AI forces companies to relearn what ‘value’ means, cut low-level work for humans
What you’ll learn:
- This moment isn't just about automation or productivity gains. It's about a structural realignment.
- For the first time in business history, intelligence is not scarce. AI can analyze vast datasets in mere seconds, as just one example.
- AI isn't just improving productivity. It's correcting market distortion.
Enterprise AI adoption is accelerating. Budgets are being reallocated. Roadmaps are being rewritten. Boards are demanding measurable outcomes from these adoptions. But beneath the excitement lies an uncomfortable truth: For decades, most companies have drifted away from value and toward process. AI is now forcing corrections, however.
This moment isn't just about automation or productivity gains. It's about a structural realignment in how businesses invest, design workflows, and define human contribution in the value chain.
Three shifts are emerging that will determine who captures real ROI from AI—and who layers new tools onto old inefficiencies, hampering AI’s maximum capabilities or failing at adoption entirely.
Process-centric to value-centric operations
Modern enterprises are saturated with process. Approval chains. Reporting layers. Manual reconciliations. Integration workarounds. Escalation protocols. Specialized review committees. These structures weren't irrational—they were responses to scarcity.
Information was hard to access. Expertise was concentrated. Analysis took time. And human intelligence was the bottleneck, so organizations built process scaffolding to manage risk and coordinate decision-making.
Over time, the scaffolding became the structure. Instead of asking, "What creates value?" organizations began asking, "How do we optimize this process?" The result was incremental improvement layered onto increasingly complex systems.
AI disrupts this model because it eliminates the original constraint.
For the first time in business history, intelligence is not scarce. AI can analyze vast datasets in mere seconds, simulate scenarios across thousands of variables, identify anomalies, generate forecasts, draft contracts, synthesize research, and surface probabilistic insights—continuously.
When intelligence becomes abundant and processes become automated, many intermediate steps collapse. The critical question shifts from “How do we automate this process?” to “If intelligence is effectively unlimited, what does this process need to look like at all?”
Over time, the scaffolding became the structure. Instead of asking, "What creates value?" organizations began asking, "How do we optimize this process?"
Can we work with the end in mind—from A directly to Z and skip everything in between? This focus on ultimate value drives a complete reimagining of processes and, ultimately, operating models: people, process, and technology. This is where companies begin to see structural advantage.
In supply chain environments, root cause analyses that once required weeks of cross-functional coordination can now be completed in minutes. Strategic sourcing cycles shrink dramatically when AI handles scenario modeling. Inventory optimization becomes dynamic rather than periodic.
The breakthrough isn't speed alone—it's simplification. AI exposes which activities directly create value and which exist merely to manage information scarcity. Companies that redesign around value eliminate layers. Companies that don't simply accelerate inefficiency.
AI is correcting a longstanding market misalignment
For years, executives have emphasized investment in technology. Yet financial reality tells a different story.
Historically, enterprises have spent more on services than on technology itself. Consulting, implementation support, customization, integration, and change management have absorbed the majority of enterprise investment. Why? Because traditional technology required human-heavy services to configure, integrate, and sustain it.
This created a structural imbalance: Perceived value was attributed to technology, while actual investment flowed disproportionately to services.
AI changes the economics. Low-code platforms, AI-assisted configuration, autonomous analytics, and intelligent orchestration significantly reduce the need for extended implementation cycles and ongoing services overhead.
Services don't disappear—they shift toward targeted enablement rather than long-term dependency. The result is a tightening alignment between perceived value and actual investment. Technology begins to drive outcomes more directly.
See also: IFS debuts package of ‘digital workers’ in next iteration of agentic AI industrial software
AI isn't just improving productivity. It's correcting market distortion.
The human repositioning in the value chain
As AI removes many routine cognitive tasks, the human role doesn't disappear—it elevates. When intelligence is abundant, judgment becomes scarce.
Humans shift their roles toward defining strategic objectives and constraints, framing trade-offs and risk exposure, aligning stakeholders, ensuring ethical governance, and designing systems rather than executing steps.
Companies adopting AI that redesign around value eliminate layers. Companies that don't simply accelerate inefficiency.
The defining question for professionals becomes: Where do I create value in a system where intelligence is ubiquitous?
Organizations that answer this clearly unlock new leverage. Those that cling to task-based role definitions risk automating complexity rather than eliminating it.
AI is not merely a technology wave. It’s a forcing function—pushing companies back to first principles. If intelligence is abundant and capacity is effectively limitless, the only durable differentiator is clarity of value creation.
The companies that win in this next era won't be those that deploy the most AI tools. The winners will be ones that use AI to cut what no longer creates value and redesign around what does.
About the Author
Avik Ghosh
Avik Ghosh is managing director, head of AI and product innovation, at Maine Pointe, a global supply chain and operations consulting firm. He leads efforts to “productize” solutions and drive measurable business outcomes through AI and digital innovation and specializes in translating AI and data into pragmatic, high-impact enterprise solutions.