From AI Business Case and Investment Ask
For product managers and leaders running ai products
AI Product Leadership
AI Product Leadership is 20 decks totalling 842 editable slides. Each deck stands on its own and downloads separately, so you can take the one you need for Monday's session without wading through the rest.
Built for Product managers and leaders running AI products.
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What’s in this toolkit
20 decks, 842 editable slides. Download them individually or take the lot.
1/5
Decision and Investment
LDR01AppliedTemplateFillableAI Business Case and Investment Ask
Put an AI investment ask in front of a board with a baseline measured the same way as the claim.
The investment ask, built on a baseline measured the same way as the claim so it survives scrutiny.
48 slides
Checking1/5
Discovery and Definition
LDR02IntroTemplateFillableAI Feature One-Pager and Opportunity Pitch
Pitch an AI feature on one page and get a clear decision on whether to fund exploring it.
The one-page artefact for pitching a new AI feature and getting a decision on whether to fund exploring it.
39 slides
Checking1/5
Executive
LDR03AdvancedTemplateFillableAI Metrics and Health Dashboard Review
Choose the metrics that show an AI product is healthy, and drop the ones that only look like they do.
The metrics that show whether an AI product is healthy, and the ones that only look like they do.
43 slides
Checking1/5
Discovery and Definition
LDR04AppliedTemplateFillableAI PRD
Write a PRD for a feature whose behaviour is probabilistic, with acceptance defined in evals.
A product requirements deck built for AI features, where behaviour is probabilistic and acceptance needs defining differently.
55 slides
Checking1/5
Planning
LDR05AppliedTemplateFillableAI Product Roadmap
Publish a roadmap for capability that arrives in steps you cannot date precisely.
A roadmap format for AI products, where capability arrives in steps that are hard to date precisely.
43 slides
Checking1/5
Executive
LDR06AdvancedTemplateFillableBoard-Level AI Narrative
Brief a board on an AI programme at the altitude it actually needs.
The board narrative for an AI programme, pitched at the altitude a board actually needs.
43 slides
Checking1/5
Decision and Investment
LDR07AppliedTemplateFillableBuild vs Buy vs Fine-tune Decision
Structure a build, buy or tune decision with the lock-in and cost consequences of each made explicit.
Structure the build, buy or tune decision with the lock-in and cost consequences of each made explicit.
44 slides
Checking1/5
Planning
LDR08AppliedTemplateFillableCapacity and Cost Forecast
Forecast inference capacity and cost, and present a defensible range instead of one number.
Forecast inference capacity and cost, and present the range rather than a single figure.
39 slides
Checking1/5
Market
LDR09AppliedTemplateFillableCompetitive and Market Landscape Brief
Brief a category that is still moving, without pretending the picture is settled.
Brief the competitive landscape for an AI product category that is still moving.
38 slides
Checking1/5
Discovery and Definition
LDR10AppliedTemplateFillableData Requirements and Readiness Assessment
Establish what data a feature needs and whether it exists in usable form, before committing to build.
Establish what data an AI feature actually needs and whether it exists in usable form, before committing to build.
39 slides
Checking1/5
Executive
LDR11AppliedTemplateFillableExecutive AI Strategy Update
Run the recurring update that keeps an AI programme funded and honest.
The recurring executive update that keeps an AI programme funded and honest.
38 slides
Checking1/5
Market
LDR12AppliedTemplateFillableGo-to-Market Deck for an AI Feature
Take an AI feature to market without overclaiming what it can do.
Take an AI feature to market without overclaiming, and set expectations the product can meet.
43 slides
Checking1/5
Delivery
LDR13AppliedTemplateFillableIncident and Quality Postmortem
Run a postmortem where root cause is rarely a single line of code.
Run a postmortem on an AI quality incident, where root cause is rarely a single line of code.
44 slides
Checking1/5
Delivery
LDR14AppliedTemplateFillableLaunch Readiness Review
Gate a launch on the failure modes that only appear in production.
The readiness gate for an AI feature, covering the failure modes that only appear in production.
44 slides
Checking1/5
Delivery
LDR15AppliedTemplateFillableModel Evaluation Readout
Present an evaluation so a non-specialist can judge whether the result is trustworthy.
Present a model evaluation so a non-specialist audience can judge whether the result is trustworthy.
44 slides
Checking1/5
Discovery and Definition
LDR16AppliedTemplateFillablePrompt and Context Change Proposal
Review a prompt or context change with the rigour you apply to a code change.
Propose and review a change to prompts or context with the same rigour applied to a code change.
34 slides
Checking1/5
Planning
LDR17AppliedTemplateFillableQuarterly Planning (OKRs) for an AI Product
Write quarterly objectives that survive model behaviour shifting under them.
Quarterly objectives for an AI product, written so they survive contact with model behaviour that shifts.
38 slides
Checking1/5
Executive
LDR18AdvancedTemplateFillableResponsible AI and Risk and Compliance Review
Evidence the responsible AI controls a reviewer will actually ask to see.
The responsible AI review, covering risk, compliance and the controls that have to be evidenced.
44 slides
Checking1/5
Delivery
LDR19AppliedTemplateFillableSprint and Experiment Review
Review a sprint as a set of experiments and report what was learned, not what shipped.
Review an AI sprint as a set of experiments, reporting what was learned rather than what was shipped.
38 slides
Checking1/5
Decision and Investment
LDR20AppliedTemplateFillableVendor and Model Selection Scorecard
Score vendors and models on the criteria that actually separate them.
A scorecard for selecting between vendors and models, weighted on the criteria that actually differentiate.
44 slides
Checking
How this toolkit is built
Not a description of the toolkit. The actual shape of what’s inside it, drawn from the decks themselves.
Objectives
This Toolkit includes frameworks, tools, templates, tutorials, real-life examples and best practices to help you:
Decision and Investment: 3 decks: AI Business Case and Investment Ask; Build vs Buy vs Fine-tune Decision; Vendor and Model Selection Scorecard.
Discovery and Definition: 4 decks: AI Feature One-Pager and Opportunity Pitch; AI PRD; Data Requirements and Readiness Assessment; Prompt and Context Change Proposal.
Executive: 4 decks: AI Metrics and Health Dashboard Review; Board-Level AI Narrative; Executive AI Strategy Update; Responsible AI and Risk and Compliance Review.
Planning: 3 decks: AI Product Roadmap; Capacity and Cost Forecast; Quarterly Planning (OKRs) for an AI Product.
Market: 2 decks: Competitive and Market Landscape Brief; Go-to-Market Deck for an AI Feature.
Delivery: 4 decks: Incident and Quality Postmortem; Launch Readiness Review; Model Evaluation Readout; Sprint and Experiment Review.
Questions about AI Product Leadership
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Examples of projects done with our Toolkits and Experts
AI Product Strategy
Defining a two-year AI product strategy for a B2B SaaS platform, resulting in three funded product bets and a 30% increase in expansion revenue within 18 months.
Use Case Portfolio
Running enterprise-wide AI use-case discovery for an industrial group, producing a scored portfolio of 60+ use cases and a funded first wave of 8 pilots.
GenAI Feature Launch
Guiding a marketplace team from PRD to launch of an AI assistant, reaching 40% weekly active adoption within one quarter.
AI Governance
Standing up an AI review board and tiered risk framework for a financial services firm ahead of EU AI Act enforcement.
Procurement AI
Deploying AI spend classification for a global retailer, uncovering $25M in addressable savings across 4 categories.
Demand Forecasting
Piloting ML demand forecasting for a CPG company, cutting forecast error by 18% and safety stock by 12%.
Supplier Risk
Designing an AI-assisted supplier risk monitoring program covering 2,000 suppliers with automated tiering and alerting.
Control Tower
Blueprinting a supply chain control tower for a logistics provider, reducing exception resolution time by 35%.
AI Academy
Launching a role-based AI academy for an operations function of 1,500 people, with 85% completion in six months.
Contract Intelligence
Implementing clause-extraction workflows for a legal-procurement team, cutting contract review time by 50%.
S&OP Upgrade
Embedding forecast-value-added analysis into S&OP for a manufacturer, aligning demand plans across three business units.
Operating Model
Designing a hub-and-spoke AI operating model for a services group, clarifying decision rights across 5 business lines.