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Chief Operating Officer

Technology & Digital Operations

Enhances✓ Available Now

What You Do Today

Ensure technology supports operations — system reliability, automation adoption, digital workflow optimization. You're not the CIO, but you own the operational outcomes that technology enables.

AI That Applies

AI-powered operational technology monitoring that tracks system health, predicts capacity needs, and identifies technology-driven bottlenecks in operational workflows.

Technologies

How It Works

For technology & digital operations, the system tracks system health. Predictive models fit to historical outcome data identify which variables are the strongest leading indicators, then apply those weights to current inputs to generate forward-looking scores. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context. The technology investment decisions.

What Changes

Technology's operational impact becomes measurable. The AI shows that a system slowdown caused a 15% drop in processing throughput and predicts when capacity limits will be reached.

What Stays

The technology investment decisions. Choosing which operational technologies to adopt, how to integrate them, and how to manage the transition requires both technical understanding and operational expertise.

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 technology & digital operations, understand your current state.

Map your current process: Document how technology & digital operations 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 technology investment decisions. 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 IoT Analytics 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 technology & digital operations 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 technology & digital operations?

They shape expectations for how AI appears in governance

your CTO or CIO

Who on our team has the deepest experience with technology & digital operations, 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 technology & digital operations, 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.