A target operating model for operations that run on ServiceNow and AI
An operating model describes how your organization works across six dimensions.
We design all six for your commercial, customer and project operations, before a new system changes how those teams work and after it goes live. ServiceNow carries the workflows, Claude brings the intelligence, and the model decides who owns, runs, and improves both.
Designed by senior practitioners who also implement the platforms.
Not sure where to start? Roadmapping will get you started.
What is a target operating model?
A target operating model describes how an organization intends to work: ownership, decision rights, governance and how work flows. It typically covers six dimensions, from how teams are structured to how outcomes are measured. A useful one is specific enough that every team knows who decides, who does the work, and which systems carry it.
Business strategy × People × Processes × Technology × Data × AI
The six dimensions are where Value Multiplied™ becomes concrete. Value comes from business strategy × people × processes × technology × data × AI, so one weak dimension limits all the others. We design the six together, starting from the business strategy they serve.
Why technology and AI change the operating model
Most operating models were designed for work that people did and systems recorded. On ServiceNow with AI, systems route the work, prepare decisions, and, with agents, take actions. That changes all six dimensions at once. Adding AI to the old model automates work that should not exist.
So the right time to design it is before the system changes how your teams work. Roles, approvals and handoffs change when the system changes. Set up the organization late and the gaps surface as change requests and workarounds. Where the platform is already live, we start from how work and decisions actually run today and design forward.
A new system changes your operating model whether or not anyone designs it. The choice is whether you design it first.
How commercial, customer and project operations are structured
The organization dimension decides how teams and business units are structured around the work. When sales, service and delivery share one platform, that means how they connect, where the platform team sits, and whether it works centralized, federated or hybrid. We work with your existing structure rather than redrawing it.
Centralized
Keeps standards and build in one platform team.
Consistent, but a bottleneck once demand exceeds capacity.
Federated
Gives domain teams ownership within common standards.
Fast, but at risk of divergence.
Hybrid
Sets central guardrails and distributes the build.
The most common answer and the hardest to hold, because somebody has to maintain the guardrails.
Transformation Partner keeps the change coherent while it runs. Operating Model Design designs the ownership the organization keeps afterwards.
Who decides what, at what threshold
Governance defines who makes which decisions, at what threshold, on what evidence, and what happens next. On a shared platform the recurring decisions are predictable: process changes, configuration versus customization, new integrations, data ownership, AI use-case approval, release readiness and upgrade timing. If your model cannot say who makes each one, it is a diagram rather than an operating model.
Thresholds mean most decisions are settled by a standard rather than reviewed by a person. Authority sits at the lowest level that can be accountable for it, and forums exist only for what falls outside the standard.
A governance meeting is not a governance model. Governance only works when somebody in the room can actually make the decision.
How work flows across sales, service and delivery
The process dimension decides how work flows across the company and who owns each flow from end to end. In commercial, customer and project operations, value is usually lost at the handoffs: a quote that does not become a clean order, a case that moves between teams, a project that cannot see its margin. Each flow needs one owner across those handoffs.
- 01Requested
- 02Prioritized
- 03Released
The operating model does not redesign each process. It sets the pattern that makes ownership hold across all of them: who owns a flow, who can change it, and how a change is requested, prioritized and released. Every request gets a visible answer, so teams stop working around the system.
Business Process Consulting decides how work should flow and which systems support it. Operating Model Design decides who owns, governs and improves it.
The roles and skills the model needs
This dimension defines the roles and skills the model needs. On ServiceNow with AI, six accountabilities must exist somewhere: a business owner for each outcome, a platform owner, process owners, data owners, an architecture owner, and an owner for what AI may do. In the mid-market, one person often holds several. What fails is an accountability nobody holds.
A business owner for each outcome
A platform owner
Process owners
Data owners
An architecture owner
An owner for what AI may do
Platform ownership, process and data ownership, and the decision rights themselves.
Build capacity and specialist skills.
Some capabilities should stay in your organization. For each capability, the plan says internal, external or shared, when external becomes internal, and how knowledge actually moves. Your organization gets stronger with every phase.
ServiceNow, Claude and the data the model runs on
This dimension decides which systems and information enable the model. We design for two platforms working together: ServiceNow orchestrates the workflows, holds the system of record for the work and governs it, and Claude provides the intelligence that reads, reasons and drafts. Each significant business object gets a named data owner, separate from the system that stores it.
Orchestrates, records, governs
The workflows, the system of record for the work, and its governance.
Reads, reasons, drafts
The intelligence inside the work.
The model also sets how technology decisions get made:
Principles
that settle arguments: "configuration before customization"
Standards
each with an owner
A design authority
with thresholds
Decision records
that keep the platform upgradeable when people move on
Enterprise Architecture decides what the landscape should be and why. Operating Model Design decides who owns those decisions and how they get changed.
How outcomes are measured and managed
Performance management decides how outcomes are measured and who acts on them. Go-live is where accountability for value starts. Each measure needs an owner, a review rhythm, and permission to spend capacity on work nobody requested, such as technical debt and upgrade readiness. Without those, the roadmap becomes a request backlog.
The last row is the one almost nobody tracks, and it tells you whether the rest will keep improving.
Which decision keeps returning to the same meeting?
ServiceNow Center of Excellence and AI Center of Excellence: do you need one?
A Center of Excellence (CoE) holds the standards, decisions and expertise that every project would otherwise remake. A ServiceNow CoE earns its overhead when the platform serves several business functions, several teams build, or several entities share one instance. Below that, a named platform owner with a decision list and one standing review does the same job.
An AI Center of Excellence finds, assesses and reuses AI patterns, prompts and agent designs, so each team does not start from zero. For the mid-market, we recommend one combined capability: one set of standards and one architecture authority for platform and AI, with AI expertise added rather than duplicated.
A named platform owner
With a decision list and one standing review. It does the same job.
ServiceNow CoE
Earns its overhead when the platform serves several business functions, several teams build, or several entities share one instance.
AI CoE
Finds, assesses and reuses AI patterns, prompts and agent designs, so each team does not start from zero.
One combined capability
One set of standards and one architecture authority for platform and AI, with AI expertise added rather than duplicated.
AI governance itself runs on AI Control Tower. Its AI steward role brings discovered AI into governance, and in the mid-market it is usually somebody's second job. We design the role that way.
A CoE should enable delivery, not become another approval queue.
How Pulse & Patterns designs your operating model
We run Operating Model Design as a design engagement under its own agreement, with named deliverables and a decision log. Every engagement has a business owner who holds one of the numbers the model affects, so the design carries authority across sales, service, delivery and technology.
Scope and outcomes
What has to be true, and what causes the most damage today.
Current state
How each of the six dimensions works today, and what a coming system change will alter.
Target model
The six dimensions designed together and sized to your organization.
Capability and sourcing
What stays internal, what sits outside, and how knowledge moves.
Transition and cadence
The review rhythm and a sequenced path from today, because an operating model that arrives all at once does not arrive at all.
A model your organization can run
A target operating model across all six dimensions, ready to use.
The team and platform structure
A decision-rights matrix with thresholds
Process ownership and the change flow
Role definitions and a capability plan
Data ownership, architecture standards and decision records
A measurement set with owners
A Center of Excellence recommendation for platform and AI
A sequenced transition plan
The people who design your operating model also implement the platform
Operating models succeed or fail where decision rights meet technical choices. Our practitioners also implement ServiceNow and build on Claude, so the model maps onto choices your teams actually face. We build and run AI agents in our own operations and in client environments, so the roles we design around them come from practice.
Platform and AI, as one model.
As a ServiceNow Consulting & Implementation and Resell Partner and an Anthropic Claude Consulting & Implementation Partner, we design for both together.
Senior practitioners throughout.
Our founders and senior experts bring decades of transformation, process and platform experience. The people who design your model help you run it, from first workshop to go-live. No account layer, and no handover to a team you have not met.
Design the operating model before the system designs it for you.
Bring us the system change that is coming, or the decision that keeps returning to the same meeting. We will show you how your six dimensions look today, where the gaps are, and whether the answer is a new model, a smaller one, or three accountabilities nobody has assigned yet.
The questions buyers ask us.
What should a target operating model contain?
A target operating model should be specific enough to run. It names the owner of each outcome, process, data object and AI capability. It lists the recurring decisions with their thresholds and the evidence each needs. And it sets the team structure, the roles, the systems and the measures. Every element should answer two questions: who does this, and who decides about it.
What is the difference between a current and a target operating model?
The current operating model is how your organization actually works today, including the informal routes. The target operating model is how it intends to work. The gap between them becomes the transition plan, so the current state has to be described honestly.
What is a ServiceNow operating model?
A ServiceNow operating model defines how an organization owns, governs and evolves its ServiceNow platform: platform ownership, demand intake, configuration and integration standards, architecture authority, release governance, capability building and value accountability. It matters most once ServiceNow expands beyond IT, because service desk governance rarely fits commercial or customer operations.
How do AI agents change roles in the operating model?
AI agents hold permissions and take actions, so they add accountabilities rather than only tasks. People move from doing a step to supervising it: reviewing proposals, handling exceptions and deciding when the agent may act alone. Someone must own what each agent may do and be accountable when it is wrong, before the agent goes live.