For years, the narrative surrounding artificial intelligence in the enterprise has been consistent: companies struggle to absorb new technology, so vendors must step in and do it for them. This logic has driven a massive wave of forward-deployed engineering teams sent by leading AI labs and cloud providers. The promise was that these experts would bridge the gap between theoretical model capabilities and practical business application.<\/p>\n
However, a critical re-evaluation of this strategy suggests that the industry may be solving the wrong problem. While technical proficiency is high, the conversion rate from pilot to production remains stubbornly low, and the financial impact on many organizations is negligible. The core issue is not a lack of technical skill, but a fundamental misalignment between what vendors deliver and what businesses actually need to survive and grow.<\/p>\n
Research from MIT indicates that approximately 95% of enterprise AI pilots produce no measurable profit impact, despite tens of billions of dollars being spent on these initiatives. This statistic highlights a structural failure in how AI projects are scoped and executed. The industry often celebrates “winning the benchmark” as a victory, but winning a technical benchmark is not the same as winning a commercial deal or creating sustainable value.<\/p>\n
A customer who never gets a solution into production will never renew their contract. Yet, the current engagement model incentivizes short-term wins over long-term integration. Forward-deployed functions, which sit on the commercial side of vendor organizations rather than in research, are measured by the number of pilots converted to production within a single quarter. Against this scoreboard, the models perform optimally. But this metric masks a deeper reality: many of these “successful” deployments fail to change operational decisions in any meaningful way.<\/p>\n
Consider an intelligent demand-forecasting agent built by a vendor team. The engineering readout might show that accuracy improves by double digits. On paper, this is a genuine success. However, if this improved accuracy does not translate into better operational decisions\u2014such as adjusting safety stock levels, renegotiating supplier commitments, or changing production schedules\u2014the project has freed zero cash.<\/p>\n
In fact, it can make things worse. If a model is tuned primarily to optimize fill rates because that is what the commercial team requested, inventory levels may climb unnecessarily. This suffocates working capital. In supply chain management, inventory is simply cash sitting on a shelf. Every forecast is a claim on that cash, and every service-level promise decides how much liquidity is locked away. The true value of AI lies not in optimizing a single, isolated number, but in managing the complex trade-offs between accuracy, cost, and liquidity.<\/p>\n
Outside engineers, no matter how brilliant, cannot own these trade-offs. Their engagements are typically sold on deflected tickets, automated records requests, or closed underwriting cases. These are local, siloed outcomes within a single function, measurable in weeks. They are the only types of results an outsider can deliver before rolling off to the next client logo.<\/p>\n
The results that a Chief Financial Officer (CFO) truly cares about cross multiple functions and often do not appear in financial statements until two quarters later. When an engagement ends, the immediate question should be whether the organization is better equipped to find and solve the next problem independently. If the capability leaves the building when the outside engineer packs up their laptop, that was delivery, not diffusion.<\/p>\n
Delivery is a fine business model, but it does not compound. Diffusion, however, is compounding. It requires redesigning planning architectures from the ground up, staffed by operators who have spent years inside the specific supply chain or business unit. These individuals know where the real constraints sit\u2014the lead times, the allocation rules, and the supplier commitments that no outsider would have known to model.<\/p>\n
When an initiative is staffed with internal domain experts who apply AI directly to these decision points, the results differ significantly. Inventory can come down and stay down, turns can improve, and crucially, nobody needs to be flown in to repeat the process the following quarter. There is no version of that outcome a single outside engineer could have designed, regardless of the sophistication of the model in their hands.<\/p>\n
This distinction between delivery and diffusion is the test for any AI engagement. Are we building a one-off solution, or are we building internal capability? The former creates dependency; the latter creates resilience and competitive advantage.<\/p>\n
Scale this insight up, and the macro picture becomes clear. A country\u2019s AI advantage will not come from a few hundred flagship deployments managed by external consultants. It will come from millions of operators applying AI to operational decisions they were previously making poorly. We are currently spending millions sending AI experts to learn business operations. We should be sending business operators to learn AI.<\/p>\n
We are running the harder transfer at immense cost and mistaking it for progress. The easy path is to hire someone who knows the model cold. They can tell you where it will break, what it will cost, and which benchmark numbers to ignore. But knowing the model is not the same as knowing your business. The real issue is that the people sent to help are often making the exact mistake they are being paid to fix: focusing on technical implementation rather than operational integration.<\/p>\n
For business leaders deciding how to proceed with AI, the message is clear. Stop looking for more forward-deployed engineers. Start looking for backward-deployed domain experts. These are the people already on your payroll who understand the nuances of your specific market, your supply chain, and your financial constraints.<\/p>\n
Before signing another vendor contract, ask yourself: who owns the trade-offs? Who understands the cash flow implications of the decisions being optimized? If the answer is an external consultant, you are likely buying delivery, not diffusion. If the answer is your internal team, supported by AI tools, you are building compounding capability.<\/p>\n
Nobody is selling backward-deployed domain experts because they are not a product; they are a workforce development challenge. Which means the only people who can build them are already within your organization. The task is not to import intelligence, but to amplify the existing knowledge of those who live the business every day.<\/p>\n