In Q4, deals are decided in rooms your champion can’t fully represent you in, and only a business case an executive can read, rank, and fund without them will survive the budget table.
Leading value teams aren’t asking whether to build or buy AI: they’re building a governed intelligence layer that makes every seller and CSM sound like their best value engineer.
A century ago, from an office on 23rd Street, the Fuller Brush Man built one of the greatest sales runs in American history. Here’s the neuroscience behind why his approach worked, and seven lessons for today’s B2B value sellers.
Buyers have stopped listening to pitches, and interactive capability and maturity assessments let you replace them with diagnosis: they earn engagement, prioritize the right opportunities, and turn customer success into a roadmap for growth.
Two years of AI investment, and the bill is coming due. The spending is not slowing. The proof is not arriving. And a handful of the largest AI providers have just started doing something that should make every revenue leader sit up: they are offering to get paid only when the work actually gets done.
That is not a pricing experiment. It is a transfer of risk. And most go-to-market organizations are not instrumented to survive it.
Most revenue organizations spend real money quantifying value to win the deal, then abandon the model the day the contract is signed. Leverage the Shared Value Plan to help solve this Realized Value challenge.
With AI, you probably can build the value program yourself, which is why arguing the capability question is both wrong and unpersuasive. The questions that actually decide the outcome are whether it sits on a validated foundation, whether the six months you scoped becomes nine or twelve, and what the revenue you were going to earn in the meantime was worth.
At the 2026 Gartner CSO conference, nearly 8 in 10 sales leaders said their top concern was getting sellers to quantify value instead of pitching products, and the wider research explains why it has become existential: buyers now automate the logic with AI and reserve their scarce human attention for proof of value. The value gap and the growth gap have become the same gap, and closing it is no longer a training project but a structural mandate for every revenue leader.
Enterprise software is drifting into a dangerous cycle where buyers and vendors optimize for activity metrics like token consumption, utilization, and renewals, while the actual business outcomes behind the investment remain poorly measured and undervalued.
As AI accelerates the shift toward consumption-based pricing, companies are discovering that maximizing usage does not guarantee maximizing impact, fueling the need for a new model centered on realized business value: Outcome Maxxing.
Salesforce just introduced something called Agentic Work Units, or AWUs. On the surface, it looks like a new billing metric. But if you read it as a market signal, it’s much more significant than that.
We are officially entering the Outcome Economy for AI.
AI is forcing GTM leaders to rethink their Value Automation strategy, but the real question isn’t build vs. buy. It’s who owns the workflow that defines, proves, and delivers value.
For years, the role of the Value Engineer or Value Consultant has been a cornerstone of modern go-to-market teams. Helping quantify ROI, build business cases, and justify decisions has become essential in complex B2B sales.
But as the market shifts toward an Outcome Economy, it raises an important question: Is the role itself evolving beyond its name?
