Editor’s note: The following is a guest post from Binny Gill, founder and CEO at agentic automation company Kognitos.
The AI market is still largely focused on infrastructure as capital spending conversations are dominated by chips, data centers, models, compute capacity, and the enormous capital required to support them. That makes sense while we’re in the buildout phase. Goldman Sachs has estimated that the AI buildout could require trillions of dollars in capital between 2026 and 2031 across compute, data centers and electricity.
But that is not where AI becomes real for most customers. For a CFO, the test is not the size of the model or the scale of the infrastructure behind it. It is about whether forecasting is more accurate and whether the finance team handles less manual work. That is where I believe the channel has a much larger role to play than many people currently recognize. AI prevalence in the modern enterprise makes the channel’s judgment more important than ever.
What outcomes-as-a-service looks like in practice
That judgment starts with understanding the core processes. A finance team may need help identifying where cash application slows, where disputes linger or why the end of the quarter depends on temporary staff and manual checks. Meanwhile, in procurement, common challenges include supplier onboarding, missing documents and approvals that pass through too many hands before anyone notices they are stuck.
These may not be the high-profile use cases highlighted at AI conferences, but they often deliver the earliest enterprise value. The most impactful AI applications are found in accounts payable, accounts receivable, shared services, customer operations, supply chain, healthcare administration and back-office workflows, where minor delays can quickly escalate into high costs and attract management attention.
The Century Supply Chain Solutions logistics team, for example, was handling bills of lading in multiple formats and languages, as well as carrier bookings that generated tens of thousands of emails each month. After applying AI-powered automation, the company, a Kognitos customer, reportedly automated more than 50,000 bills of lading and carrier bookings per month, eliminated 98% of manual entry, auto-resolved 90% of exceptions, and saved $50,000 per month.
The channel as an AI traffic controller
This shift creates a new opportunity for the channel. Leading partners establish their value early by asking insightful questions about processes, risks and desired outcomes before any product selection.
That is why I think the channel will increasingly serve as the AI traffic controller for enterprises. Customers face a complex landscape of vendors, models, agents, copilots, integrations, and promises. Like a traffic controller, the channel provides judgment, sequencing and safety, determining what can proceed, what should wait, what requires approval, and what should be held back.
Top channel partners apply this discipline to AI by guiding customers on where to begin, which workflows to defer and which use cases pose excessive compliance or operational risk without robust controls. Their responsibilities include testing workflows before deployment, validating integrations, stress-testing exception handling and embedding governance from the outset.
This also means partners need to move beyond the language of resale. The role can no longer be to recommend a tool and move on. Instead, they help define outcomes, implement workflows, monitor performance, and drive continuous improvement as processes encounter real business variation.
Enterprise work rarely follows the clean path imagined during process design. The same workflow may run smoothly for weeks before a supplier changes a document format, a customer makes a partial payment or a team discovers that an invoice matches the purchase order in every respect except the one field that now requires judgment. For AI to be useful in that environment, it has to handle exceptions in a way people can understand, correct and trust the next time they appear.
Traditional automation often breaks at those edges. AI only helps in that environment when it is designed for execution, traceability and human control. In an enterprise setting, it is not enough for AI to generate a plausible answer. If it touches billing, compliance, finance or customer data, the business needs to know exactly what it did and why.
In my view, enterprise AI becomes useful at scale only when those who understand the work can also comprehend, question and guide the automation. Many companies are now facing this trust gap as they transition AI from pilot projects to daily operations.
Where partners earn trust
This is where partners can make AI useful. A finance partner, for example, can help a customer translate a broad goal like “use AI in accounts receivable” into a specific operating target: reducing manual remittance matching, routing disputes more consistently and showing managers where cash application is getting stuck.
Customers will not judge AI investments by how advanced the underlying model sounds, but by whether the work is more reliable, whether people can trust the system and whether the business can explain the result when asked. That requires technology, but it also requires judgment and people who understand the customer’s systems, incentives, constraints and risk tolerance.
The channel already has that proximity. In many cases, partners know the customer’s operating environment better than vendors. They know which ERP system is outdated but essential, who holds actual decision-making authority which integrations are fragile, and which reporting metrics matter most to executives.
The AI economy may be built on infrastructure, but enterprise adoption will be won in the process layer. Companies spending heavily on hardware and models will lay the foundation. The channel will help determine whether that foundation leads to cleaner operations, faster decisions, and measurable business outcomes.
