Eight months into leading River Point Technology’s AI Center of Excellence, the framework shaping our approach predates AI entirely — and that is precisely the point.
I joined River Point Technology last September as Director of Product and Business Solutions and was recently appointed the leader of our AI Center of Excellence, RPT Labs. The question I most often field from my fellow Product peers is some version of: “Where do we begin?”
The answer I have arrived at is not a tool, a vendor, or a model. It is a system.
Organizations that fail to incorporate AI into the core of how they operate will be outpaced — not in a decade, but in the present cycle. That is not a slogan; it is the operating reality of every services and product company. The firms that figure out how to be faster, more productive, and measurably smarter — both internally and in service of their clients — will outrun those still treating AI as a side initiative.
Yet, the failure I see most often is not inaction — it is the impulse to pursue everything at once.
The Wishlist Problem
Most AI Centers of Excellence I’ve witnessed or heard from firsthand are stuck in the same pattern. A backlog of ideas. A queue of vendor demonstrations. A leader who has lost the ability to say no because every prospect or idea feels as though it could be the one. Six months in, nothing has shipped. Twelve months in, the CoE is being reorganized.
This is not a talent problem. It is a portfolio management problem. AI generates an extraordinary volume of plausible ideas absent of a system to triage them. Every CoE becomes a scattered wishlist with no true process or direction.
VCT: A funnel, not a roadmap
At River Point Technology, we apply the same framework to AI that we apply to our core solutions business. We call it VCT — Value Creation Technology — developed by our founder, Jeff Eiben, well before AI became the question on every executive’s desk. That timing matters. VCT was built to discipline all portfolio decisions. We did not need to invent a new framework for AI. We only to apply the one we already trust.
VCT has three stages, and it operates as a product management funnel: Choose, Incubate, Scale.
Choose. Ideas arrive from every direction — our solution pillar leaders, our delivery engineers, our clients, the market. We require each idea to pass through the same evaluation before resources are assigned or funding is allocated. What value is created if this works? What does it cost to find out? What is the smallest version we can credibly test? What do we already possess that uniquely positions us to win? An idea that cannot answer these questions does not advance — regardless of who proposed it. Let’s also not forget AI isn’t necessarily the best answer to all problems. Not everything is a nail, so each hurdle/gap must be critically evaluated to determine if AI is truly the right answer – not just the new “easy button.”
Incubate. Ideas that survive Choose receive small, time-boxed investments with a hypothesis, budget, and deadline. The discipline at this stage is what separates serious CoEs from the rest: a willingness to kill an incubation early when the evidence is absent, and to invest more heavily when it is present. Most CoEs skip this stage and move directly to deployment. That is how many organizations end up with tools or so called “solutions” no one uses.
Scale. Only the ideas that survive Incubate — with demonstrated value and a clear path to repeatable economics — are scaled. Scale is where the meaningful capital goes, and where the wrong investment is most costly. VCT exists to ensure that capital is deployed only against bets that have already been validated.
Two streams, one funnel
Applying VCT to AI differs from applying it to a traditional offering in one important respect: we run it across two streams in parallel. Our solution pillar leaders are divided between internal AI initiatives and external AI offerings.
Internal is the operation of RPT itself — our back-office practices, delivery workflows, knowledge management, and sales operations. Faster, more productive, sharper about our own business.
External is how we serve our clients — the agents, integrations, and AI-augmented delivery patterns that appear in client engagements, alongside the platform, policy, and infrastructure work our delivery organization is bringing to market.
These are not separate strategies. They are the same funnel applied twice. The point I would press hardest with any CoE leader is this: an organization that has not made its own business faster and smarter with AI has no standing to advise its clients to do the same. Internal credibility precedes external credibility. Be your own client zero. The pillar split ensures investment in both, every quarter, with equal rigor.
Product management discipline, applied to a moving target
The deeper value of the VCT funnel — and this matters more in AI than in any category I have worked in — is that it imposes product management discipline on a market determined to behave like a technology fad. New models emerge weekly. New vendors arrive daily. The pull to chase is significant.
Product management has spent decades developing methods to evaluate ideas under uncertainty: funnel principles, hypothesis testing, kill criteria, stage gates. None of this is novel. What is novel is the conviction required to apply these methods to AI even as the noise outside argues for speed at any cost.
Eight months of clarity
What I have learned in my first eight months at River Point Technology is not that AI is difficult. It is that the organizations that will win at AI are the ones that resist the urge to treat AI as exceptional. Treat it as the most consequential portfolio decision your business is making — because it is — and apply the same disciplines you would apply to any other portfolio.
Choose. Incubate. Scale.
Without a funnel, you have a wishlist. And the market will not wait for you to sort it out.
If you are standing up an AI CoE — or refining one that has stalled — I welcome the conversation. DMs are open.