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VP of Engineering

Innovation & R&D

Enhances◐ 1–3 years

What You Do Today

Create space for innovation — hackathons, R&D sprints, technology exploration, and the time for engineers to experiment with ideas that might become the next product breakthrough.

AI That Applies

AI-powered innovation scouting that identifies emerging technologies relevant to your product, surfaces academic research applicable to your challenges, and tracks competitive R&D investment.

Technologies

How It Works

The system ingests competitive R&D investment as its primary data source. NLP models process the text input by identifying entities, classifying intent, and extracting the structured information needed for downstream decisions. The output — academic research applicable to your challenges — surfaces in the existing workflow where the practitioner can review and act on it. The innovation culture.

What Changes

Technology scouting becomes systematic. The AI surfaces relevant research papers, open-source projects, and competitive technical moves that might inform your R&D direction.

What Stays

The innovation culture. Giving engineers permission to explore, protecting R&D time from delivery pressure, and recognizing breakthrough ideas — that's leadership that enables innovation.

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.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for innovation & r&d, understand your current state.

Map your current process: Document how innovation & r&d works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: The innovation culture. These are the boundaries AI won't cross.
Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support NLP tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long innovation & r&d 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.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

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 innovation & r&d?

They shape expectations for how AI appears in governance

your CTO or CIO

Who on our team has the deepest experience with innovation & r&d, 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 innovation & r&d, what would we measure before and after to know it actually helped?

Their lessons learned are worth more than any consultant's framework

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.