AI has the potential to meaningfully elevate productivity, but only when leaders rethink what productivity truly means through an AI-first mindset. When organizations treat AI as a transformation of how work happens rather than another tool to bolt-on, they create the conditions for meaningful, sustained impact. That impact emerges when clear goals, strong data foundations and human judgment work in concert with machine intelligence, allowing each to strengthen the other and elevate performance. “Every dollar of spend must tie back to a measurable outcome,” says Corey Coto, an Operating Advisor at Fauntleroy Partners, where he helps leaders connect AI investments to concrete business results and operating mechanisms like spend-to-outcomes and zero-based budgeting. Without that discipline, he says, companies risk chasing AI hype instead of business value.
Coto has spent more than two decades working at the intersection of engineering, executive strategy and enterprise transformation. “I’ve always worked with one foot in the code base and one foot in the board deck,” Coto says. That blend of perspectives helps explain why he approaches AI as a structural shift in how organizations operate. It’s from this vantage point that he outlines the fundamentals enterprises must get right before AI can deliver measurable, scalable productivity gains.
Why AI Fails in the Enterprise
Many leaders see AI as a plug-and-play solution for productivity, however, the real barriers to successful transformation tend to be structural over technical. “You get AI theater instead of value when you treat it like a bolt-on feature,” he says. The first challenge is treating AI as a shift in how the enterprise operates, not an add on. Buying copilot licenses or adding a chatbot does not constitute a strategy. Without changes to incentives, workflows and metrics, these tools simply crowd an already cluttered ecosystem.
The second barrier is weak foundations. Generative AI, for example, is exceptionally good at producing confident, coherent output. That means poor data quality and ambiguous processes risk being amplified rather than addressed. “If your data is inconsistent or you lack strong observability, you end up automating the wrong things with great efficiency,” he says. The final barrier is trust. Employees often wonder whether AI is designed to replace them or support them, and leaders sometimes frame AI solely as a cost-cutting tool, which erodes adoption. Productivity, Coto emphasizes, is ultimately a human story. The most successful AI efforts involve frontline teams co designing solutions instead of being handed new tools with little context.
Strategies That Actually Move the Needle
Coto believes three principles separate successful AI programs from the rest, each one directly addressing the barriers that often derail enterprise AI. These strategies help move organizations from “AI theater to real operational impact” by grounding adoption in clarity, workflow integration and disciplined execution.
1. Start with a specific job to be done and a measurable metric
Rather than setting broad goals about boosting productivity, focus on clear, tactical outcomes such as reducing customer proposal preparation time by forty percent or cutting vulnerability resolution time by sixty percent. Precision creates accountability and sharpens decision making.
2. Embed AI directly into existing workflows
The most effective AI systems behave like capable teammates inside the tools employees already use. This can include auto-drafted status updates from telemetry data, next step summaries from customer calls or suggested test cases surfaced directly within an integrated development environment. “AI should meet people where they already are, not ask them to adopt another destination.”
2. Treat AI like a product, not a project
Successful AI programs require cross-functional teams, continuous discovery and clear ownership. They also depend on consistent operating rhythms, including weekly reviews, business readouts and ongoing reallocation of resources based on measured value. Managed with true product discipline, AI becomes a compounding force rather than a one time initiative.
The Next Three Years
Looking ahead, Coto sees enterprise AI evolving quickly, and the shifts on the horizon only underscore why the earlier strategies matter. The first major shift is the transition from copilots to workflow agents capable of orchestrating entire sequences of tasks across multiple systems. These agents will take informed actions, not just provide suggestions, raising new questions about reliability and accountability.
The second shift involves organization-tuned models trained on a company’s language, knowledge and history. A generic LLM can be useful, but Coto believes tailored models will become essential. He envisions every engineer, seller or support representative having a digital colleague who understands their portfolio, systems and past decisions. The third shift mirrors the rise of reliability engineering in software. Enterprises will define AI-specific service levels, evaluation pipelines and cost per decision as a managed metric. Companies with strong observability and platform engineering foundations will have a distinct advantage.
These emerging shifts reinforce the strategies outlined earlier. As AI moves from copilots to workflow agents and organization-tuned models, companies grounded in clear metrics, workflow integration and real product discipline will capture the greatest gains. The next era of enterprise AI will favor organizations that already have the muscle to deploy, measure and govern these systems with rigor. “AI is here to give your best people unfair advantages,” he says. The real breakthroughs happen when human insight and machine intelligence reinforce one another through clear goals, clean data and thoughtful guardrails. Organizations that learn to blend technology, incentives and culture will be the ones that redefine what productivity looks like in the decade ahead.
Connect with Corey Coto on LinkedIn or visit his website for more insights.
