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Digital Strategy Leader

Emerging Technology Evaluation

Enhances✓ Available Now

What You Do Today

You separate hype from value — evaluating new technologies against actual business problems, running proofs of concept, and deciding what's worth a bet versus what's a distraction.

AI That Applies

AI-curated technology intelligence feeds that filter vendor noise, track academic research, and map emerging technologies to your specific industry use cases.

Technologies

How It Works

The system ingests academic research 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 results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context. The 'so what.

What Changes

The signal-to-noise ratio improves. AI filters the firehose of vendor pitches and analyst reports down to what's actually relevant to your strategy.

What Stays

The 'so what.' Knowing a technology exists is easy. Knowing whether it solves a real problem for your customers or operations — and whether your organization can actually adopt it — requires deep business context.

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 emerging technology evaluation, understand your current state.

Map your current process: Document how emerging technology evaluation 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 'so what. 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 emerging technology evaluation 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 CEO or executive sponsor

What data do we already have that could improve how we handle emerging technology evaluation?

They set the strategic priority for transformation initiatives

your CTO or CIO

Who on our team has the deepest experience with emerging technology evaluation, and what tools are they already using?

They own the technology capability that enables your strategy

the leaders of the business units you're transforming

If we brought in AI tools for emerging technology evaluation, what would we measure before and after to know it actually helped?

Their buy-in determines whether your strategy actually gets implemented

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.