As AI moves from pilots into production, Trace3 says the real test is no longer whether the technology works, but whether enterprises can define the right use cases, drive adoption, and prove business value.
IRVINE, Calif., July 15, 2026 /PRNewswire/ — Enterprise AI is running into a problem many organizations did not fully model during the pilot phase: value is not scaling at the same pace as adoption. BCG found that only 5% of companies worldwide are consistently generating substantial AI value, while 35% are beginning to scale AI and generate value, leaving most organizations still working to turn investment into measurable results. For Trace3, a leading IT and AI solutions provider, the next AI challenge is not whether the technology can work in a controlled environment, but whether organizations can profit once it reaches production.
“Technically, a lot of AI solutions work in the pilot phase,” said Ben Prescott, Head of AI Solutions at Trace3. “The harder part shows up when organizations have to connect AI to business impact, user adoption, and workflow change. The pilot-to-production gap is often something I call the first mile and last mile problem.”
The AI Rollout Is Not the Same as AI Readiness
Enterprise AI spend and deployment are accelerating. Recent CIO survey data from RBC Capital Markets, reported by Business Insider, found that more than half of companies already have AI in production, while another 35% expect to reach production within six months. Deloitte’s 2026 State of AI in the Enterprise report found that sanctioned AI tool access expanded from under 40% to about 60% of workers in one year, yet only 30% of organizations are redesigning key processes around adoption.
Prescott said that gap matters because production AI changes the conditions. In a pilot, the solution may be tested in a small lab, with limited users and a narrow definition of success. In production, the radius of impact expands. More employees are affected, more workflows are touched, and leadership expects the investment to show measurable return.
That is where many AI programs begin to strain. Companies often buy or build a tool, publish a training guide, hold a rollout session, and assume adoption will follow. But AI does not behave like traditional software deployment. It changes how people search, decide, create, analyze, and move work through a process.
The First Mile Is the Business Problem
The first mile of AI implementation starts before a tool is selected. Organizations need to define the business outcome they want to improve, identify where AI can realistically support the work, understand which processes will change, and decide how success will be measured. “Too many companies start with the tool instead of the business problem,” Prescott said. “There is no shortage of AI products. The question is what problem should the organization be solving, and how will it know whether AI improved the outcome?”
That discipline becomes important as companies evaluate generative AI, agentic AI, deterministic models, automation, and other AI-enabled systems. The point is not to force every workflow into the newest AI trend.
Some workflows require repeatable, deterministic outputs. Others can benefit from probabilistic models that support judgment, summarization, or flexible decision-making. When organizations apply the wrong model to the wrong problem, they can create inconsistent outputs, reduce trust, and weaken adoption. “The mistake is treating every AI like generative AI,” Prescott said. “Some work needs the same output every time. Other work allows for flexibility. It comes down to using the right tool for the job.”
The Last Mile Is Adoption and Value
The last mile begins after the tool is available. According to Prescott, organizations need a model for training users, collecting feedback, monitoring outcomes, measuring adoption, and refining the solution as business needs to change. Without that structure, an AI rollout may look successful on launch day but fail to become part of the operating rhythm of the business.
If users have a poor experience, do not trust the output, or do not understand where the tool fits into their work, adoption can spike early and then fall off. That makes ROI harder to prove and can damage confidence in future AI investments. It also creates a cycle where enterprises continue buying new tools without fixing the operating problem that caused earlier deployments to underperform.
Trace3’s consulting approach focuses on helping enterprises connect AI strategy to execution. That includes identifying business objectives, prioritizing use cases, mapping workflows, defining KPIs, selecting the right technical approach, and supporting ongoing adoption after rollout.
AI value does not come from deployment alone,” Prescott said. “It comes from making the right decisions before implementation and continuing to optimize long after launch. Organizations that succeed with AI understand that both the first mile and the last mile determine the outcome.
About Trace3:
In today’s fast-moving AI market, Trace3 stands out by pairing innovation with real-world implementation, with more than 20 years of experience helping enterprises turn emerging technology into measurable business value. The company provides technology solutions and consulting services across AI, data, cloud, cybersecurity, infrastructure, and digital transformation, helping organizations move from strategy to execution with clarity and scale. With elite engineering, deep innovation expertise, and the agility of a startup backed by the strength of a scalable enterprise, Trace3 helps clients put emerging technology to work in practical, outcome-driven way. All Possibilities Live in AI. For more information, visit www.trace3.com.
References
- Apotheker, J., Beauchene, V., de Bellefonds, N., Forth, P., Franke, M. R., Grebe, M., Kataeva, N., Kirvelä, S., Kleine, D., de Laubier, R., Lukic, V., Luther, A., Martin, M., Walters, J., & Schweizer, C. (2025, September 30). The widening AI value gap. Boston Consulting Group. bcg.com/publications/2025/are-you-generating-value-from-ai-the-widening-gap
- Barr, A. (2026, June 26). This new research challenges nearly every big AI narrative of 2026. Business Insider. businessinsider.com/enterprise-ai-spending-grows-openai-leads-rbc-reveals-2026-6
- Deloitte. (2026, January 21). From ambition to activation: Organizations stand at the untapped edge of AI’s potential, reveals Deloitte survey. deloitte.com/us/en/about/press-room/state-of-ai-report-2026.html
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