AI First: The Playbook for a Future-Proof Business and Brand (Adam Brotman & Andy Sack)
- Martin Swartz
- 18 hours ago
- 10 min read
AI First delivers a practical playbook to turn generative AI into productivity, marketing advantage, and a future-proof business and brand.
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INTRODUCTION
This is a Book Essential publication for leaders who feel the ground moving under their feet, and don’t want to be the last one to react. The spark for AI First begins with a moment the authors describe as pure cognitive whiplash: a sunny day in San Francisco, walking out of a meeting with OpenAI CEO Sam Altman, trying to process what “AGI” could mean in practice.
Altman’s prediction wasn’t framed as a distant sci-fi timeline. It was framed as a near-term business shockwave, especially for marketing and brand-building. And that’s the first key move this book makes: it refuses to treat AI as a “tech trend” that can be safely delegated to IT. Instead, it treats AI as a leadership and operating-model disruption, one that will reshape how decisions are made, how work gets done, and how brands compete.
From there, Brotman and Sack do something practical: they translate the noise into a playbook. They connect interviews and research to a set of repeatable steps leaders can actually run, starting with AI literacy, then scaling toward proficiency, governance, opportunity assessment, and ultimately continuous experimentation.
The book is built like a guided journey.
First: what is happening? (copilots, agents, productivity shifts, uneven adoption).
Then: what do I do about it? (marketing transformation, mindset shifts, a structured yet adaptable playbook, and case studies showing how real organizations moved).
If you’re a CEO, CMO, CRO, operator, or team lead wondering how to turn “AI hype” into measurable advantage, without creating chaos, this Book Essential is meant to give you traction in one sitting, and a plan you can start running this week.
U365'S VALUE PROPOSITION
Who benefits most
CEOs and founders who need an AI transformation that doesn’t stall in “pilot purgatory.”
CMOs and brand leaders who see AI changing the economics of creativity, personalization, and performance marketing.
Functional leaders (ops, finance, HR, product, legal, IT) who must balance speed with safety and governance.
High-performing individuals who want an unfair productivity edge without waiting for permission.
Core problems the book solves
The “blank chat box problem”: leaders don’t know what AI can actually do, so they underuse it (or fear it).
The adoption gap: uneven AI proficiency creates competitive winners and laggards inside the same market.
The execution gap: mindset matters, but you still need a practical operating playbook (education → governance → pilots).
Unique insights / approaches
AI as “human + machine” leverage (copilots now, agents soon) rather than a replacement narrative.
A clear maturity path (literacy → proficiency → fluency) for both individuals and organizations.
“Essential utility” framing: treat genAI like electricity, embed it into daily work first, then chase bigger bets.
Real-world transformation patterns from case studies (e.g., Moderna’s adoption engine and change management).
OVERVIEW
AI First argues that generative AI is not just another software rollout, it’s a new layer of capability that will reshape productivity, marketing, and competitive dynamics. The book starts with disruption, then shifts into a pragmatic playbook leaders can tailor to their own culture and risk profile.
At its core, the message is simple: don’t wait for certainty. Build literacy, put governance in place, run structured experiments, and keep your organization dynamic, because the next wave is coming faster than you think.
Human + Machine advantage: copilots and agents amplify teams the way prior revolutions amplified labor.
Productivity is real but uneven: the “jagged frontier” means leaders must learn where AI helps vs. harms.
Marketing gets rebuilt end-to-end: from research to creative to deployment to measurement—many tasks become agentic.
AI First mindset = Growth + Lean + AI: continuous learning, customer-centric iteration, proactive transformation.
The AI First Playbook: education, proficiency, governance, and road-mapped pilots—with AGI-horizon thinking.
SUMMARY
Introduction — The Holy-Shit Moment
The book opens with the authors’ “holy-shit” realization after speaking with Sam Altman, especially the implication that marketing work (strategy + creative + testing) could be radically automated and optimized by AI systems, quickly. That single moment reframes AI from “interesting tool” to “strategic urgency.”
Part One: What Is Happening?
Chapter 1 — Human + Machine reframes AI as augmentation. In their conversation with Reid Hoffman, the authors emphasize copilots and the coming era of AI agents, systems that don’t just generate answers, but can take actions (with appropriate permissions). The key implication: organizations that learn to direct this “human + machine” leverage will outcompete those who simply dabble.
Chapter 2 — Productivity Redefined grounds the conversation in evidence and practical nuance. The “jagged technological frontier” research shows meaningful productivity improvements, but not uniformly across tasks. The authors highlight two collaboration modes, centaur (divide-and-conquer) vs cyborg (tight back-and-forth), and the managerial challenge becomes: teach teams where to trust AI, where to verify, and how to orchestrate human judgment with machine speed.
Chapter 3 — The Middle Era names the messy period we’re in right now: models are already powerful, but adoption and understanding are uneven. Jaime Teevan’s early exposure to GPT-4 becomes a stand-in for the “aha moment” many leaders still haven’t had. Meanwhile, Mustafa Suleyman’s idea of “artificial competent intelligence” (a major step change before true AGI) sharpens the timeline pressure: you don’t need perfect foresight, you need readiness.
Part Two: What Should I Do about It?
Chapter 4 — AI First Marketing takes marketing apart into its “jobs to be done” (research, segmentation, creative, media, CRM, analytics). The argument is not that marketing disappears, but that the unit economics change: creativity becomes faster to explore, personalization becomes scalable, and AI agents become the bridge from plans to execution (draft, test, publish, measure, iterate). The authors even sketch a near-future workflow where marketers command a team of creative and optimization agents to run multisegment campaigns continuously.
Chapter 5 — AI First Mindsets introduces the book’s core behavioral framework: AI First combines a growth mindset, lean thinking, and generative AI. The maturity model (literacy → proficiency → fluency) matters because it tells you what to do next: first get everyone using AI daily for basic work, then level up into custom workflows and tools, then push into strategy and differentiated offerings.
Chapter 6 — Embrace AI, and Pivot Hard becomes the operating playbook: train the organization, build proficiency, deploy governance (AI council + AI use policy), and run an opportunity assessment with a road map and pilots. Importantly, the authors don’t argue for one transformation style, case studies show both “democratized” approaches (e.g., mandated learning time and idea sharing) and top-down leadership approaches, depending on culture.
Chapter 7 — The Essential Utility is the “proof by example” chapter. Moderna becomes the gold-standard case: build internal momentum through training and a prompt contest, create champions, track adoption, and treat genAI as foundational “intelligence as a service.” A standout takeaway is the shift away from obsessing over ROI too early, embed genAI into daily work first, and the bigger transformation opportunities become easier to see and execute.
Conclusion — Another Intelligence in the Room closes with a warning and a challenge: the playbook is just the beginning. Ethan Mollick urges “dynamism”, because the tooling and capabilities will keep shifting. The next move, beyond the initial playbook, is to build a genAI R&D lab so your organization can continuously test, learn, and re-architect itself as the curve accelerates.
A few concrete examples to make this click
A marketing team uses AI to generate 20 campaign angles, then rapidly prototypes creative, then tests performance daily—with agents handling reporting and iteration.
A company runs a prompt contest to crowdsource hundreds of use cases, then turns the winners into internal “playbooks” and custom GPTs.
A leadership team stops treating AI like an IT project and instead builds an AI council and use policy that enables faster safe experimentation.
IN PRACTICE
Here’s a fast, practical way to apply AI First without turning your organization into a chaotic experiment. Think in four lanes (the playbook), then add a fifth capability (dynamism via an AI lab).
1) Education: create AI literacy in 10 business days
Run a short internal “AI literacy sprint”: 5 micro-lessons (30 minutes each) covering what genAI is good at, where it fails, and how to prompt responsibly.
Add “office hours” twice per week so people can bring real work and get help applying AI safely. (Moderna’s internal academy approach is the model.)
Measure: % of employees who used an approved AI tool 3+ times this week; self-rated confidence before vs after.
Example: Your finance team uploads a recurring monthly report template and asks AI to draft the narrative summary + highlight anomalies. That’s literacy-level impact, but it compounds fast.
2) Proficiency: turn “occasional use” into daily leverage (weeks 2–6)
Launch a “prompt contest” (yes, really): ask every function to submit their best prompts for real tasks. Reward outcomes, not gimmicks.
Create a shared library of “gold prompts” by role (sales, HR, marketing, ops).
Require each team to ship one “before/after” workflow rewrite: what took 3 hours now takes 30 minutes, with a quality check built in.
Measure: weekly active users; tasks automated; cycle-time reduction; quality improvements (peer review scores or customer CSAT where relevant).
Example: A marketer uses the cyborg approach: AI drafts multiple strategies, the human selects and refines, AI generates variations and a mood board, the human approves, AI drafts the rollout plan.
3) Governance: speed through clarity, not bureaucracy (weeks 2–8)
Stand up an AI council with cross-functional leaders (include legal + IT input). Define: mission, cadence, what the council approves, and how pilots get selected.
Publish an AI use policy that is enabling by design: what tools are approved, what data is prohibited, and how to document AI-assisted work.
Measure: time-to-approval for new pilots; number of incidents; number of teams confidently running safe experiments.
Example: Sales wants AI to summarize customer calls. Governance clarifies: transcripts allowed only in approved systems, no sensitive exports, and human review required before CRM updates.
4) Opportunity assessment + road map: pick the right pilots (weeks 6–12)
Audit what AI is already happening “in the shadows” (teams using free tools quietly).
Build a backlog of use cases across: cost savings, stakeholder experience, revenue growth, and product differentiation.
Prioritize pilots by (a) impact, (b) feasibility, (c) data readiness, (d) risk.
Add an AGI-horizon check: even if AGI isn’t here, what becomes possible if agents get dramatically better next year? Use that to future-proof your priorities.
Measure: pilot velocity (ideas → prototypes); business KPI movement; readiness improvements (data, tooling, skills).
Example (Marketing personalization pilot): Use AI to define 3 high-potential segments, create tailored creative variations, deploy small-budget tests, and report twice per day on ROAS and learnings, then iterate.
5) Add dynamism: build a small GenAI R&D lab (month 3 onward)
Mollick’s warning is that companies get “stuck” at the copilot stage. The antidote is a small lab (4–8 people) that prototypes new workflows, tests agentic tools, and continuously updates internal best practices.
Staff it with trained internal people (not only contractors).
Give it a pipeline: 2-week experiments, clear success metrics, fast demos.
Output: reusable “AI kits” (prompt packs, custom GPTs, checklists, templates).
QUOTES
Context: The “holy-shit” catalyst that reframes marketing as an urgent AI strategy problem.
“Ninety-five percent of what marketers use agencies… will be handled by the AI.”
Practical relevance: Don’t debate whether marketing changes, assume it does, and redesign workflows so humans direct, verify, and differentiate.
Context: A simple mental model for why human + AI collaboration becomes the new baseline.
“AI is like the steam engine for the mind.”
Practical relevance: Treat AI like leverage: embed it into daily work the way prior eras embedded machines into production, then compete on how well you orchestrate it.
Context: Why early ROI obsession can slow adoption of a foundational capability.
“No one ever gave me the ROI of electricity… I think highly enough of gen AI that I believe it’s in the same category.”
Practical relevance: First embed “intelligence as a service” into daily decisions and workflows; the bigger ROI opportunities become visible once teams are proficient.
Context: A warning that today’s playbook will get outdated unless you stay adaptive.
“What’s missing from your playbook is dynamism.”
Practical relevance: Don’t stop at copilots, create an AI lab and a cadence of experimentation so you can pivot as models and agents accelerate.
AUTHORS EXPERTISE
Adam Brotman brings deep “digital transformation from the inside” credibility: he helped build Starbucks’ payment, ordering, and loyalty platform, and later served as president/chief experience officer/co-CEO at J.Crew. He cofounded Forum3 to help brands leverage emerging tech, and has been recognized by Fast Company and the CDO Club for innovation leadership.
Andy Sack complements that with the operator-investor lens: a longtime entrepreneur and investor, former Techstars Seattle managing director, and an adviser/consultant on digital transformation (including work connected to Microsoft). He cofounded Forum3 alongside Brotman and has been a central figure in the Seattle startup community.
RESOURCES
Author official platforms
Adam Brotman on LinkedIn https://www.linkedin.com/in/adambrotman
Adam Brotman on X/Twitter https://x.com/adambrotman
Andy Sack on LinkedIn https://www.linkedin.com/in/andysack
Andy Sack on X/Twitter https://x.com/AndySack
Forum3 (authors’ company) https://www.forum3.com
HBR Press listing for AI First https://store.hbr.org/product/ai-first-the-playbook-for-a-future-proof-business-and-brand/10742?srsltid=AfmBOopEWaXadSCuBzmA4JjK_yxZXrhl93L_d0vsRdpeqh_HzwDausEV
Related materials & further reading
OpenAI customer story on Moderna (the case referenced in the book) https://openai.com/index/moderna/
Research referenced: “Navigating the Jagged Technological Frontier” (HBS working paper; cited in the book’s notes)
Ethan Mollick’s work (the thinker the authors consult in the conclusion)
Mustafa Suleyman’s The Coming Wave (mentioned in the book’s middle-era discussion)
BONUS - THE U365 AI ADVANTAGE |
Use these “AI First” moves as simple defaults, so AI becomes a daily operating advantage, not an occasional gimmick.
Adopt a two-pass habit: first draft with AI, second pass with human judgment. This matches the book’s “human + machine” posture and reduces hallucination risk. It's the best workflows into reusable assets:** build a shared prompt library by role, and promot custom GPTs/Gems/agents for repeatable tasks (analysis, drafting, QA checklists). Use the UP Method ( University 365 Prompting Method) that invites you to create "UP" reusable Context files : See the UP Method
Use “cyborg promes: ask for options, then iterate:
“Give me 10 approaches.”
“Rank them by impact/effort.”
“Draft version 1.”
“Critique it against these constraints.”Thd-forth collaboration pattern highlighted in productivity research.
Shift from ROI obsession to adoption + proficiency metrics first: track weekly active usage, time saved, and quality lift, then convert wins into bigger roadmap bets.
Design for the next wave: run a lightweight “AGI-horizon” review quarterly (what becomes possible if agents get much better?) and update your roadmap accordingly.
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