AI is no longer something business leaders can afford to ignore. From generative AI and intelligent automation to predictive analytics and decision support, new capabilities are changing how organizations work and compete.
But I think one question deserves more attention: Are we adopting AI because the technology is available, or because it can genuinely make our business better?
A successful pilot is encouraging. AI training is useful. A sophisticated model can be impressive. None of these automatically translates into better business performance. For me, the real test is whether AI improves operations, decisions, risk management and business value.
Start With the Business, Not the Technology
Whenever I consider an AI opportunity, I prefer to begin with a business conversation. What is taking too long? Where are we losing efficiency? Which decisions lack reliable information? What risks are becoming harder to manage? Only after identifying the problem should we determine whether AI is the right solution.
I often connect the discussion to three business priorities: Profit—productivity, quality, revenue and efficiency; Cash Flow—faster cycles, better inventory decisions and fewer unnecessary expenses; and Risk—stronger compliance, sounder decisions and fewer avoidable failures. Not every initiative addresses all three. The point is to know the outcome that matters and why.
My Five-Pillar Approach
I use five connected questions to structure conversations about responsible AI adoption and business transformation.
Business Value
Why are we doing this?Define the business problem, expected value and strategic fit before investing.
Organizational Readiness
Are we prepared?Check leadership, people, processes, data and change readiness. Address key gaps.
Responsible Governance
What must we control?Set accountability, data safeguards, human review and risk-based controls.
Disciplined Execution
How will we make it work?Assign an owner, prepare the team and test one focused use case.
Measurable Outcomes
What has improved?Measure quality, speed, cost and risk against a clear baseline.
Leadership Still Matters
As AI becomes more capable, the way we work will change. The need for sound leadership will not disappear. Someone still has to set priorities, allocate resources, develop people, challenge assumptions and remain accountable for results.
My experience in Human Capital leadership and responsible operations has taught me that even strong strategies struggle without alignment, capability and execution discipline. I approach AI strategy as a business leadership responsibility supported by technology expertise—not a technology initiative that executives simply observe.
From Knowing AI to Making AI Work
I believe organizations should be ambitious about AI while being thoughtful about how they pursue it. Starting small is sensible when the objective is clear. Governance should support confident innovation, not unnecessary bureaucracy. Investing in people is part of AI transformation, not separate from it.
Begin with business value. Understand organizational readiness. Build responsible governance. Execute with discipline. Measure what matters.
Instead of asking only, “How can we use AI?”, I encourage leaders to ask: “How can we use AI responsibly to make our business perform better?”
What This Could Look Like in Manufacturing
Consider a manufacturer facing recurring quality problems, production delays and inconsistent access to operating procedures. Intelligent knowledge retrieval, predictive analytics or computer vision might help. I would not begin by choosing the technology.
I would first identify where performance is being lost and what meaningful improvement looks like. Next come process and data readiness, supervisor and employee capability, and clear rules for reviewing AI recommendations. A suitable solution can then be piloted in a defined workflow against an agreed baseline.
If the evidence supports the investment and risks are manageable, scaling becomes a business decision grounded in results. The goal is not to prove that AI works. It is to prove that the business works better with AI.