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Digital Strategy Leader

Digital Operating Model Design

Enhances◐ 1–3 years

What You Do Today

You define how the organization structures itself to deliver digital value — team topologies, funding models, delivery methodologies, and the boundary between centralized and federated capabilities.

AI That Applies

AI analysis of organizational network data that maps communication patterns, decision bottlenecks, and collaboration gaps to recommend structural changes.

Technologies

How It Works

For digital operating model design, the system draws on the relevant operational data and applies the appropriate analytical models. 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 — structural changes — surfaces in the existing workflow where the practitioner can review and act on it. The human architecture.

What Changes

You get data-backed evidence for org design decisions. AI can show you where information flows break down and where teams are duplicating effort.

What Stays

The human architecture. Org design isn't a math problem — it's about people, culture, power dynamics, and what your specific leaders are capable of managing.

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 digital operating model design, understand your current state.

Map your current process: Document how digital operating model design 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 human architecture. 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 Network Analysis 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 digital operating model design 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 digital operating model design?

They set the strategic priority for transformation initiatives

your CTO or CIO

Who on our team has the deepest experience with digital operating model design, 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 digital operating model design, 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.