Enterprise AI is exhilarating and unforgiving. I’ve seen gorgeous demos fall apart under real-world constraints and seemingly modest pilots unlock outsized value at scale. In this reflection, I share the playbooks I rely on to build, scale, and sell generative AI in the enterprise—what actually moves deals, secures product-market fit, and sustains trust with the C-suite and the front line.
Why is it so difficult to scale AI products for enterprise? Because the bar is higher on every dimension: data governance, security, extensibility, integration depth, reliability, and measurable ROI. An enterprise-grade, full-stack generative AI platform isn’t just a model; it’s the surrounding system—observability, evals, policy, workflow, and human-in-the-loop—that makes outcomes predictable, auditable, and safe. The fastest path to adoption is simple: deliver on-brand, on-policy content and decisions using a customer’s first-party data, and prove that quality holds up under load.
My north star is dependability over demo magic. The number one challenge is making model output dependable across messy, high-variance enterprise inputs. I build an evaluation harness early, with gold datasets, task-specific metrics, and human adjudication. Every change ships behind guardrails and is measured against cost, latency, and quality SLOs. When governance, change management, and procurement show up (they always do), I treat them like first-class product requirements, not hurdles.
Champions are the secret to winning complex accounts. I map the org, find operators who feel the pain daily, and quantify that pain in dollars and hours. Then I define success criteria upfront—time-to-value in under 30 days, measurable uplift (e.g., deflection, conversion, cycle time), and a plan for scale. I deploy forward deployed engineers alongside the business to co-design workflows, refine prompts and evaluators, and document before/after outcomes. Champions don’t just approve pilots; they co-author the business case and defend it.
To win the enterprise, trust architecture matters as much as model architecture. I lead with clear answers on data residency, encryption, SSO, RBAC, DLP, and retention policies; I address whether customer data trains models, default behaviors, and opt-in controls. I offer flexible deployment (VPC or private networking when needed), transparent pricing, and SLAs with real teeth. I also integrate where work already happens—CRM, help desk, knowledge bases—so value shows up in the flow of work.
Signs of healthy product-market fit are unmistakable: pull from lookalike buyers, multi-threaded expansions, champions who present results internally without me in the room, and usage that moves from experimentation to business-critical. I watch for weekly active usage above pilot thresholds, POCs converting to multi-year deals, and adjacent teams (Support, Marketing, Legal, RevOps) asking to onboard with minimal push. PMF feels less like persuasion and more like coordination.
Scaling large language models for specific use cases requires ruthless focus. I constrain scope to tightly defined workflows, pair retrieval with structured knowledge, and mix model strategies (base models, fine-tunes, tools, and function calling) based on cost and latency budgets. I codify policy-as-code and deploy guardrails at the orchestration layer, not just the model layer. Continuous evaluation—both automatic and human—is the heartbeat of quality.
My advice to AI founders in 2024 is pragmatic. Start with outcomes, not demos. Establish outcomes vs output OKRs that tie directly to revenue, cost, risk, or customer experience. Use gen AI for product prototyping to shorten discovery cycles, but graduate quickly to instrumented workflows in production. Align early with InfoSec and Legal; your speed will be gated by trust, not code. And when in doubt, ship smaller, safer increments faster.
Healthy co-founder relationships look the same across winning companies: clear domains, fast escalation, and a shared appetite for “disagree and commit.” I keep a decision log, time-box debates, and make moments-of-truth visible to the team and board. You’ll know it’s working when you have more energy after hard conversations than before.
The future of agentic AI is deeply enterprise: multi-agent workflows that plan, act, and verify with human oversight where it matters. The winners will combine reasoning, tool use, retrieval, and policy with audit trails that satisfy compliance while keeping velocity high. Think of it as re-engineering business processes around AI-native steps, not sprinkling AI on top of legacy workflows.
Culture turns strategy into reality. I anchor my teams on “connect, challenge, and own.” Connect means obsess over the customer problem and internal alignment. Challenge means we red-team our ideas, run experiments, and measure impact. Own means we accept outcomes, not just output, and we iterate until the business moves. This is how a customer support ai strategy becomes a durable moat, not a slide.
If you’re a product creator or product management leader, the above playbooks are meant to be lifted and adapted. Start where the pain is loudest, quantify the win, and let champions carry the story. The compound interest of disciplined product discovery, strong governance, and relentless evaluation is a generative AI business that sells itself—and scales.
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