Numerous articles continue to insist that companies are failing to see a return on their artificial intelligence (AI) investments. And those investments are considerable. Gartner analysts predict spending on AI this year alone will total $2.52 trillion.[1] According to business and technology writer Beth Stackpole, “Few organizations have successfully parlayed artificial intelligence experimentation into large-scale initiatives that move the needle on critical business metrics like revenue growth or productivity gains.”[2] One of the reasons for this failure, she insists, is that firms are not transforming to match this new business environment. Nick van der Meulen, a Research Scientist at MIT Sloan Center for Information Systems Research, told Stackpole, “A common pattern we see is that organizations are applying yesterday’s best practices to an inherently different technology.” And, freelance writer Kristin Burnham reports that an MIT research paper entitled “Chaining Tasks, Redefining Work: A Theory of AI Automation” concludes, “AI’s biggest impact comes from how it reshapes entire workflows — specifically, how tasks are sequenced, grouped, and handed off between humans and machines.”[3]

Transforming Business Operations in the AI Era

Earlier this year, Blaine Carter, Global CIO at FranklinCovey, told a group of convention goers that the greatest risk for enterprises today is hesitation to adopt AI. According to Carter, “The message is clear: AI strategy is now a business credibility issue, not just a technology roadmap goal.”[4] As noted above, however, enterprises that merely adopt AI as an add-on tool without transforming their business models is a mistake. McKinsey & Company analysts explain, “The companies that are truly innovating with AI are doing something very different from their peers: They are conceptualizing and developing AI capabilities that reshape their products, services, core business processes, and organizational systems.”[5] In other words, the only way to move the business needle is to transform the business. The question is: How can a company transform successfully? McKinsey analysts offer twelve “guideposts along the transformation journey to value.” They are:

1) Create enduring capabilities. The most successful companies, according to McKinsey analysts, are “rewired” companies. Boston Consulting Group analysts agree. They insist investments in AI should “aim to create new capabilities or new business advantage, changing how the company competes, not just how efficiently it runs and scales.”[6] McKinsey analysts conclude, “When these new capabilities are built — and they take time to build — the company accelerates its business transformation with technology and outperforms its peers. The capabilities become the competitive advantage.”

2) Focus on economic leverage points. McKinsey analysts note, “Any business model has a few key economic leverage points that provide the biggest impact when improved with AI.” BCG analysts believe AI investments should “sustain and secure the core business, supporting operating systems and platforms while ensuring compliance and resilience.”

3) Make sure investments move the business needle. Stackpole writes that one mistake companies often make is “starting AI projects without a clear path to value.” McKinsey analysts note that the most successful businesses “concentrated their efforts on one to three business domains, reinventing them with AI. That required creative problem-solving, coordinated use of tech and non-tech levers, maniacal focus on the customers/users, and clear accountability for the business KPIs that mattered most.”

4) Build the AI muscle of senior business leaders first. According to McKinsey analysts, senior leaders must drive business transformation. This is not a new revelation; however, it’s worth reiterating. They write, “At leading companies, they actively own the tech agenda — from defining how the business will be reimagined with technology to steering solution development to ensuring value delivery.”

5) Business transformation is also people transformation. Over the years, I have made this point repeatedly. Business transformation is never just about the technology. McKinsey analysts explain, “Leading companies have largely completed this transition, which results in higher talent density and much tighter business ownership. As AI agents take on more of the coordination, execution, and routine decision-making work, human roles shift up the value stack.”

6) Business must operate at computer speed. McKinsey analysts explain, “Speed requires embedding AI engineering and other functional talent directly in the business, maximizing technology and data reuse through platforms, and governing with clear business outcomes and sustained funding tied to results, not projects. This shortens cycle times dramatically. Without it, no company can truly innovate with technology and AI at scale; they will simply be too slow.” One of the reasons that Enterra Solutions® has focused on Enterra’s Autonomous Decision Science™ (ADS®) and Generative AI powered business applications is because we understand the need for speed in today’s business environment. ADS can transform how enterprise optimization, planning, and decision-making are performed. Enterra’s business applications pair industry-specific business knowledge and data, with Enterra’s proprietary math and human-like reasoning engines, to generate insights and recommendations with the subtlety and judgment of a company’s best experts — but at a level of speed and accuracy humans can’t achieve.

7) Invest in tech platforms as strategic assets. According to McKinsey analysts, “Platforms determine a company’s execution speed, drive down its unit costs through reuse, get technology and data into the hands of the people who need them, and enable AI to scale responsibly.” BCG analysts add, “IT investments modernize and scale capabilities, enabling incremental performance improvements, regulatory compliance, and future viability.”

8) Democratize data. If a business is going to truly adapt to the AI Era, it must ensure that everyone has access to the data and tools they need. McKinsey analysts insist, “In most organizations, data often still acts as the constraining factor. Scaling AI therefore starts by productizing data — making it easy to discover, access, and consume across many AI-powered applications.”

9) Design for adoption and build for scale. McKinsey analysts observe, “AI systems create value only when they are adopted and scaled. That may sound obvious, yet it remains one of the hardest challenges. Adoption often fails because adjacent upstream and downstream processes are left unchanged.” Stackpole adds, “Organizations see the biggest financial gains when they make the leap from pilots to new ways of working built around AI.”

10) Develop trust. Users rightfully harbor a good deal of distrust when it comes to use of AI. They’ve all heard about hallucinations, AI slop, rogue systems, and inaccurate responses. McKinsey analysts explain, “Digital trust grows when stakeholders have confidence that your organization protects consumer data, enacts effective cybersecurity, offers trustworthy AI-powered products and services, and provides transparency around AI and data usage.”

11) Master agentic engineering. According to McKinsey analysts, “Leading companies are moving quickly to master agentic engineering. They are ingesting unstructured data, extending their AI platforms with agentic capabilities, automating guardrails and controls, and rapidly experimenting to codify what works into a repeatable agentic playbook.” As computing costs rise, mastering agentic engineering will become even more critical.

12) (Re)learn like your business depends on it. McKinsey analysts bluntly conclude, “Organizations that learn, unlearn, and relearn the fastest have the advantage.”

Concluding Thoughts

Stackpole notes that it is a mistake to overlook “how AI changes the business itself.” However, transforming businesses to match the requirements of the AI Era won’t be easy. Burnham notes, “Many companies expect rapid returns from AI investment, but research suggests that meaningful gains often emerge only after organizations have adapted their workflows and built sufficient capability.” And that takes, time, effort, and resources. Burnham adds, “Organizations that treat AI as a plug-in tool may see incremental improvements, while those that rethink how work is structured — grouping AI-compatible tasks, reducing unnecessary handoffs, and redesigning workflows — are more likely to unlock its full potential.” McKinsey analysts agree. They conclude, “Building the complete set of rewired capabilities is the cornerstone of every successful tech and AI transformation.”

Footnotes

[1] Staff, “Gartner Says Worldwide AI Spending Will Total $2.5 Trillion in 2026,” Gartner, 15 January 2026.

[2] Beth Stackpole, “What leaders still get wrong about AI,” MIT Sloan Management School, 18 May 2026.

[3] Kristin Burnham, “How AI is reshaping workflows and redefining jobs,” MIT Sloan Management School, 22 April 2026.

[4] Samara Lynn, “AI: Why ‘doing nothing’ is now the riskiest strategy,” Computing, 24 March 2026.

[5] Alex Singla, Alexander Sukharevsky, Eric Lamarre, Kate Smaje, and Robert Levin, “The AI transformation manifesto,” McKinsey & Company, 7 April 2026.

[6] Rohit Nalgirkar, Michael Grebe, Andrew Arcuri, Bill Braun, Eugenia Zanina, Bhavika Panjwani, and Howard Rubin, “How CIOs Can Prove the Value of Technology in the Age of AI,” Boston Consulting Group, 9 June 2026.