Andrew Spanyi

Few companies deliberately integrate process management and knowledge management practices when deploying AI. Process management (PM) and knowledge management (KM) typically reside in separate silos where process often sits in operations with an emphasis on Lean Six Sigma and knowledge management often rests in HR. Meanwhile, AI initiatives are regularly led by data/IT teams. Major opportunities are missed as these three initiatives are rarely integrated.

Quantum computing stands at an intriguing but early stage of development. The technology is advancing, and there are credible signs of progress across hardware, algorithms, and ecosystem readiness. However, the leap from controlled pilots to mainstream enterprise adoption remains substantial. For now, quantum computing is best understood not as an immediate disruptor, but as a strategic, long-term investment—one that organizations should monitor closely, experiment with cautiously, and prepare for thoughtfully.

A great deal has been written about readiness for artificial intelligence (AI). In Factors influencing readiness for artificial intelligence: a systematic literature review, the authors examined 52 papers to study AI readiness factors.

New research reveals a widening gap between hype and real enterprise value. A recent article reviews MIT’s The GenAI Divide: State of AI in Business 2025, revealing that 95% of generative-AI pilot programs fail to deliver meaningful business outcomes, such as revenue growth or productivity improvements.

As artificial intelligence (AI) systems rapidly proliferate—powering decisions in healthcare, finance, employment, and public safety—the call for regulatory oversight has intensified. Yet a stubborn myth persists: that regulation and innovation are fundamentally at odds. Many technologists and policymakers argue that oversight stifles creativity and hinders emerging ventures. This article challenges that assumption. In fact, well-designed regulation doesn’t just protect society—it facilitates sustainable innovation. By creating clarity, trust, and a fair playing field, regulation can become a foundational driver of progress in the AI era.

The volume of headlines around AI is staggering. In boardrooms around the country, it's being hailed as the most revolutionary technology since the Internet. But while leaders are fascinated by the promise of artificial intelligence, few are seeing significant returns on their investments. In fact, many AI initiatives fail. However, there are exceptions. Companies such as Mars Wrigley, Colgate Palmolive, Turbo Tax and Pega Systems are putting AI into action.

Deploying artificial intelligence (AI) has complex challenges concerning ethics, transparency, bias, and fairness. AI governance can mitigate these challenges. What is AI governance? OECD has proposed that artificial intelligence (AI) governance refers to the comprehensive framework of policies, regulations, ethical guidelines, and processes designed to oversee the development, deployment, and utilization of artificial intelligence (AI) systems in a manner that is ethical, transparent, and aligned with societal values. According to IBM, artificial intelligence (AI) governance refers to the processes, standards and guardrails that help ensure AI systems and tools are safe and ethical. AI governance frameworks direct AI research, development and application to help ensure safety, fairness and respect for human rights.

While artificial intelligence (AI) has the potential to be transformative, the track record to date is disappointing. Although billions have been invested in AI, recent research reveals that only 1 percent of companies surveyed consider themselves to be “mature” – i.e. to have fully integrated AI into workflows and thereby produce better business outcomes. The same research report found that the biggest barrier to scaling AI is not employees—but leaders. Mayer, Hannah, Lareina Yee, Michael Chui, and Roger Roberts. "Superagency in the Workplace: Empowering People to Unlock AI’s Full Potential." McKinsey & Company, January 28, 2025. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work.

According to a recent Boston Consulting Group (BCG) report, while there is much hype around artificial intelligence (AI), the value is hard to find. Based on recent research involving more than 1,000 companies worldwide, only 22% of companies have advanced beyond the proof-of-concept stage to generate some value, and only 4% are creating substantial value.

It is well known that artificial intelligence (AI) enables better, faster and more automated decisions.  Indeed, it has been proposed that AI is driving a resurgence of interest in redesign business processes.[i] That’s partly due to the ability of certain AI tools, such as robotic process automation (RPA) which when combined with machine learning as “intelligent process automation,” can automate information-intensive processes. It has also been argued that AI fits well into improvement methods such as Lean Six Sigma and can be applied at each stage of the so called DMAIC process (Define-Measure-Analyze-Improve- Control).[ii] Note that Six Sigma and Lean Six Sigma are highly codified and structured methods of process improvements which have a strong bias towards incremental improvement within organizational boundaries. The integration of AI into process improvement may have the potential to reignite interest in more major change – targeted at large enterprise processes – perhaps even reengineering.

While excitement about generative AI is high, some questions persist as to how much value is being delivered. AI has been used by leading firms such as Amazon and Netflix to improve shopping recommendations, but examples of significant applications to improve overall business performance are not abundant. One area where AI has considerable potential is new product development (NPD). The NPD process has not changed much in most organizations for decades with fewer than 30% of new product projects becoming commercial successes. Yet only 13% of firms are using AI in NPD.

While randomized controlled trials (RCTs) have traditionally been considered the gold standard for drug development, it is widely recognized that RCTs are expensive, lengthy, and burdensome on patients. According to some estimates, it takes more than a billion dollars in funding and a decade of work to bring one new medication to market. Despite exciting advances in genomics, patient-centric awareness, decentralized clinical trials, and the application of artificial intelligence (AI), there is a lack of compelling evidence to date that these trends have had a significant impact on the time and cost of the typical oncology clinical trial.

The central role of process in digital transformation was recognized by MIT scholars a decade ago. Similarly, McKinsey emphasized that the actions organizations can take to encourage digital process innovation involves mapping and then streamlining selected end-to-end business processes and gaining a clear view of how information and data are managed across the company.

In a recent LinkedIn post, David Rogers rightly described digital transformation as a combination of both digital strategy as well as organizational transformation. His simple formula reveals why so many companies struggle with their digital programs. It is: DX = D strategy + organizational X. It’s not enough to craft a business strategy enabled by digital. Organizational transformation is also needed. There’s the rub.

We have known for over a decade that a major advantage of digital maturity is that higher digital maturity drives better financial performance. More recently, a survey of over 1,200 executives revealed that digitally mature companies were three times more likely than lower maturity companies to outperform their industry average on key financial metrics.

The business benefits of focusing on customer experience have been known ever since 1954 when Peter Drucker wrote that “there is only one purpose of a business: to create a customer.” However, it wasn’t until 1989, when Jan Carlson, the chief executive officer of Scandinavian Airlines (SAS), published Moments of Truth advocating a focus on customer experience (CX) and providing practical guidance. He famously coined insights such as:

Why do so many companies talk about digital transformation, and yet they deploy digital technologies for modest incremental improvement? They automate simple, repetitive, rule-based tasks. They don’t redesign. They tinker at the margins with AI through small proof of concept projects and pilots and fail to deploy models at scale for true economic value.

Large scale change has never been easy. Nearly three decades ago, leadership guru Dr John Kotter reported that 70% of all major change efforts in organizations failed. Just a couple years later, the late Dr. Michael Hammer estimated a 70% failure rate for the radical reengineering efforts. Now, that transformational efforts are often driven by technology, the recent success rate is equally bleak according to research by BCG. The root cause of failure with large scale digital change is captured by George Westerman’s first law of digital transformation, which states that: Technology changes quickly, but organizations change much more slowly. 

There is a great deal of excitement these days around “digital transformation.” Instead of being preoccupied with “transformation,” companies may be better off paying attention to their level of maturity in deploying digital technologies – and then working diligently to become more digitally mature.

A recent survey on Big Data and Artificial Intelligence (AI) reported that cultural challenges, not technological ones, were the biggest hurdle to overcome around Big Data and AI initiatives. According to this 2021 survey, the vast majority of respondents — 92% of mainstream companies — continued to struggle more with cultural challenges than with technological ones.