Databook Names Anton Stetsenko VP of AI Strategy and Deployment, Formalizing Its AI-Native Services Commitment

Former McKinsey associate partner and Alvarez & Marsal managing director will lead the forward-deployed team that builds and embeds AI go-to-market systems inside enterprise revenue organizations

PALO ALTO, Calif.--(BUSINESS WIRE)--Databook today announced that Anton Stetsenko has joined the company as VP of AI Strategy and Deployment, where he will lead Databook's AI-native services organization — the forward-deployed engineers, AI strategists, and enablement leads who work alongside customer revenue teams to design, deploy, and operationalize AI go-to-market systems.

The appointment formalizes a shift in how Databook delivers value. Enterprises have spent two years buying AI capability and struggling to convert it into revenue. Even the strongest technology produces nothing until it is embedded in how sellers, managers, and operators actually work. Closing that gap requires a mutual commitment to process redesign, adoption, and outcome measurement, where vendors partner with customers instead of simply providing another login destination.

Stetsenko joins from Alvarez & Marsal, where he was a managing director leading end-to-end AI transformations for private equity sponsors and their portfolio companies — partnering directly with sponsors and CEOs to turn AI into a value creation lever with measurable impact. Before A&M he spent more than a decade at McKinsey & Company, most recently as an associate partner in New York, where he led the zero-to-one development and launch of McKinsey Contract AI. He holds an MBA from The Wharton School, where he graduated as a Palmer Scholar.

Under Stetsenko, Databook's services organization engages across the full arc of an enterprise relationship, beginning with a 90-day, risk-free proof of value:

Evaluation

• Forward-deployed engineers embedded from the earliest weeks of an evaluation, building a working solution against the customer's real use case rather than promising a configuration after signature
• Baseline definition — establishing with the customer what improvement means, how it will be measured, and against what starting point, before anything is deployed
• Decomposition of the customer's own sales methodology — stages, qualification criteria, and the decisions made at each one — into the workflows AI can meaningfully carry

Co-design and deployment

• Co-designed agentic workflows, built with the customer on Databook's platform, across use cases like territory scoring, account research and account planning, executive points of view, first-call and meeting preparation, value and discovery coaching, account prioritization and whitespace analysis, and renewal strategy
• Integration with and into the systems revenue teams already work in, including CRM, collaboration tools, and the AI assistants their organizations have standardized on
• Governance design — provenance, approvals, and the controls enterprise revenue leaders need to trust what the system produces

Adoption and enablement

• Manager and seller enablement, change management, and redesign of the ways of working the new capability depends on
• Databook AI bootcamps that train GTM value architects inside the customer's organization, equipped to extend and build on the system after Databook steps back

Value realization

• Measurement of results against the agreed baseline, reported against the milestones set at the outset
• Ongoing tuning of workflows and agents as the customer's motion, market, and priorities change

“Enterprises are increasingly learning that buying an AI platform is not the same thing as getting value out of one,” said Anand Shah, CEO and co-founder of Databook. “Value shows up when the technology is embedded in the way people already work, and that takes behavior change, not just software. The frontier AI labs are funding services companies industry by industry. We're building one by function, and the function is go-to-market. Anton has run these transformations at scale.”

“Companies don’t need another consulting engagement that ends in a deck or another prototype that doesn’t scale,” said Stetsenko. “Real AI transformation happens when the intelligence, the workflows, and the people are all organized around what the customer is trying to achieve, not around the vendor's product. That is how Databook has always worked, and delivering it is what this team exists to do.”

About Databook

Databook delivers the industry's only true GTM Decision System, built on the Databook Customer Context Graph — a proprietary intelligence and AI deep reasoning framework that ensures go-to-market teams are consistently grounded in verified, actionable customer truth. Elite GTM teams from enterprises including Salesforce, Microsoft, Konica Minolta, Databricks, and Schneider Electric use Databook to build pipeline, advance deals, and scale consistency across their seller base.