain knowledge, data and its management, algorithmic and technology, and even psychology. "Fast decision-making and bias-free models are important, but they’re not enough," he contends. "Organizations must focus on use cases that align with their strategic goals and deliver unique value."
When it comes to deciding whether to build or buy AI solutions, companies must evaluate their strategic objectives. For businesses that do not view AI as a core differentiator, off-the-shelf solutions may suffice. However, those organizations aiming to gain a true competitive advantage should consider building custom AI tools tailored to their needs. Larger companies with more resources tend to benefit more from AI as a differentiator, while smaller organizations may see less impact.
“Ultimately, the right approach depends on the unique goals and needs of the business,” says Dr. Proksch.
When addressing the challenges of data in AI, two key issues often arise: data quality and data value. Quality is crucial because AI systems rely on accurate and granular data to recognize and learn patterns effectively. High-quality data must be detailed and contextually relevant to ensure that AI can make precise predictions and decisions. Without this precision, AI models can produce unreliable results or fail to meet their intended objectives. Furthermore, understanding the context in which data is used is essential for its successful application.
Another common challenge companies confront, Dr. Proksch says, is determining the value of data. Having large volumes of data is less important than having data that is valuable and relevant to the business problem at hand. To effectively leverage AI, organizations should start by defining their desired business outcomes and then identify the data that can drive those outcomes. This approach ensures that data is actionable and serves a clear purpose in achieving business goals. “By focusing on actionable data and understanding how it can influence outcomes, companies can better harness AI's potential to create value,” said Dr. Proksch.
When it comes to creating value through AI, one of the keys often overlooked is the psychological component. The technology itself may be advanced, but without buy-in from stakeholders—whether internal employees or external customers—adoption risks remain high.
Dr. Proksch notes that dashboards, despite being around for decades, still face low adoption rates in many organizations. The genesis of this issue is less technological than psychological. While dashboards are seen as tools to augment decision-making, AI introduces the fear of fully automating decisions and rendering certain roles obsolete. This fear can slow down the implementation of AI projects, as employees struggle to trust whether the organization's intentions are supportive or harmful.
In reality, he says, AI has more often reshaped jobs than eliminated them, especially in the case of generative AI, where the human role has evolved to reviewing and correcting AI-generated content.
“The key to overcoming these psychological challenges lies in building trust and ensuring that AI solutions are seen as partners in improving performance, rather than threats to job security,” says Dr. Proksch.
To derive lasting value from AI, organizations must adopt a broader and more strategic mindset that goes beyond questions of technology. Dr. Proksch warns that neglecting any single aspect is a recipe for failure, as the formula for AI value creation is multiplicative in nature, not additive. Executives should take a step-by-step approach, identifying overlaps between different factors and ensuring cross-stakeholder collaboration. True value creation comes from integrating AI as a partner in day-to-day functions, leading to meaningful adoption and maximizing return on investment.
About Ben Blanquera
Ben Blanquera is a Vice President with Rackspace Technology. Rackspace is a global leading hybrid cloud services provider with specialties in Data, AI, Application Development and Security Modernization. Ben is passionate about creating amazing business outcomes by leveraging data and analytics. He is on the CDO magazine editorial board and is interviewing global CDOs to gain their insights to create a ‘playbook’ for the industry.