Demand for AI-enabled go-to-market platforms is surging, but the frameworks consultants use to assess them are lagging behind, warns Teresa Allan, founder and managing partner of Magnus Consulting. Without a clear way to classify these suppliers, businesses risk signing contracts that lock them into the wrong commercial model, with expensive consequences.
The classification problem
AI-enabled GTM suppliers all sound the same. Their marketing promises AI-powered, outcome-led, integrated solutions. Beneath that language, however, sit four distinct commercial models: licensed software tools, human-delivered services wrapped in a tech interface, managed services where the supplier runs a functional domain, and hybrid blends that often aren't disclosed clearly to buyers.
"These are not variations on a theme," Allan said. "They are fundamentally different commercial commitments, with different accountability structures, different IP implications and categorically different exit positions." Getting the category wrong at the start means every downstream decision - contract structure, governance, performance expectations - inherits the error.
Why this lands on the consultant's desk
Management consultants face this issue from two directions. First, clients increasingly ask them to evaluate AI-enabled GTM tools as part of commercial transformation, procurement strategy, or AI adoption work. Most firms lack a standard methodology for these assessments, leaving consultants to rely on frameworks built for agency procurement or SaaS licensing - neither of which fits.
Second, consultancies are buyers themselves. As firms build out their own growth infrastructure, they encounter the same market. Knowing how to classify and govern what you're buying is as relevant to a firm's own commercial decisions as it is to client advisory work. For consultants building this expertise, an AI Learning Path for Management Consultants can provide structured guidance on evaluating and deploying AI in commercial contexts.
Three tests that classify any supplier in 20 minutes
Allan's research identified three questions that quickly reveal a supplier's true model. The operating model test asks what the supplier's team actually does each week: are they operating a function on your behalf, or handing you tools to run yourself? The output ownership test examines what you take with you if the relationship ends - who owns the audiences, models, content and pipeline intelligence built during the engagement?
The governance test looks at how delivery is measured. Is there a performance standard the supplier is accountable to, or just a set of deliverables? The answers dictate how the contract should be structured, which IP and portability provisions matter, and what the exit architecture needs to look like before signature.
A core competency, not a specialist skill
AI-enabled GTM is becoming mainstream as adoption accelerates across commercial functions. The speed of adoption is outpacing the buying frameworks that should govern it. For management consultants, classifying these models is fast becoming a core competency - not because the technology is complex, but because the commercial architecture is novel and misclassification costs are rarely recoverable.
Why this matters for management
Leaders evaluating AI-enabled GTM investments need to recognize that supplier selection is not a technology decision. It is a commercial architecture decision with long-term operational consequences. The frameworks used for traditional agency or SaaS procurement won't work. Building internal capability to classify suppliers - or hiring advisers who can - protects against contracts that look identical on the surface but deliver fundamentally different outcomes. For teams embedding AI into broader management strategy, resources like AI for Management offer practical entry points to strengthen that capability.
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