Any company can get an AI pilot off the ground. A small initiative, a few tools explored, promising results, and then… nothing. A pilot that’s successful ends up languishing in isolation while the rest of the company continues to do what it’s always done. This happens time and time again in a phenomenon that’s not related to failed technology or poor pilot design. Getting a pilot off the ground requires different skills, mindsets, and team involvement than scaling AI across an enterprise.
Pilots are run in controlled environments with controlled teams and with a limited scope and participants who volunteered to be involved because they’re already predisposed to new technology. Scaling means forcing AI on teams who neither asked for it nor volunteered to be engaged with new technology. It means legacy systems, it means integration challenges, budget conversations, and most importantly, recognizing that what’s good for five may break at 50.
Why the Pilot Gets Stuck
The chasm between pilot and production is not usually technical. Companies can assess whether the technology is available and functional during the pilot phase. What they cannot assess is whether their company can adjust to such changes. There’s often no clear owner of the scaling process; the IT department was in charge of the pilot, but scaling involves sales, operations, customer service, finance—all departments that are now intermingled, and no one is truly sure how much it will take to create order.
Then there’s budgeting. Pilot programs are often run on a shoestring or discretionary budget. Scaling requires real budgets with competing priorities; someone has to advocate for the budget to be redistributed or at least champion why this initiative warrants spending money where others have not. Why does this need leadership approval when others do not? The tech team is usually ill-equipped to make that internal sales pitch.
Then there’s workflow. If the pilot worked, the team likely adjusted its workflow to work for the AI, not vice versa. Well, now a company wants departments that have been doing their job for years to adjust so that AI can be utilized? They see this as another layer of bureaucracy that will inhibit their day-to-day tasks and create time wasting endeavors that ultimately won’t work anyway. AI has to fit into existing work patterns which require customization and integration efforts not explicitly called for during the pilot phase.
What Happens When Experts Get Involved
This is where working with AI experts changes the game because they’ve seen this exact scenario so many times before and know what breaks when companies try to scale—and how to avoid those pitfalls. It’s not about knowing better technology than the in-house team; it’s about knowing organizational dynamics that speak to whether adoption even occurs.
For example, a consultant will get in there and assess what’s going on beyond what’s perceived as going on. They’ll talk to people in different departments to assess from where pushback will come and why. All too often, those responses are valid—the technology might make their lives more difficult or it hasn’t been assessed against edge cases that happen regularly in practice—but whatever the case may be, it’s best to air these grievances earlier so corrective measures can be put into place before rollout instead of delaying it once it’s underway.
Consultants will also bring structure to scaling; who needs to approve what, when, who adopts first and who needs to hold off—and how can each stage be measured if it’s working? These are basic questions that go unasked because everyone is so busy trying to focus on the technical aspects—but without proper coordination, it’s all for naught.
Building Internal Capability
For example, a consultant who does this properly with intentions beyond making themselves permanently needed knows that they’re building internal capability so the company can manage AI on its own after initial scaling is complete. This requires workshops, documentation, and knowledge transfer during the project—not after—for all expected learning outcomes to remain retained.
This occurs through proper workshops—not just a general overview of AI but specific use cases relevant to each department—where customer service learns how AI helps with their customer woes, where sales sees how it applies to their pipeline management efforts. This direct correlation ensures retention and utilization in regular work life.
The best consultative projects will also instill internal champions who understand both the technology as well as the culture well enough that they can continue this champion mentality after external help is done. These champions need mentoring and support along the way to assume that role because it’s distinctly different from what they generally do; when new use cases emerge or questions arise, this internal champion becomes the go-to resource.
Making the Economics Work
Scaling AI should improve economics—not just preserve them—and consultants help companies measure the right metrics for value attribution. It’s not always a direct correlation; some costs are more intangible than others; an improved speed to customer response can certainly be tracked over time but better decision making because people have good information has less malleability but can be more useful and valuable.
The ROI needs to account for both, but also consider costs—not just technological costs but time effort from teams and potential productivity dips in transition as well as maintenance thereafter. If companies short-change these efforts, they will inevitably be surprised later which jeopardizes confidence in a much-needed solution in multiple areas of operation.
When the economics are determined and the organizational dynamics assessed, scaling gets less intimidating; teams know what’s expected of them, leadership knows what’s involved from a budgeting perspective and there’s practical realism for how one gets from pilot to production.
