When I was at my old enterprise role, I was in charge of our marketing technology stack while also running a £100M/year marketing budget across EMEA, APAC and North America.
The first thing I did when I joined the company was a MarTech audit. As with every enterprise, there are always tech products that either outlived their purpose or were bought by previous teams but never retired.
After I straightened out our tech stack, I realised that we needed a Customer Data Platform. This technology allows you to use your own customer data to target on media buying platforms, so your budgets are spent effectively on the right audience. I reviewed three of them with my team, I being the champion and my team the end users.
The meeting I prepared for
We agreed on two vendors and went into the request for proposal (RFP) with the entire buying committee: economic buyer (CIO), champion (myself), end users (my team), legal, data privacy, Info Sec and procurement.
Our CIO wanted to know how effectively it would connect with our internal systems. His concern was how long the implementation would take and what resources from his team I would need.
Legal wanted to know about the data transfer. As a financial services software company, we process a lot of sensitive data. Our lawyers wanted to make sure we were complying with GDPR and CCPA, and that nothing sensitive was being transferred into the technology. Legal was also worried about excluding groups of customers from targeting. It is normal practice to exclude customers who are less likely to generate revenue from marketing campaigns, but legal considered this discrimination.
I realised nobody was asking for features, or how the tool worked. I was preparing for the wrong meeting.
Who holds the veto
Average enterprise buying committees are a staggering 22 people. The hidden figures like legal, data privacy, procurement and Info Sec veto 70% of all deals. They are far removed from the product. They are also extremely risk averse: fear of messing up. Nobody wants to jeopardise their reputation or their role by buying the wrong software.
Compared with five years ago, the C-suite is now involved in every deal above €25K. This wasn’t the case pre AI adoption.
AI created the paralysis and trust barrier. Buyers either want to make sure the vendor will still be active in three years’ time, or they won’t invest at all. They look at vendors not only for what they can do, but for what happens in contingencies when things go wrong.
Why the fear is rational
The trust fear is extremely rational. Enterprise buyers can clearly see the conflicts of interest between Big Tech and the regulators who are supposed to keep it in check. This oversight looks uncertain, and the risk falls completely onto the buyer. The EU AI Act, for instance, not only has very loose definitions of risk, but its high-risk categories now have a grace period of one year, which leaves most enterprises investing in AI more on edge.
What the pitch misses
The issue is the disconnect between what vendors are selling and what buyers are expecting.
Vendors sell capability and bells and whistles. I call this the Magic Narrative. Imagine you are sitting on a software demo call and all you hear is features, product and AI.
Buyers, however, expect vendors to de-risk these decisions. They hear something impressive but are no closer to deciding by looking at shiny features. Pragmatist buyers purchase a solved problem, and in a regulated firm that includes the risk of approving it. This is a big risk that vendors are mostly not aware of.
That is the AI paralysis we have on our hands at the moment. Decisions are being delayed until next year. The status quo becomes good enough. Sales cycles are getting longer. CAC payback is at an all-time high of 18 months. All of this stems from the risk aversion of buyers and vendors not being able to provide peace of mind.
What peace of mind looks like
As the champion, I gathered all the stakeholders, and we devised a small proof of concept to satisfy the risk thresholds of legal and my CIO. We implemented on a smaller scale with limited data access, to test the value first before exposing our entire tech and data structure, and to assure ourselves we wouldn’t be exposed to any negative consequences.
If I were a buyer in this climate, I would ask for this on any large enterprise AI purchase. I would demand a small PoC without fully implementing anything. A vendor who won’t scope something small is telling you how they handle risk.
Vendors should ask the likely blocker what would let them approve without worrying about their reputation. Their answer is the requirements list.
How are you making sure you are de-risking your AI adoption? Is it based on small pilots?
Ayse Guvencer is the founder of ELVT Advisory.
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