Lillian Pierson, P.E., fractional CMO and growth strategist, engineers marketing systems that drive predictable growth for tech startups. With a professional engineering license and 20 years of experience driving growth for Fortune 100 and early-stage companies alike, she bridges product and growth marketing to drive real boots-on-the-ground traction. She's trained 2M+ professionals in AI and data, grown organic channels to 750K+ followers, and authored 11 books including Data Science For Dummies. Lillian helps VC-backed and bootstrapped founders (pre-revenue to $6M ARR) scale through structured playbooks and data-driven systems.
I explain how to turn public web data into CRM-ready prospect lists, ICP research, and competitive signals.
I explain five evidence areas—evals, observability, governance, deployment, and a trust pack—to shorten enterprise AI review cycles.
I break down AI spreadsheet choices that keep MRR, CAC, funnel math, and runway tied to live data for faster GTM decisions.
Sort AI GTM actions by risk; require human approval for customer-facing, pricing, or sensitive-data tasks and run weekly checks.
Align ICP to workflow outcomes, pick an ACV-based sales motion, and run tiered ABM combining AI agents and humans.
Treat AI spend as separate buckets—optimize inference, serverless, and orchestration to stop margin leaks.
How lean AI founders ship, measure, and connect product to revenue with one tool per job, clear metrics, and staged budgets.
Compare Coreworks, Sourcetable, and Docsumo to pinpoint funnel leaks, clean intake data, and turn pipeline signals into actionable revenue decisions.
Compare code-first, no-code, and AI support tools to fix activation gaps and speed time-to-value in PLG products.
Compare prebuilt, plain-language, orchestration, and custom AI workflow models to pick the right fit for lean teams.
Compare five AI agent platforms for GTM teams, weighing no-code speed versus governance, deployment, and workflow reach.
AI search drives high-converting B2B traffic but is largely undercounted—fix analytics first, then optimize page structure.
Log incorrect AI answers, trace their sources, fix site copy, schema, and third-party profiles, then recheck on a schedule.
Run repeatable AI prompt audits across platforms, score citations, and prioritize fixes by revenue to improve AI search visibility.
Governance-first checklist to evaluate AI marketing ops tools—workflows, RBAC, security, reporting, and a weighted vendor rubric.
Automate reports with a KPI tree, clean attribution, CRM ties, and human review so AI summaries drive actionable decisions.
Fix MOps and tracking before automating: clean routing, UTMs, and ownership prevent messy data and bad attribution.
GenAI in MOps: adoption, workflows automated, hours saved, and ROI — prioritize repeatable work for fast payback.
How small B2B teams run capture-enrich-route-execute-report with role-based governance and AI, without hiring large MOps teams.
Explore how real-time data revolutionizes market predictions, enabling businesses to respond swiftly and accurately to changing trends.
Lifetime value is shaped long after the sale closes. Every post-sales interaction sends a signal about reliability, respect, and commitment.
