Skip to content

Chief Technology Officer

R&D & Emerging Technology

Enhances✓ Available Now

What You Do Today

Lead R&D investments — prototyping new capabilities, evaluating emerging technologies, and building proof-of-concepts that might become the next product feature.

AI That Applies

AI-assisted prototyping that accelerates proof-of-concept development, automated benchmarking of emerging tools and frameworks, and research synthesis from academic and industry sources.

Technologies

How It Works

The system ingests academic and industry sources as its primary data source. A language model processes the input by identifying relevant context, generating appropriate responses, and structuring the output to match the expected format and domain conventions. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context. The R&D direction.

What Changes

Prototyping accelerates. The AI generates initial code, identifies relevant open-source components, and benchmarks approaches against alternatives.

What Stays

The R&D direction. Choosing which bets to make, how much to invest, and when a prototype is ready for production requires technical judgment and business awareness.

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 r&d & emerging technology, understand your current state.

Map your current process: Document how r&d & emerging technology 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 R&D direction. 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 AutoML 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 r&d & emerging technology 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 r&d & emerging technology?

They shape expectations for how AI appears in governance

your CTO or CIO

Who on our team has the deepest experience with r&d & emerging technology, 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 r&d & emerging technology, 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.