Strategy and growth. Chief Innovation Officer
AI for the Innovation Office
You’re not short of ideas. You’re short of decisions.
Most innovation teams run more pilots than they can ever scale. Scouting happens by hand, business cases rest on guesses and pilots drift on because nobody set the date to decide. We map the Innovation Office the way your team actually works, then put agents where they speed up finding, testing and deciding.
The short answer
It helps you find, test and decide faster. Agents scan markets, start ups, patents and technology every day, build first business cases from real data, track every pilot against clear kill criteria and keep each bet linked to a business owner.
Your team keeps the curiosity and the judgment. The agents do the searching and the tracking, so more of the good ideas reach the business and fewer pilots run forever.
The mandate
Find the next growth engine before a competitor does. Then prove it, fast, and hand it to the business.
What the board measures you on
Validated bets
Ideas proven with real data
Idea to pilot time
How fast a bet gets tested
Pilots decided on time
Scaled or stopped, not left running
New revenue share
From offerings launched recently
Return on innovation spend
What the budget actually produced
Handover to the business
Bets with an owner who runs them
The canvas of the Innovation Office
Innovation is often judged on how many ideas it explores. The board judges it on what made it into the business.
Canvas 1
What you run
The pipeline of new bets.
Scouting and foresight
Markets, technology, start ups, patents and trends.
Idea pipeline
Ideas from employees, customers, partners and research.
Experiments and pilots
Tests, prototypes and proofs of concept.
Ventures and partnerships
Start up deals, corporate venturing and joint pilots.
Innovation budget
How money gets spread across bets and stages.
Canvas 2
What you answer for
The things the CEO and the board hold you to.
Return on the portfolio
Which bets turned into revenue, savings or capability.
Stage gates and kill decisions
Clear criteria, kept to on time.
Partner and IP terms
Who owns what from every pilot and partnership.
Responsible experimentation
Data, safety and AI rules in every test.
Where value leaks
MIT’s NANDA study found that 95% of generative AI pilots showed no measurable impact on profit and loss. The ideas are rarely the problem. The leaks below usually are.
01
Scouting done by hand
Analysts search databases and newsletters and still miss what matters.
Scouting agents. Scan funding news, patents, research and launches every day against your themes and brief the team on what’s new.
Signals reviewed
Time to spot
02
Pilots that never end
Nobody set the criteria or the date, so pilots drift on.
Pilot agents. Track every pilot against kill criteria and a decision date set under our Validation First Framework, and raise it when the date arrives.
Pilots decided on time
Spend on stalled pilots
03
Business cases built on guesses
Market size and value get estimated in a spreadsheet the night before.
Case agents. Build first business cases from market, customer and internal data, with every assumption sourced.
Time to first case
Case against actual
04
Ideas lost between lab and business
A pilot works, but no business unit is ready to own it.
Tracking agents. Keep every bet linked to a business owner from day one and flag the ones with nobody to hand over to.
Bets with an owner
Pilots scaled
05
The same experiment twice
Two business units test the same idea with two vendors.
Portfolio agents. Read every pilot brief across the company and flag overlap before the money is spent.
Duplicate spend
Shared learning
06
Start up deals that stall
Proofs of concept wait months for contracts, data access and security checks.
Partnership agents. Prepare due diligence, track contract and security steps and chase the blockers.
Time to contract
Partner pilots started
Process lifecycles
We start from the tools and rhythm you already have. The orange steps are where an agent takes on the work, so your team spends its time on the bets, not the admin around them.
Scout to shortlist
Every day
Signal
Funding, patents, launches
Agent
Screen
Against your themes
Agent
Shortlist
With a short brief
Agent
Meet
Decide
Typical systems. Crunchbase, CB Insights, patent databases, research feeds
Idea to scale
Every bet
Idea
Case
Built from data
Agent
Pilot
Track
Against kill criteria
Agent
Decide
Handover
To a business owner
Agent
Typical systems. Idea management tools, Jira, Miro, finance systems
Start up partnership
Every partner pilot
Find
Assess
Due diligence prepared
Agent
Contract
Steps tracked and chased
Agent
Proof of concept
Integrate or exit
Typical systems. CRM, contract management, security review tools
Portfolio review
Quarterly
Measure
Every bet, every stage
Agent
Compare
Overlap and return
Agent
Fund, fix or stop
Report to the board
Typical systems. Portfolio tools, finance systems, BI
Agent intervention point
The Innovation control tower
Not a slide of logos and pilots. A live view of what’s in the funnel, what’s overdue for a decision and what has actually reached the business.
Illustrative
Innovation control tower
Sample data to show the view. Not client results.
Bets in the funnel
48
▲ 6 this quarter
Idea to pilot
7 wks
▼ from 19 wks
Pilots overdue
3
Past decision date
Stalled spend
€0.8m
On overdue pilots
Bets with an owner
82%
▲ 21 pts
New revenue share
6.4%
▲ 1.1 pts
This week’s innovation brief
Decide
Route optimisation pilot passed its decision date
Hit 2 of 3 kill criteria. Scale or stop at Thursday’s review
Overlap
Two units are testing document AI with different vendors
Combined spend €140k. Portfolio agent
Scout
Four new start ups match the circular packaging theme
Briefs ready. Two raised funding this month
Handover
Pricing pilot moves to the CRO Office to scale
Owner named and budget agreed
Bets by stage
Idea
31
Validate
9
Pilot
6
Scale
2
Only 2 bets at scale this year. Three pilots could join them if decided this month.
Illustrative example. Figures are invented to show how the view works. In discovery we build it on your own data and KPIs.
Your industry changes the agenda
The canvas stays the same. Where the next growth engine might come from does not. Here’s the question we hear most from innovation leaders in each industry, and where agents help first.
CPG
Which new categories and formats are worth a real bet?
Trend and start up scouting by category
FMCG
How do we test new products faster and cheaper than a full launch?
Concept tests and early demand signals
Retail
Which new store and service formats should we pilot next?
Format pilots tracked against criteria
D2C
What’s our next product line or market before growth slows?
Market sizing and adjacent category scans
Automotive
Which software, energy and mobility bets deserve real money?
Technology scouting and partner pilots
Healthcare
How do we test new care models without risking patient safety?
Evidence reviews and pilot governance
Energy
Which new energy technologies and services should we back now?
Technology and regulation scanning
Manufacturing
How do we move from smart factory pilots to every plant?
Pilot tracking and handover to operations
Logistics
Which automation and digital services pay back first?
Start up scouting and business cases
BFSI
How do we partner with fintechs without slowing to a crawl?
Partner due diligence and contract tracking
B2B technology
How do we find the next product before AI makes ours a feature?
Competitive and technology scanning
How the Innovation Office connects
Innovation only pays when a bet leaves the lab and lands somewhere that can run it. The agents that serve the Innovation Office share the same platform and data as the offices around it, so a winning pilot has a clear path to scale.
How we’d start
Pilot 1
Every live pilot, decided
↳
All pilots running today listed with owner, spend and stage
↳
Kill criteria and a decision date set for each one
↳
A scale, fix or stop recommendation, with the evidence
Your decision
Which pilots to scale, and which to stop funding this quarter.
Pilot 2
Scouting agents on your themes
↳
Two or three themes that matter to your strategy
↳
Daily scans of start ups, patents, research and competitor moves
↳
A weekly shortlist with a short brief on each company
Your decision
Which companies to meet, and whether to scout every theme this way.
Both run under our Validation First Framework. They’re tested on your own data before you commit a larger budget, and if the numbers don’t hold, we tell you.
Your intervention map
Bring every pilot you’re running and the bet you most want to prove. We bring the Innovation Office canvas. In one session we map it to your portfolio and mark where agents would pay back first.
01
Your Innovation Office canvas
What you run and answer for, checked against how your team, partners and business units really work.
02
Your value chain
The canvas placed on your industry and the parts of the business your bets need to land in.
03
Your intervention points
Each leak with an agent design, its controls and the KPI it moves. Delivered as a mind map, an architecture view or an interactive dashboard.
AI for the Innovation Office, answered
What can AI do for the Innovation Office?
Agentic AI helps the Innovation Office find, test and decide faster. Agents scan markets, start ups, patents and technology every day, build first business cases from real data, track every pilot against clear kill criteria and keep each bet linked to a business owner. Fewer ideas get lost, and fewer pilots run forever.
How do you get AI pilots out of pilot purgatory?
By agreeing before the pilot starts what success looks like, who owns the result and when you decide. Our Validation First Framework sets kill criteria, a business owner and a decision date for every pilot. Agents then track each pilot against those criteria, so the decision happens on time.
Can AI help with technology and start up scouting?
Yes. Scouting agents scan funding news, patents, research, product launches and competitor moves against the themes you care about. They shortlist what matters and write a short brief on each one, so your team spends its time meeting the right companies instead of searching for them.
How should innovation be measured?
By decisions and outcomes, not activity. Useful measures are time from idea to pilot, the share of pilots that reach a scale or stop decision on time, the cost of stalled pilots and the revenue that comes from offerings launched in the last three years.
How is the Innovation Office different from the Chief AI Office?
The Innovation Office looks for new growth, with or without AI. The Chief AI Office runs the AI platform, governance and value tracking across the company. They work best together. Innovation finds and tests the bets, and the Chief AI Office helps the winners scale safely.
Where should an Innovation Office start with agentic AI?
With the pilots you already run. Put every live pilot against clear criteria and decide which to scale and which to stop. It frees budget within a quarter. Then add scouting agents on two or three themes that matter to your strategy.
