The strategy mandate
'What's our AI strategy?' is the wrong question until it's reframed — meet Alder Logistics, the mandate you've been handed, and the five artifacts that make a strategy real.
The board meeting ends with a sentence you'll hear in some form this year if you haven't already: "Our competitors are announcing AI initiatives. What's our AI strategy?" You are Jordan Okafor, SVP of Operations at Alder Logistics — 1,200 people, regional freight and warehousing, decent margins under steady pressure — and as of this morning, the AI mandate is yours. This course is the next ninety days done right: from that sentence to a board-ready roadmap, one workshop artifact at a time.
First move: reframe the question
"What's our AI strategy?" treats AI as a destination. Companies that answer it literally produce a document about technology — models, platforms, a chatbot — that no operating leader can execute. The reframe that produces something executable: "Where can AI capabilities materially advance the business strategy we already have — and what has to be true for us to capture that value?" Alder's actual strategy is boring and clear: win regional contracts on reliability, defend margin against national carriers, keep drivers. The AI strategy is those three sentences, with new leverage — not a fourth sentence about technology.
- AI is a capability set, not a project. 'Do AI' fails the way 'do electricity' would. The unit of strategy is a specific capability applied to a specific business problem with a specific owner — everything in this course operates at that unit.
- The deployment gap is the real story. Behind the headlines, most enterprise AI value today comes from unglamorous wins: document handling, support triage, forecasting assists, drafting. The gap between AI's demo ceiling and its deployed floor is where strategy lives — leaders who calibrate to demos overpromise; leaders who calibrate to deployments compound.
- Your scarce resources aren't compute — they're attention, trust, and change capacity. An organization absorbs a finite amount of workflow change per quarter. Strategy is choosing where to spend that, which is why prioritization (Module 2) gets a whole module and 'more pilots' is not a strategy.
- This isn't a solo effort — staff it minimally before you start. The mandate assumes a small support team: at least one analyst to build the inventory and run the numbers, and a part-time PM or chief of staff to drive the workshops and track owners. The ninety days below assume that support exists; if it doesn't, securing it is your actual first move.
The five artifacts (your course deliverables)
- Opportunity inventory — every candidate use case, scored (Module 1).
- Priority portfolio — the funded few, with kill criteria (Module 2).
- Build/buy decisions — for the top initiatives, with vendor criteria (Module 3).
- Governance charter — one page: risk appetite, approvals, oversight (Module 4).
- The roadmap — all of it assembled for a board audience, with a change plan and metrics (Module 5 capstone).
This is the strategy half of the leadership pair. Its sibling — AI Governance, Risk & Compliance — goes operator-deep on the risk, regulatory, and audit machinery that Module 4 here treats at executive altitude. Take this one first if you're setting direction; take both if you're accountable for the program. No code in either; the technical courses (Foundations through LLMOps) are where your teams get equipped, and Module 5 shows you how to route them there.
Leaders learn AI's texture fastest by using it on their own work. Every workshop in this course includes prompts to run against a frontier assistant — drafting, stress-testing, red-teaming your own strategy. You'll form better intuitions in four weeks of hands-on skepticism than in a year of vendor briefings. If you haven't set up an AI assistant yet, spend the first hour of AI Foundations first — or have your team stand up a sanctioned account — before the workshops.