Chief Digital Officer
Digital Product Strategy
What You Do Today
You define and prioritize the digital products and platforms the organization takes to market — mobile apps, self-service portals, digital marketplaces, API-based services. You decide what to build, what to buy, and what to sunset.
AI That Applies
AI-driven product analytics that identify usage patterns, feature adoption, and customer friction points across digital products, enabling data-informed prioritization.
Technologies
How It Works
The system tracks product usage data — feature adoption, user flows, error rates, and engagement patterns. Machine learning models identify the patterns in historical data that most strongly predict the target outcome, then apply those patterns to score new inputs. The output — data-informed prioritization — surfaces in the existing workflow where the practitioner can review and act on it. The product vision.
What Changes
Product decisions get faster when AI surfaces what customers actually use versus what they say they want. Feature prioritization shifts from stakeholder opinions to behavioral data.
What Stays
The product vision. Deciding what your digital portfolio should look like in three years, which bets to make on emerging channels, and how to differentiate digitally — that requires market judgment and strategic courage.
What To Do Next
This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.
Establish Your Baseline
Know where you are before you move
Before adopting AI tools for digital product strategy, understand your current state.
Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.
Define Your Measures
What to track and how to calculate it
Time per cycle
How to calculate
Measure how long digital product strategy takes end-to-end today, then after AI adoption.
Why it matters
The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.
Quality of output
How to calculate
Track error rates, rework frequency, or stakeholder satisfaction scores before and after.
Why it matters
Speed without quality is just faster mistakes. Measure both.
Start These Conversations
Who to talk to and what to ask
your board chair or lead independent director
“What data do we already have that could improve how we handle digital product strategy?”
They shape expectations for how AI appears in governance
your CTO or CIO
“Who on our team has the deepest experience with digital product strategy, and what tools are they already using?”
They own the technology infrastructure that enables AI adoption
a peer executive at a company further along on AI adoption
“If we brought in AI tools for digital product strategy, what would we measure before and after to know it actually helped?”
Their lessons learned are worth more than any consultant's framework
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.