If AI is ready, what’s holding your operations back?
Driving operational performance with AI starts by redesigning how work gets done
Key takeaways
- AI is finally operationally relevant; not as a future promise or centralized analytics capability, but in the field, at the asset, and alongside operators and engineers. The core challenges operations leaders face every day, whether improving safety, increasing throughput, protecting revenue, or managing operational risk, are now within reach.
- The opportunity is not simply to deploy AI, but to rethink how work is designed. Operations leaders must navigate two distinct shifts: doing today’s work differently by augmenting human judgement with AI and doing different work by reimagining decisions and workflows that were previously not possible. Sustainable value comes from embracing both.
- Scale follows value, not data perfection. Operations leaders who succeed anchor AI to business outcomes from the start, defining value upfront to guide every decision about where AI is applied. Data investment follows the same logic: measure value rigorously and strengthen the data where it unlocks results.
Asset‑intensive industries have invested in advanced operational technologies for decades to support field and control room decisions. The next wave of value requires a different approach, not more of the same.
Why? Because most initiatives have focused on deploying technology onto existing processes instead of redesigning how work gets done and decisions are made. As a result, value creation has been hard to measure, executive sponsorship has eroded over time, and many operations leaders remain justifiably skeptical over investments that promised more than they delivered.
What’s different now is that AI can directly support frontline decisions as work unfolds by connecting signals across siloed systems, surfacing leading indicators earlier, recommending actions, and, when authorized, triggering interventions in time to matter. This is where agentic AI has the chance to deliver: not just reporting what happened, but augmenting judgment and intervening before the moment passes.
For operations leaders, AI is neither a platform nor a model. Rather, it’s a set of capabilities that puts insight directly into the hands of operators and engineers, translates fragmented data into practical guidance, and enables teams to rethink what’s possible.
This distinction matters. AI doesn’t fall short because technology itself is inadequate. It falls short when it’s treated as a separate, often IT-led initiative, instead of a business-led change to everyday work, where adoption, accountability, and value take shape.
Accelerators vs. blockers: Four factors to consider
Your next moves: How to make AI work for your operations and scale it
For most COOs, the question shouldn’t be, “Where can we pilot AI?” Rather, leaders must ask: “Where do we need a better outcome, and what work will we redesign to deliver it?” Here is a practical path that aligns to how operations leaders work:
Balance your quick wins and bigger bets with disciplined value measurement
Many AI initiatives fail not because the technology falls short, but because value was never clearly defined or measured. The organizations who scale successfully start differently: they define the outcome upfront, identify the right metrics, establish a baseline, and commit to measuring impact against a clear timeline. That discipline is what separates AI proof-of-concepts from initiatives that scale.
To apply that discipline, adopt a three-track approach, and across all three, be explicit about the outcome, who owns it, and how value will be measured, both qualitatively and quantitatively.
- Quick wins: Put AI tools directly into the hands of operators, engineers, and supervisors to remove manual effort, explore data, and test hypotheses through daily use. Track the value delivered from the start, including time saved, risk reduced, and decisions improved. These wins build trust and create the evidence base that determines where to invest. What gets measured here is what gets invested in next.
- Foundational investments: Strengthen data, integration, and governance where repeatable outcomes have been demonstrated. Over time, AI embedded into everyday work improves asset data quality naturally, revealing gaps, enriching data through use, and focusing improvements where they have the greatest impact.
- Bigger bets: These take longer but they are where real advantages are built. Commit to one or two targeted bets tied to meaningful outcomes, such as revenue growth or cost reduction, and follow through with conviction. Define how each bet is expected to influence the outcome, identify the value levers that matter, and track progress with discipline. In many cases, this requires redefining KPIs so teams are empowered to work differently and make better decisions, rather than being constrained by legacy performance models.
Redesign the work, not just the tool
Start with the outcome you want to improve—whether that's safety, throughput, or cost—then challenge how work flows today. Look closely at how decisions are made, how information moves, and where human judgment matters most. This clarity determines what to automate, what to augment, and where AI should only advise.
Key questions to ask yourself today:
- Which operational decision or process, if improved once, would benefit multiple teams, assets, or sites?
- Which operational process consumes the most effort or cost today?
- Where do we have the greatest potential to unlock revenue or performance gains?
- Where are we most exposed because insights arrive too late or without sufficient context?
- Which parts of our operations would fail first if we lost our most experienced operators or supervisors tomorrow, and what impact or risks would that create?
AI-enabled operations are already taking shape as leaders rethink how work gets done, equip their workforce with better insight, and invest intentionally in the data that matters. These questions help identify where redesigning work can deliver the greatest operational impact.
