Pundits have loudly and repeatedly warned businesses that if they don’t transform into digital enterprises and adopt artificial intelligence (AI) they will no longer be competitive. As a result, many companies pursued ill-conceived projects that failed to launch. Lots of time and money have been wasted as organizations struggled to get a return on their digital transformation investment. Transformation isn’t easy. It takes thought, effort, resources, and time. Journalist Mark Samuels explains, “Digital transformation involves using technology to improve business effectiveness or efficiency. The idea is to use technology not just to replicate an existing service digitally, but to transform that service into something significantly better. While the concept sounds simple, digital transformation can be a long, expensive, and complicated process that doesn't always go to plan.”[1] In today’s business environment, ensuring AI is part of that transformation is table stakes. However, implementing AI also requires time, effort, resources, and clear thinking.

Business writer Beth Stackpole explains, “Organizations can be so consumed with doing something AI-related that they forget that the technology is another tool in the toolbox — and a complex one at that. To generate real value from AI, organizations must invest in the right capabilities and practices to do the work properly as opposed to expecting instantaneous results.”[2] Although Stackpole implies that everyone knows what the “work” is that needs to be done properly, the very nature of work is changing in today’s complex business environment.

Changing Nature of Work

Analysts from the Boston Consulting Group (BCG) insist that traditional, human-led work functions are “breaking down, due to the convergence of several forces.”[3] Those forces are:

• Artificial Intelligence. “AI is compressing knowledge work while AI-enabled competitors operate with leaner cost bases and faster decision cycles.”

• Complex Business Landscape. “Regulatory and geopolitical complexity is increasing administrative burdens, even as corporate functions are weighed down by decades of organizational layering.”

• Upskilled Workforce Requirements. “Talent requirements are tilting toward digital and technology specialists, and decision speed has become a competitive advantage.”

For years I have argued that today’s organizations need to increase their decision speed. That’s why Enterra Solutions® has focused on advancing Enterra’s Autonomous Decision Science™ (ADS®). Using ADS, organizations can go beyond advanced analytics to understand data, generate insights, answer queries, and make decisions at the speed of the market. This powerful capability uniquely enables “End-to-End Value Chain Optimization and Decision-Making” at scale and allows organizations to uncover and understand the inter-relationships that lead to innovative new product development and innovation, heightened consumer understanding, and targeted marketing, revenue growth tactics, and intelligent demand and supply-chain planning.

Digital Supply Chains: Tweaking Processes and Adding Value

Supply chain journalist Robert J. Bowman writes, “Reports of supply chains adopting generative AI are rich with stellar results: fewer errors, faster response times, leaps in productivity.”[4] You can almost anticipate the “but” coming. In this case, Bowman writes, “The problem: For the moment, such outcomes are rare. That’s because the number of businesses and supply chains to have taken GenAI beyond the pilot stage to full implementation is relatively small.” Bowman reports that a new study identifies one of the reasons behind these results. He explains, “Turns out that the culprit behind the lack of AI progress wasn’t the technology itself. It was the sticking point that bedevils just about every new tech implementation: a lack of proper business processes to support the change.”

This finding concurs with the findings of the BCG analysts. They explain, “What distinguishes the leading companies on the right side of the ledger is not simply greater use of AI, but how it is applied. Rather than layering tools onto existing tasks, they have rebuilt functional activities around AI — reworking processes and how tasks are handled end to end, simplifying decisions, and redesigning how support functions operate. This helps explain why many left-side organizations deploying AI are not seeing comparable results: the technology is being added, but the underlying model remains unchanged.”

To help organizations put the right processes in place to take advantage of advanced AI solutions, Enterra® has introduced the Enterra Dynamic Enterprise Resiliency System™ (EDERS™). The System was designed to enable enterprises to convert macroeconomic and geopolitical trends into a powerful competitive advantage. It anticipates market shifts and recommends optimal actions and makes decisions with up to 90% accuracy, even in the most volatile economic and political environments. Enterra’s ADS Platform allows EDERS to process vast amounts of internal and external data, anticipating risk to an organization’s most critical assets, as well as those of its competitors. EDERS then systemically predicts and recommends the best actions for the organization to take to avoid or deflect risk, while exploiting market opportunity to win. Enterra® also offers Enterra Business WarGaming™, which includes Enterra Global Insights and Decision Superiority System™ (EGIDS™), which can help business leaders rapidly explore a multitude of options and scenarios.

Samuels observes, “Proving the value of AI is more challenging than it sounds.”[5] He suggests five ways that organizations can improve the ROI of AI investments. They are:

1) Concentrate on business outcomes. Stackpole agrees with this insight. She insists that all AI projects should start with “a clear path to value.”

2) Determine the operational benefits. BCG analysts explain, “Many organizations lack clear KPIs for AI adoption, underinvest in capability building, and fail to redefine roles and decision rights. Tools get deployed, but behavior doesn’t shift.”

3) Cooperate tightly with others. Ewa Zborowska, research director at IDC, told Samuels, “Tight cooperation between the IT department and lines of business is crucial to achieving value-generating goals.” BCG analysts explain what happens when there is no cooperation. They write, “AI sharpens the individual silos without closing the gaps between them — and that is where much of the value disappears.”

4) Let people who use it tell brilliant stories. No system or solution will be of any value if it’s not used. To ensure employees use AI tools, it is important that users get a chance to test and refine the tools. Richard Corbridge, CIO at Segro, told Samuels, “Let people who want to try AI get their hands on it, so they can start to have a look, and they become the biggest fans out there, who've had a crack and can talk to others about their experiences.”

5) Embrace the watercooler chat. If AI solutions are implemented correctly and used consistently, they may create an extra bit of time for users. Let them enjoy it. Many innovations and process improvements have found their genesis during informal chats around the watercooler.

Concluding Thoughts

Barbara Wixom, a principal research scientist at MIT, told Stackpole, “Organizations are failing to recognize that there are two types of generative AI: tools that enhance personal productivity, and tailored solutions used to achieve strategic business goals.” In the long run, solutions that help achieve strategic business goals will prove to have the greatest value. BCG analysts conclude, “The implications are clear. Companies that rethink how corporate functions perform will gain speed, control, and efficiency that traditional models cannot match. More importantly, they determine how effectively and flexibly the company operates and competes.”

Footnotes

[1] Mark Samuels, “What is digital transformation? Everything you need to know about how technology is changing business,” ZD Net, 13 May 2026

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

[3] Matthew Marchingo, Emmanuel Sissimatos, Kevin Kelley, Pragya Maini, and Tarun Kajeepeta, “Corporate Functions of the Future Won't Look Like Functions at All,” Boston Consulting Group, 11 May 2026.

[4] Robert J. Bowman, “A New Report Reveals Why So Many AI Projects Are Failing to Launch in Supply Chains,” SupplyChainBrain, 11 May 2026.

[5] Mark Samuels, “Forget productivity: Here are 5 strategic shifts that drive real AI value,” ZD Net, 1 May 2026.