Generative AI has taken enterprises by storm — promising efficiency, creativity, and decision-making at machine speed. But for Shammik Gupta, CEO of 3-Cubed, the real challenge isn’t in acceleration — it’s in alignment.
“Technology doesn’t fix business problems,” he says. “Leadership judgment does. Accelerate without clarity, and you just reach chaos faster.”
For Gupta, GenAI is not a silver bullet but a new kind of employee — “a brilliant PhD intern with full system access on day one.” His analogy is simple yet sharp: “Like a puppy, GenAI is delightful — but without early training, it will chew up your processes with the same enthusiasm it used to help.”
That’s why, he argues, enterprises must move beyond the excitement of automation to the discipline of orchestration. The conversation around GenAI has shifted — from doing and dreaming to deciding.
From Automation to Decision Intelligence
Most organizations, Gupta observes, are stuck in the first two phases of AI adoption. The doers automate tasks — drafting reports, creating summaries, or coding workflows. The dreamers imagine fully autonomous enterprises where AI manages everything from procurement to payments.
But the real opportunity lies in the deciding phase — where technology, people, and process move in rhythm. “Decisions don’t happen in code,” Gupta notes. “They happen at the intersection of context and judgment.”
He recounts the case of a global retail bank that used separate AI systems across marketing, operations, and finance. Each worked well in isolation, yet the business stalled. “Marketing’s AI drove demand, but fulfillment wasn’t ready. Finance adjusted pricing just as customers were ready to convert. They were doing and dreaming — but not deciding together.”
When the bank adopted 3-Cubed’s decision-intelligence framework — aligning GenAI models across functions — performance transformed. “Execution became fluid. Campaigns synced with capacity. Pricing reflected real-time sentiment. Customers felt the difference.”
Governance as a Leadership Discipline
For Gupta, governance is not a policy binder — it’s a living framework for how decisions stay connected. At 3-Cubed, his team extends the traditional “people, process, technology” model into PROFIT — Process, Risk, Operations, Finance, IT, and Talent.
“It’s a way to ensure every change echoes responsibly,” he explains. “Change a process, and risk shifts. Add too much control, and you slow the customer. Remove it, and exposure grows.”
Governance, he emphasizes, is the coordination layer that keeps organizations from silently drifting apart. It ensures every technological change is matched by a change in people and process — with IT connecting, not commanding.
Compliance Without Killing Innovation
In the rush to automate, many organizations overcorrect — turning compliance into a bottleneck. Gupta advocates for a lighter, real-world approach.
“Compliance works when it’s dynamic, not dogmatic,” he says. “Start with control objectives — what must go right — and risk taxonomy — where things can go wrong. Then design controls that evolve with technology.”
He cites a logistics client where AI cut routing costs but inadvertently raised compliance incidents. The solution wasn’t more code — it was human. “We added a five-minute driver check and a manual override rule. Efficiency stayed; accountability returned.”
The Future: Agentic AI Councils
Looking ahead, Gupta envisions a world of machine-mediated governance — what he calls Agentic AI Councils. Imagine a council of AI agents representing each function: Process, Risk, Operations, Finance, IT, and Talent. Each evaluates the impact of proposed changes before human leaders make a decision.
“This isn’t replacing leadership,” he clarifies. “It’s augmenting it. Humans still consent. But now, every decision is previewed across cost, control, and client outcomes before impact.”
It’s governance at digital speed — foresight, not hindsight.
Judgment at Scale
Ultimately, Gupta believes that the GenAI era demands more from leaders, not less. “Governance is not a compliance checkbox. It’s a leadership discipline,” he says. “When a regulator or board asks, ‘Why did the AI decide this?’ — you should answer not with code, but with clarity.”
That clarity, he adds, is what builds confidence — and confidence scales faster than any algorithm.
“The technology is real,” Gupta concludes. “But leadership still steers. AI may be the engine — yet without judgment, acceleration just spins in circles. Responsible AI isn’t about control; it’s about consent.”