Innovation Strategy and the Innovation Placemat
Align goals, win an executive champion, and plan for risk.
Innovation strategy connects corporate goals to funded bets. Set the portfolio mix, name an executive champion and define what evidence earns more investment or triggers a stop.
Why this matters
Ideas compete with core escalations, renewals and feature demands. A strategy gives longer bets explicit resources, sponsorship and evidence milestones.
George Day’s 2007 HBR article reported minor innovation at 85–90% of the average development portfolio, with major innovation falling from 20.4% in 1990 to 11.5% in 2004. These are historical figures. Use them to recognize the pull toward the core.
Answer four questions: which corporate goals innovation serves, where to innovate, how much each band receives and how to fund or stop bets. The one-page innovation placemat records those choices. Chapters 21–23 cover learning, tools and scaling.
Core concepts and frameworks
The innovation placemat
An innovation placemat records one bet or a small related portfolio on one landscape page. Innovation courses use the term for plans linking goals, champions, measures and risk. This book’s nine-box version adapts it for B2B and platform products:
| Box | What it says | The question it answers |
|---|---|---|
| 1. Strategic intent | The corporate goal this serves, quoted from the company strategy, and a one-sentence innovation thesis | Why should the company care? |
| 2. Where we will play | The cell on the ambition matrix and the innovation types used | How far from the core, and what kind of innovation? |
| 3. Customer and job | Who has the problem, what the problem is, and what evidence already exists | For whom, and how do we know? |
| 4. Bets and hypotheses | The two to four assumptions that must be true, ranked by risk | What has to be true? |
| 5. Goals | One or two outcome goals, leading indicators, a guardrail and a learning goal for this stage | How will we know it is working? |
| 6. Resources and horizon | People, money, time box and the portfolio band it draws from | What does it cost, and from whose budget? |
| 7. Executive champion | Named person, what they own, decision rights and cadence | Who protects it? |
| 8. Risks and mitigations | The top five risks from the risk register, each with an owner and a kill or pivot trigger | What could go wrong, and what will we do? |
| 9. Milestones and stage gates | The evidence needed to earn the next tranche of funding | What earns more money, and when do we stop? |
The placemat records reciprocal commitments. The team delivers evidence, goals and honest risk reporting. The sponsor provides resources, protection and gate decisions. Both sign before funding.
Revise the placemat at each gate. Keep prior versions to compare commitments with learning.
Ten types of innovation
Doblin’s Larry Keeley and colleagues organized more than 2,000 successful innovations into ten types in Ten Types of Innovation (2013):
| Category | Type | B2B and platform example of a move |
|---|---|---|
| Configuration | Profit model | Charge per outcome instead of per seat |
| Network | Build a partner or connector ecosystem | |
| Structure | Reorganize talent or assets, such as a shared AI platform team | |
| Process | Change how the work is done, such as AI-assisted support triage | |
| Offering | Product performance | Faster, more accurate, more capable product |
| Product system | Integrations, extensibility, complementary products | |
| Experience | Service | Onboarding, managed services, post-incident reviews |
| Channel | Partners, marketplaces, product-led trials | |
| Brand | What the company stands for, such as trust in AI | |
| Customer engagement | The moments that make customers care, such as a clear answer in under five minutes at 3 a.m. |
The authors’ central argument is that most companies over-invest in product performance, the most visible type and the easiest for competitors to copy, and that innovations combining several types are harder to copy. Treat the second claim as the authors’ finding from their own database.
Scan each type. Profit models, networks and product systems can rely on contracts, relationships and installed base that model vendors cannot readily copy.
Sustaining and disruptive innovation
Christensen introduced disruptive innovation with Joseph Bower in 1995 and developed it in The Innovator’s Dilemma (1997). Use the term for the specific process below.
The Christensen Institute defines two kinds of innovation:
- Sustaining innovation makes good products better for an incumbent’s most demanding customers. It can be incremental or a breakthrough, and it usually comes at a higher price and margin.
- Disruptive innovation starts with simple, cheap offerings at the bottom of a market, or in a market that did not exist, and moves upmarket until it displaces incumbents.
Disruption has two footholds. A low-end foothold serves the least profitable customers, the ones incumbents are happy to lose, with a cheaper, good-enough model. A new-market foothold serves people who were not customers at all, because the old products were too expensive or too complicated.
A better, higher-priced product for existing demanding customers is sustaining innovation. Christensen, Raynor and McDonald’s 2015 clarification used Uber as a counterexample to disruption and emphasized the process over time.
Critics include Jill Lepore, who challenged case selection in 2014, and King and Baatartogtokh, whose 2015 analysis found few examined cases met all core conditions. Use the theory as a lens with limits.
Ask which customers are we overserving, and who could serve them more cheaply? A bundled agent can serve small or previously unserved teams that enterprise feature comparisons overlook.
Core, adjacent and transformational: the ambition matrix
Bansi Nagji and Geoff Tuff’s 2012 HBR innovation ambition matrix classifies bets on two axes:
- Where to play (horizontal): serve existing customers and markets, expand into adjacent ones, or create new ones.
- How to win (vertical): use existing products and assets, add incremental ones, or develop new ones.
The matrix gives three bands:
- Core: optimizing existing products for existing customers.
- Adjacent: expanding from the existing business into business that is new to the company.
- Transformational: developing breakthroughs and new offers for markets that do not yet exist.
The authors reported that companies that outperformed their peers allocated roughly 70% of innovation resources to core initiatives, 20% to adjacent and 10% to transformational, and that the returns ran in roughly the inverse ratio, with transformational bets producing the largest share of long-term return. They also cautioned that the right mix varies by industry, competitive position and stage of development.
Treat 70-20-10 as a consulting finding, rather than a universal target. Classify each bet, total spend by band and compare with your agreed mix. Explain the gap, such as migration work keeping core spend at 85% against a 65% target.
Three horizons
Baghai, Coley and White’s The Alchemy of Growth (1999) classifies businesses by maturity into three horizons:
- Horizon 1: extend and defend the core businesses that produce most of today’s profit.
- Horizon 2: build emerging businesses that are gaining traction and need investment to scale.
- Horizon 3: create options on future businesses through research, pilots, partnerships and small stakes.
Use profit and efficiency for Horizon 1, adoption and growth for Horizon 2, and learning milestones and option value for Horizon 3.
The model needs three caveats.
- Horizons describe maturity, not calendar time. Reading them as “this year, two years, five years” breaks in fast markets, where a Horizon 3 option can mature in a year.
- The horizons now run at the same time. Steve Blank argued in HBR in 2019 that the model no longer applies as originally drawn. Its assumption that Horizon 3 ideas have years to mature no longer holds when startups and new technologies can turn them into threats to the core quickly, so companies need a way to run work in all three horizons at once.
- Horizons and the ambition matrix measure different things. The matrix measures distance from the core; horizons measure maturity and management mode. Most transformational bets start in Horizon 3, but not all. Use the matrix to set the mix and the horizons to decide how to manage each bet.
Aligning innovation strategy to corporate strategy
Write the link to corporate strategy as an innovation thesis:
Because the company is pursuing [corporate goal], we will innovate in [ambition band and types] for [customers], measured by [outcome], funded at [target mix].
Quote the corporate goal from the company’s own strategy document. If you cannot find a goal to quote, there are two honest options: find a sponsor who will argue for the bet outside the stated strategy, or stop.
Govindarajan and Trimble recommend a deliberate partnership between innovation teams and the core performance engine. O’Reilly and Tushman’s ambidextrous organization separates exploration while integrating it at the senior team. Define shared assets, reciprocal commitments and executive dispute resolution.
Specify which customers, data, sales capacity and platform services the team borrows, what the core receives and who settles conflicts.
Choosing where to innovate
Five inputs decide where to innovate:
- The ambition matrix gap: which band is underfunded against the agreed mix.
- The ten types scan: which types you have ignored, especially profit model, network and product system.
- The disruption lens: which customers you overserve, and who could serve them more cheaply.
- Customer evidence: jobs, pains and switching stories from discovery (Chapter 01).
- Your right to win: assets you have and a competitor or model vendor cannot easily copy.
George Day’s 2007 HBR article offers a screen built on three questions: Is it real? (Is there a real market, and can a real product be built?) Can we win? (Can the product be competitive, and can this company win with it?) Is it worth doing? (Will it be profitable at acceptable risk, and does it fit the strategy?) Run each candidate bet through the three questions before it gets a placemat.
In 2026 the right-to-win question deserves the most weight, because foundation models make generic capability cheap for everyone. Favor bets where proprietary data, installed base, partner distribution, workflow position or ecosystem position give you an advantage that does not come with the model. Chapter 16 covers defensibility in depth.
Executive sponsorship
An executive champion secures resources, removes blockers, represents the bet and makes gate decisions.
Prosci reports active, visible sponsorship as its leading success factor since 1998. Its vendor surveys associate highly effective sponsors with nearly three times the goal attainment of weak sponsors. About half of respondents said sponsors poorly understood their role. Prosci’s ABCs are participation, coalition-building and direct communication. Kotter’s 1995 HBR article similarly emphasizes a guiding coalition.
Three practical rules for choosing a champion:
- Pick the executive whose goals the bet serves. The best champion is the leader whose own metric moves if the bet wins, which is often not your line manager. A pricing innovation might belong with the chief financial officer or chief revenue officer.
- Avoid sponsors who only lend their name. The organization reads a sponsor’s absence as permission to defund.
- Agree the kill criteria while everyone is optimistic. Ending a bet is the hardest conversation. Have it at the start, in writing, so the end is a decision rather than a fight.
The executive champion brief in this chapter’s artifacts puts the ask on one page. Chapter 36 covers stakeholder management more broadly.
SMART goals and their limits
George Doran’s 1981 SMART goals meant Specific, Measurable, Assignable, Realistic and Time-related. Later variants use Achievable, Relevant and Time-bound. Doran noted that every objective need not be quantified.
SMART forces precision: a named outcome, a number, a date and an owner. It has two limits for innovation.
Early-stage bets cannot set honest revenue targets. A transformational bet does not know its market size, price or adoption curve, so a “realistic” revenue target is a guess or a negotiation. Rita McGrath and Ian MacMillan’s discovery-driven planning (HBR, 1995) argues that new ventures need a different planning tool: plan to learn, list the assumptions that the plan depends on, and convert them into knowledge at checkpoints before committing more money. Chapter 21 covers the experiments that do the converting.
Watch goal side effects. Ordóñez, Schweitzer, Galinsky and Bazerman’s 2009 “Goals Gone Wild” paper identifies narrow focus, unethical behavior, distorted risk appetite, cultural damage and reduced intrinsic motivation. SMART evidence varies by context. Supervise targets and guardrails together.
The fix in this book is to pair every outcome goal with three companions:
| Element | Purpose | Example shape |
|---|---|---|
| Outcome goal (SMART) | The result the bet exists to produce | By [date], [segment] will [outcome] from [baseline] to [target], measured by [source] |
| Leading indicator | An early signal that moves before the outcome | Weekly active users of the new capability |
| Guardrail | A metric that must not get worse, to stop gaming | Customer-reported incidents caused by the feature stay at zero |
| Learning goal | The assumption this stage must confirm or refute | Customers will pay per outcome at a price that meets the margin target |
Early-stage bets lead with the learning goal. Later-stage bets lead with the outcome goal. The guardrail stays at every stage, because it is the cheapest protection against the side effects that Ordóñez and her colleagues describe.
Innovation risk and mitigation
Take intended risks at an affordable scale and monitor early signals.
Group innovation risks into seven categories:
| Category | The question | Typical B2B and AI example |
|---|---|---|
| Desirability | Do customers want it? | Users like the demo but will not change their workflow |
| Viability | Do the economics work? | Inference cost erodes gross margin |
| Feasibility | Can we build it to the needed quality? | Model accuracy is below the trust threshold |
| Adoption and change | Will users, partners and channels adopt it? | Partners resist a pricing model that cuts their margin |
| Organizational | Will the company sustain it? | The sponsor leaves; the core resents cannibalization |
| Regulatory and trust | Will regulators and security reviewers allow it? | Security blocks production access; AI regulation demands documentation |
| Ecosystem and dependency | What do we depend on that we do not control? | A model vendor changes price or terms |
The first three are the familiar lenses used throughout this book. In enterprise products, the last four are often what kills the bet.
The common mitigations are staged funding tied to evidence (each tranche earned by a milestone, not a date), reversible pilots with design partners under contract, guardrail metrics, pre-agreed kill triggers, and parallel bets when one assumption is critical and uncertain. For AI products, add evals, human approval gates and an AI risk framework. The NIST AI Risk Management Framework, released in January 2023 with a Generative AI Profile added in July 2024, organizes AI risk work into four functions: Govern, Map, Measure and Manage. Use those words for the AI rows of your risk register so your security and compliance reviewers see their own vocabulary. Chapter 33 covers AI regulation, and Chapter 31 covers evals.
The innovation risk register in this chapter’s artifacts states each risk as “if [cause], then [effect] on [goal],” scores likelihood and impact, names a leading signal to watch, an owner and a kill or pivot trigger. The top rows of the register should match the riskiest assumptions on the placemat. If they do not, one of the two documents is wrong.
How to apply it
This six-step method builds an innovation strategy for your product line and turns its first bet into a signed placemat. Plan on four to six weeks the first time; after that, revise placemats quarterly and the strategy yearly.
Step 1: Anchor on the corporate strategy (week 1).
- Have your AI harness extract every stated corporate goal, with exact wording and source, from the strategy documents, the portfolio plan and your last business review.
- Pick the one to three goals your product line can move and write the innovation thesis for each. Note each goal’s executive owner; it matters when you choose a champion.
Step 2: Map the current portfolio (week 1).
- List every funded initiative on your roadmap. Classify each into one of the nine cells of the ambition matrix and one horizon, and tag the innovation types it uses.
- Estimate the share of effort in each band from approximate engineering allocation; precision is not the point.
- Agree a target mix with your manager and the likely champion, starting from your context rather than 70-20-10. A product in a fast-moving AI category may need more outside the core than a mature product with a large installed base.
Step 3: Generate and screen candidate bets (weeks 2 and 3).
- Run the ten types scan, with at least one candidate move per type.
- Apply the disruption lens: name the customers you overserve and the cheaper alternatives that could serve them, and decide whether to defend, serve them with a lighter offer or let them go.
- Bring in customer evidence from discovery (Chapter 01) and market sizing (Chapter 13).
- Screen each candidate with Day’s three questions: Is it real? Can we win? Is it worth doing? Keep two to four bets that pass and that close the gap between your current mix and the target.
Step 4: Secure an executive champion (week 3).
- For each surviving bet, identify the executive whose goals it serves and meet them with the executive champion brief.
- Ask for specific behaviors (attending the quarterly gate, removing one named blocker, recruiting a peer) and agree the kill criteria in the same meeting.
Step 5: Write the placemat (weeks 3 and 4).
- Draft the nine boxes. Write the bets and hypotheses box before the goals box, because the goals should test the hypotheses.
- Set goals in the four-part form: outcome goal, leading indicator, guardrail and learning goal. For early-stage bets, make the learning goal the headline.
- Build the innovation risk register. Brainstorm across all seven categories, score each risk and carry the top five onto the placemat with owners and triggers.
- Define the stage gates: the evidence each gate requires and the funding it releases.
Step 6: Review, sign and run (weeks 5 and 6, then quarterly).
- Ask a skeptical peer, and your AI harness, to red-team the placemat. Fix what they find.
- Review it with the champion and get a signature, or an explicit list of changes.
- Add the bet to the portfolio forum’s ambition matrix. At each quarterly gate, update the placemat, the risk register and the matrix, and make the decision: continue, pivot, scale or stop.
The AI lens
Building AI-powered products
AI is both an innovation driver and a source of new risk. Five developments shape innovation strategy in 2026.
AI is everywhere, and the value is concentrated. Stanford HAI’s AI Index 2026 reports organizational AI adoption at 88%, up from 78% for 2024 in the previous year’s report. Yet BCG’s September 2025 study found that only about 5% of companies were achieving value from AI at scale, while about 60% reported little or none. Most competitors can buy the same models, so “adding AI” is no longer an innovation in itself. Ask which business model, workflow or ecosystem change AI makes possible, using the ten types. Charging per outcome because an agent can now do the work, connecting to the customer’s other systems, or running as a service what used to need people are each harder to copy than a model-powered feature.
The economics keep moving. The AI Index 2025 reported that the inference cost of a system performing at the level of GPT-3.5 fell more than 280-fold between November 2022 and October 2024. Ideas that failed the viability test in 2023 deserve a second screen. But agentic workloads, which run many model calls per task, can still compress margins, so put inference cost in the viability rows of your risk register and track it as a leading signal, not a quarterly surprise.
Agents are moving from demo to portfolio line, with real reliability risk. BCG estimated that agents accounted for about 17% of AI value in 2025 and projected about 29% by 2028. The AI Index 2026 reports the best agent at 66.3% on the OSWorld computer-use benchmark against a human baseline of 72.35%, while agents still fail roughly one structured task in three. Gartner predicted in June 2025 that over 40% of agentic AI projects would be canceled by the end of 2027, an analyst prediction rather than an observed rate. The lesson: classify agentic bets as adjacent or transformational, fund them in stages, and write reliability, cost and trust risks into the register from day one.
Interoperability lowers the cost of ecosystem innovation. Anthropic open-sourced the Model Context Protocol (MCP) in November 2024 as an open standard for connecting AI systems with data sources and tools, and in December 2025 MCP became a founding project of the Linux Foundation’s Agentic AI Foundation. For platform PMs, publishing connectors or letting partners publish them is now a cheap network and product-system move. It also means a competitor’s agent can plug into the same systems, so the advantage moves to data, workflow position and trust.
The disruption lens applies to you. Cloud consoles and model vendors bundling good-enough agents are classic low-end and new-market entrants for specialist B2B tools. Ask which of your customers would settle for a fraction of your product at no extra cost, and decide on purpose what you will do about them.
Doing the work with AI
Use a connected harness for sourced drafts and challenge:
- Drafting from sources. Extract the corporate goals from the strategy memo and business reviews, then draft the thesis and first placemat, with a source link on every claim.
- Classifying the portfolio. Propose a matrix cell, horizon and innovation types for each roadmap item, with a one-line reason. Your disagreements with it start the strategy conversation.
- Widening the option set. Ask for two moves per type in a ten types scan, then discard most of them.
- Building the risk register from history. Mine past post-mortems, cancelled projects, win and loss notes and security findings for risks that apply to the new bet.
- Red-teaming. Argue against the placemat as a skeptical CFO, a security reviewer and a competitor.
The rule for this chapter: the AI drafts, the PM and the sponsor decide. Check every number on the placemat against its source report, and remove anything you cannot trace. A model’s estimate of market size or adoption does not belong on a document an executive signs. Placemats often draw on customer data, pricing and unreleased plans, so use only tools your company has approved for confidential information.
The Beacon case: an innovation placemat for FY2027
The percentages, targets and spend mix in this section are illustrative numbers created for this chapter. They are not part of the Beacon case facts.
Mapping the portfolio. The harness classifies every roadmap initiative on the ambition matrix from the roadmap and engineering allocation. After you correct a handful of classifications: roughly 80% of effort is core (diagnosis quality, correlation tuning, the Classic migration, the Observe integration), about 15% adjacent (Beacon Agents, the pricing change) and about 5% transformational. The Classic migration explains most of the core weight. You propose a target of 65/25/10 over the fiscal year, freeing effort as the migration finishes.
The ten types scan. Most of what the scan turns up is not a feature:
- Profit model: per-resolved-incident pricing, which aligns Beacon’s revenue with the North Star metric (incidents resolved with Beacon’s help per week).
- Network: a multi-tenant edition for MSPs like Leo, and a marketplace where partners publish runbooks as MCP servers.
- Product system: deeper use of Northlight Observe data, set against the larger market of staying tool-agnostic.
- Service: post-incident reviews offered as a service.
- Customer engagement: Dana’s need for a clear “what broke and what do I do” in under five minutes.
- Brand: trust that the agent will not make things worse, which Priya named as a core need.
The disruption lens. Startups building ops agents, and cloud platforms bundling AI into consoles customers already pay for, are low-end entrants for the mid-market that Leo’s MSP serves. Beacon risks overserving that segment with enterprise-grade features priced for Marcus, which pushes the MSP edition up the list.
Choosing the bet. You screen the candidates with Day’s three questions. The partner runbook marketplace is real but fails “can we win” this year, because the connector ecosystem is too thin. The outcome-guaranteed autonomous service fails “is it worth doing” until diagnosis reliability improves; the hallucinated root cause that led a customer to roll back the wrong service is still fresh. The bet that passes all three is a combination: Beacon Agents with per-resolved-incident pricing, an adjacent bet that combines product performance, profit model and brand. Your thesis: Because Northlight is pursuing growth in the Operations portfolio and recovery of gross margin, we will innovate in supervised remediation and outcome-based pricing for existing enterprise customers, measured by incidents resolved with Beacon’s help, funded from the adjacent band.
Choosing the champion. The obvious sponsor is your manager, the VP of Product. The better champion is the CFO: margin recovery and the pricing change are on the CFO’s agenda, and the bet will cannibalize per-environment fees that someone senior must defend. Your brief asks for approval of a pricing pilot with 10 design-partner customers, funding for two more applied AI engineers, and explicit air cover for the revenue cannibalization. You ask the CFO to attend each quarterly gate and to recruit the chief revenue officer, whose sales team will carry the new price.
Setting goals with their limits. Your first draft of the outcome goal is an autonomous resolution rate target. A peer points out the risk Ordóñez and her colleagues describe: a team measured on autonomous resolutions has an incentive to allowlist riskier actions. You rewrite it in four parts:
- Outcome goal: by the end of Q2 FY2027, 40 enterprise customers will run Beacon Agents with at least one allowlisted autonomous action, measured in product telemetry.
- Leading indicator: weekly responders approving agent-proposed actions.
- Guardrails: zero Sev-1 incidents caused by an autonomous action, and no fall in diagnosis acceptance rate.
- Learning goal: confirm that design partners will pay per resolved incident at a price that moves gross margin back toward 78%.
The risk register. The harness proposes 23 risks from past post-mortems, security reviews and win and loss records; you cut them to 9. The top five go on the placemat: a hallucinated root cause triggering a wrong remediation (mitigated by evals, the approval gate and the customer allowlist); inference cost eroding margin (tracked weekly per resolved incident, with a price floor); Sam’s security review blocking production access (least-privilege credentials, per-action audit logs and a kill switch); MSP partners resisting per-incident pricing that squeezes their margin; and EU customers’ requests for AI Act documentation delaying deals. Each has an owner and a trigger. The first carries a kill trigger: if an autonomous action causes a Sev-1 incident, autonomous mode pauses for all customers until the eval suite is extended and the champion approves a restart.
Horizons, not calendar years. Correlate and diagnose is Horizon 1, Beacon Agents with outcome pricing is Horizon 2, and the outcome-guaranteed service and partner marketplace are Horizon 3 options. Beacon Agents launches at Northlight Live this year, so its Horizon 2 metrics (adoption and revenue growth) apply now.
The review. Red-teaming the draft as the CFO, the harness flags that the learning goal has no date and the cannibalization of per-environment fees is not quantified. You add a date and a finance estimate, labeled as such. The CFO signs, with one change: the pilot must include at least two MSP-led customers, so the partner risk is tested in the same quarter as the price.
Product 360 artifacts
# Innovation placemat · [Bet name] · v[n] · [Date]
**Owner:** [PM] **Executive champion:** [Name, title] **Status:** Draft / Signed / In gate review / Scaled / Stopped
**Portfolio band:** Core / Adjacent / Transformational **Horizon:** 1 / 2 / 3
## 1. Strategic intent
- Corporate goal served (quoted from [strategy document, date]): "[goal]"
- Innovation thesis: Because the company is pursuing [corporate goal], we will innovate in [band and types] for [customers], measured by [outcome], funded at [target mix or amount].
## 2. Where we will play
- Ambition matrix cell: [where to play] × [how to win]
- Innovation types used (Ten Types): [e.g. profit model, network, product performance]
- Sustaining or disruptive? [and for which customers]
## 3. Customer and job
- Who: [personas and segment]
- Job and problem: [job story]
- Evidence we already hold: [interviews, data, pilots, with links]
## 4. Bets and hypotheses (ranked by risk)
1. [Assumption] · Type: desirability / viability / feasibility / adoption / organizational / regulatory / ecosystem · Evidence today: strong / some / none
2.
3.
## 5. Goals
| Element | Statement | Baseline | Target | Date | Source |
|---|---|---|---|---|---|
| Outcome goal | | | | | |
| Leading indicator | | | | | |
| Guardrail | | | must not fall below | | |
| Learning goal (this stage) | [assumption] confirmed or refuted with [evidence] | | | | |
## 6. Resources and horizon
- People: [roles and FTE]
- Money: [amount this tranche]
- Time box: [start to end]
- Funded from: [band and budget owner]
## 7. Executive champion
- Name and title:
- Why it matters to them: [their goal or metric]
- Decision rights: [what they decide alone]
- Cadence: [monthly check-in, quarterly gate]
## 8. Top risks and mitigations (from the risk register)
| # | Risk | Mitigation | Owner | Kill or pivot trigger |
|---|---|---|---|---|
| 1 | | | | |
| 2 | | | | |
| 3 | | | | |
| 4 | | | | |
| 5 | | | | |
## 9. Milestones and stage gates
| Gate | Date | Evidence required | Funding released | Decision options |
|---|---|---|---|---|
| Gate 1 | | | | Continue / Pivot / Stop |
| Gate 2 | | | | Scale / Pivot / Stop |
## Signatures
Team: [PM] [date] Champion: [name] [date]
## AI disclosure
Sections drafted with AI: [list]. Every number verified against source by [name] on [date].# Innovation ambition matrix · [Product line or portfolio] · [Quarter]
**Owner:** [PM or portfolio lead] **Target mix agreed with:** [champion] on [date]
## Grid (list each bet in its cell)
| How to win ↓ / Where to play → | Existing customers and markets | Adjacent customers and markets | New customers and markets |
|---|---|---|---|
| New products and assets | Adjacent: | Transformational: | Transformational: |
| Incremental products and assets | Core: | Adjacent: | Transformational: |
| Existing products and assets | Core: | Core: | Adjacent: |
Band boundaries vary by company; agree them with your champion and keep them stable between quarters.
## Bet list
| Bet | Cell | Band | Horizon (1/2/3) | Ten Types used | People (FTE) | Spend | Placemat link |
|---|---|---|---|---|---|---|---|
| | | | | | | | |
## Mix
| Band | Current share of effort | Target share | Gap | Action to close the gap |
|---|---|---|---|---|
| Core | | | | |
| Adjacent | | | | |
| Transformational | | | | |
## Notes
- What drives the current mix (e.g. a migration, an escalation, a commitment):
- Why the target mix fits our industry and position (not copied from 70-20-10):
- Bets moved between bands since last quarter, and why:# Executive champion brief · [Bet name] · [Date]
**To:** [Executive name, title] **From:** [PM] **Decision needed by:** [date]
## The ask (one paragraph)
We are asking you to champion [bet]. Specifically: [decision], [money], [people], [air cover on a named conflict], by [date].
## Why it matters to you
This bet moves [the executive's goal or metric] by [expected effect, with confidence level]. If it works, [what changes for their organization].
## What we have learned so far
- [Evidence 1, with source]
- [Evidence 2, with source]
- What we do not yet know: [riskiest assumption]
## What we need you to do
1. [Visible behavior, e.g. open the quarterly gate review]
2. [Blocker to remove, e.g. agree a pilot exception with sales operations]
3. [Coalition move, e.g. bring [peer executive] on as co-sponsor]
## What we are not asking for
- No solution design or scope changes without a trade-off discussion.
- [Anything else out of scope]
## Decision rights and escalation
- You decide: [gate outcomes, budget release, kill]
- The team decides: [scope, sequencing, experiments]
- Escalation path for conflicts with the core business: [name, forum]
## Cadence
Monthly 30-minute check-in; quarterly gate review; ad hoc escalations within [time].
## Kill and exit criteria (agreed now)
We will stop or pivot if [trigger 1], [trigger 2] or [trigger 3]. The decision is yours; the evidence will come from [source].
## Attachments
Innovation placemat v[n]; innovation risk register; ambition matrix for [portfolio].# Innovation risk register · [Bet name] · [Date]
**Owner:** [PM] **Reviewed with:** [champion, security, finance] **Review cadence:** monthly and at each stage gate
Categories: DES = desirability, VIA = viability, FEA = feasibility, ADO = adoption and change, ORG = organizational, REG = regulatory and trust, ECO = ecosystem and dependency.
Score = likelihood (1 to 5) × impact (1 to 5).
| ID | Risk statement (If [cause], then [effect] on [goal]) | Category | Likelihood | Impact | Score | Leading signal to watch | Mitigation | Owner | Kill or pivot trigger | Status | Last reviewed |
|---|---|---|---|---|---|---|---|---|---|---|---|
| R1 | | | | | | | | | | Open | |
| R2 | | | | | | | | | | | |
| R3 | | | | | | | | | | | |
## AI rows (map to NIST AI RMF functions)
| ID | Risk | Govern (policy, accountability) | Map (context, impact) | Measure (evals, monitoring) | Manage (response, controls) |
|---|---|---|---|---|---|
| | | | | | |
## Link to the placemat
The top five risks by score appear in box 8 of the placemat. Riskiest assumptions in box 4 that are not in this register: [list, or "none"].
## Changes since last review
| Date | Risk | Change | Evidence |
|---|---|---|---|
| | | | |# Ten types scan · [Product or bet] · [Date]
**Purpose:** widen the option set beyond product performance before choosing a bet.
| Category | Type | What we do today | Candidate move 1 | Candidate move 2 | Right to win? (Y/N, why) |
|---|---|---|---|---|---|
| Configuration | Profit model | | | | |
| Configuration | Network | | | | |
| Configuration | Structure | | | | |
| Configuration | Process | | | | |
| Offering | Product performance | | | | |
| Offering | Product system | | | | |
| Experience | Service | | | | |
| Experience | Channel | | | | |
| Experience | Brand | | | | |
| Experience | Customer engagement | | | | |
## Disruption lens
- Customers we overserve:
- Cheaper or simpler alternatives that could serve them (low-end):
- Non-customers who could now be served (new-market):
- Our response: defend / serve with a lighter offer / let go, and why
## Combinations worth testing
| Combination of types | Why they reinforce each other | Day's screen: Real? Win? Worth it? |
|---|---|---|
| | | |# Innovation goal set · [Bet name] · Stage: [discovery / pilot / scale]
## Outcome goal (SMART)
By [date], [segment] will [behavior or outcome] from [baseline] to [target], measured by [source]. Owner: [name].
## Leading indicator
[Metric that moves before the outcome], reviewed [weekly / monthly]. Expected direction: [up / down].
## Guardrails
- [Metric] must not fall below [threshold].
- [Metric] must not rise above [threshold].
## Learning goal for this stage
[Assumption] will be confirmed or refuted by [date], using [experiment or evidence]. Confirmed means [result]. Refuted means [result].
## Gaming check
If the team optimized only the outcome goal, what could go wrong? [answer]. Which guardrail prevents it? [answer].
## Stage logic
Early stage: the learning goal leads. Pilot: learning goal and leading indicator lead. Scale: the outcome goal leads. Guardrails apply at every stage.Prompts
Goal: Tie your innovation work to goals the company has already stated, in its own words.
Paste in (or connect the harness to): the company strategy document, the portfolio's annual plan and your product's last business review. These are confidential; use only a tool your company has approved for them.
From the documents provided, list every corporate or portfolio goal that [product] could plausibly move. For each goal give:
1. The exact wording, with document name and section.
2. The executive who owns it, if the documents say.
3. How [product] could move it, in one sentence, and through which kind of innovation (core, adjacent or transformational).
Then draft one innovation thesis for the two goals with the strongest link, in this form: "Because the company is pursuing [corporate goal], we will innovate in [band and types] for [customers], measured by [outcome], funded at [target mix]."
Rules: quote goals exactly. If a goal is implied rather than stated, label it "inferred." Use documented owners, numbers and targets. Mark missing items explicitly.
Check the output: open the source for every quoted goal and confirm the wording. Discard any thesis built on an "inferred" goal unless an executive confirms it.Goal: See how your current effort splits across core, adjacent and transformational work.
Paste in: the roadmap or initiative list with a one-line description of each item, approximate team allocation, and a short description of current customers and markets.
Classify each initiative for [product] on Nagji and Tuff's innovation ambition matrix.
- Where to play: existing customers and markets, adjacent ones, or new ones.
- How to win: existing products and assets, incremental additions, or new ones.
- Band: core, adjacent or transformational.
- Horizon: 1 (extend the core), 2 (build an emerging business) or 3 (create an option).
- Innovation types used, from the ten types.
Give a one-line reason for each classification. Then total the approximate share of effort in each band and list the five classifications you are least sure about.
Check the output: review every low-confidence classification yourself, and any item the model put in a different band from the one your team assumed. Those disagreements are the agenda for the portfolio conversation.Goal: Widen the set of candidate bets beyond product features, and find where you might be disrupted.
Paste in: a description of the product, its customers and pricing, the main competitors and alternatives, and any discovery evidence you hold.
For [product], propose two candidate moves for each of Doblin's ten types of innovation: profit model, network, structure, process, product performance, product system, service, channel, brand and customer engagement. For each move, say what right to win we would have, or say "none."
Then apply Christensen's disruption lens:
1. Which customer segments do we overserve, paying for capability they do not use?
2. What cheaper, good-enough alternatives could serve them (low-end), and who are non-customers that could now be served (new-market)?
3. For each, recommend defend, serve with a lighter offer, or let go, with a reason.
Finish by naming three combinations of types that would reinforce each other and be hard to copy.
Check the output: delete moves that only restate features already on the roadmap. Test the "right to win" claims with someone in sales or partnerships. Treat the disruption answers as hypotheses to check with customer data.Goal: Make it easy for the right executive to say yes, and clear what yes means.
Paste in: the draft placemat, the executive's stated goals or scorecard, and what you need (decision, money, people, air cover).
Draft a one-page executive champion brief for [executive name and title] for the bet [bet name]. Use these sections: the ask, why it matters to them (tied to their stated goals), what we have learned so far, what we need them to do (three specific behaviors), what we are not asking for, decision rights and escalation, cadence, and kill criteria agreed now.
Rules: link every claim to the executive's goals to a source document. Keep it under 450 words. Write the ask in the first paragraph. Match confidence to the evidence and list open questions.
Check the output: read it as the executive. Can you tell in thirty seconds what you are being asked for and by when? Are the three behaviors things they would really do? If the kill criteria are vague, rewrite them as observable triggers.Goal: Find the risks your company has already paid to learn about, before they repeat.
Paste in (or connect the harness to): the placemat, past post-mortems, cancelled-project reviews, win and loss notes and security review findings for similar work. Remove customer identifiers, and use an approved tool for confidential material.
Build an innovation risk register for [bet]. Cover all seven categories: desirability, viability, feasibility, adoption and change, organizational, regulatory and trust, and ecosystem and dependency.
For each risk give: a statement in the form "If [cause], then [effect] on [goal]," the category, likelihood and impact (1 to 5), a leading signal to watch, a mitigation, a suggested owner role and a kill or pivot trigger. For AI risks, map the mitigation to the NIST AI RMF functions (Govern, Map, Measure, Manage).
Cite the past document that suggested each risk, or mark it "new." Then list any riskiest assumption on the placemat that has no matching risk.
Check the output: merge duplicates and cut to the ten that matter. Confirm each cited source says what the model claims. Make sure every kill trigger is observable, not a judgment call.Goal: Turn a single target into a goal set that cannot be met by gaming it.
Paste in: the current goals for the bet, its stage (discovery, pilot or scale) and the riskiest assumptions.
Rewrite these goals for [bet] as a four-part goal set: a SMART outcome goal, a leading indicator, one or two guardrails and a learning goal for the current stage.
Then, for each outcome goal, describe how a team could hit it while harming customers or the business, drawing on the side effects of goal setting that Ordóñez and colleagues describe (narrow focus, unethical shortcuts, distorted risk taking). Propose the guardrail that prevents each.
Rules: use sourced baselines. Mark missing values "baseline needed" and name the report that should provide them.
Check the output: confirm every baseline against the source report. If the bet is early-stage and the learning goal is weaker than the outcome goal, swap their prominence.Goal: Find the weak points before your champion or a portfolio forum does.
Paste in: the full placemat, the risk register and the champion brief.
Review this innovation placemat three times, as three different reviewers:
1. A skeptical CFO: are the economics and the funding stages credible? What is missing from the viability case?
2. A security and compliance reviewer: what would you block, and what evidence would you need?
3. A competitor's head of product: how would you respond if this worked, and how fast?
For each reviewer, give the three strongest objections, ranked. Then list any box on the placemat that is vague, unfalsifiable or not tied to evidence, and any number that has no source.
Give a candid critique grounded in the supplied company and market evidence. Mark unsupported assumptions.
Check the output: answer each top objection in writing. Add any new risk to the register. If an objection cannot be answered, it belongs in box 4 as a hypothesis or in box 9 as a gate condition.Anti-patterns
Checklist
Key takeaways
Background
Established companies face constant pressure to spend on the core. Innovation bets need an explicit corporate purpose, protected resources and accountable sponsorship to survive that pressure. Different kinds and stages of bets also need different evidence, goals and funding decisions.
Idea in brief
Create a signed one-page placemat for each funded bet. Align it to strategy, select a deliberate portfolio mix and fund learning in stages. The team supplies evidence and risk reporting, while the champion provides resources and decisions.
Apply it
- Quote the corporate goal and write an innovation thesis naming the customer, innovation type, intended outcome and funding band. Screen candidates for reality, ability to win and worthwhile returns.
- Use Doblin’s ten types to explore profit model, network, process, service and channel opportunities alongside features. Examine customers you overserve and defensible assets you can reuse.
- Classify distance from the core with the ambition matrix and maturity with three horizons. Choose a justified spend mix. Treat 70-20-10 as a reference point.
- Name a champion whose goals the bet serves. Agree participation, coalition-building, decision rights and kill criteria before work starts. Define the partnership with the core business.
- Pair outcome goals with leading indicators, guardrails and learning goals. Early bets should prove assumptions. Use revenue and growth measures as evidence earns the right to scale.
- Keep an owned risk register with signals and stop or pivot triggers. For AI bets, include inference economics, evaluation quality, security and trust. Review evidence at every funding gate.