AI Operating Model Assessment
Review workflows, decision bottlenecks, data flows, tools, governance, and AI readiness to identify where redesign will create the most value.
AI operating model advisory
Redesign work, decisions, and operations for the AI era.
Stratimetrix helps companies move beyond slow approval chains and legacy workflows toward AI-enabled operating models with faster decisions, clearer controls, and more resilient execution.
Legacy pattern
Work moves through meetings, inboxes, and serial approvals before anyone can act.
Target pattern
AI workflows sense signals, prepare decisions, route exceptions, and keep people focused on judgment.
Speed
Fewer coordination delays
Control
Review and escalation built in
Learning
Feedback loops that improve over time
The challenge
AI is reducing the cost of execution, but many companies are still organized for a slower era.
Many initiatives fail because new tools are dropped into workflows built around meetings, handoffs, and human bottlenecks. The real opportunity is to redesign the work itself.
What changes
- Execution costs fall when routine analysis and coordination can be automated.
- Decision quality improves when teams get better signal, context, and prepared options.
- Competitiveness erodes when operating models still depend on slow approval chains.
Who we help
Built for leadership teams that need practical change, not AI theater.
Especially relevant for companies with 50 to 1,000 employees that are exposed to AI disruption but do not have large in-house transformation teams.
CEOs and COOs facing pressure to move faster with leaner teams
Transformation leaders who need practical operating-model redesign, not AI theater
Functional heads in businesses with 50 to 1,000 employees and limited internal AI capacity
Services
Five focused services for operational AI transformation.
From assessment through pilot delivery and product implementation, the work is structured to create clarity, control, and measurable progress.
Agentic Workflow Design
Redesign priority workflows so AI agents and automation can support sensing, analysis, decision preparation, execution, and learning.
AI Governance & Human Oversight
Define review queues, audit trails, escalation paths, approval rules, and risk boundaries so faster execution does not come at the cost of control.
Rapid AI Pilot Delivery
Launch focused pilots in 30 to 60 days around high-value workflows, proving value with measurable outcomes before broader rollout.
Product Management
Do you have a new AI product in mind? We can help you prioritise, build, and implement transformative AI products with clear commercial direction, product leadership, and operational fit.
Approach summary
Start narrow, redesign the workflow, govern it properly, then scale.
The consulting approach is designed to help leadership teams move from vague AI ambition to disciplined operating change.
Phase 1
Diagnose
Identify high-margin, high-friction, or disruption-exposed workflows. Map approvals, handoffs, rework, and decision delays.
- Find where execution is expensive and response time matters most
- Expose approval chains and hidden coordination costs
- Prioritize workflows with both operational and strategic upside
Phase 2
Redesign
Reimagine the workflow around intelligence, automation, and human judgement. Decide where AI should sense, analyze, recommend, act, or escalate.
- Separate judgment from routine coordination work
- Design clear roles for agents, automation, and human operators
- Reduce meetings and status chasing with structured workflow signals
Phase 3
Govern
Add human review, audit logs, rollback plans, approval thresholds, and risk controls so AI supports responsible decisions.
- Set boundaries for confidence, exception handling, and escalation
- Create oversight mechanisms that fit the workflow rather than slow it back down
- Preserve accountability with traceability and clear ownership
Phase 4
Pilot
Build a narrow, measurable pilot around one workflow or function and test it with real users and real business data where appropriate.
- Keep scope tight enough to learn quickly
- Measure speed, quality, risk, and user adoption
- Refine operating rules before broader rollout
Use cases
Examples of workflows where redesign can create real operating leverage.
The goal is not to automate everything. It is to improve the speed, structure, and quality of work where delay and coordination costs matter most.
Competitive intelligence and market response
Monitor market shifts, summarize signals, prepare response options, and route exceptions to leaders who need to make judgment calls.
Sales pipeline triage and proposal support
Improve qualification, prioritize next actions, support proposal drafting, and reduce delay between opportunity signal and commercial response.
Customer service escalation and knowledge workflows
Structure escalation paths, automate knowledge retrieval, and ensure sensitive cases move to the right human owners at the right time.
Finance operations and invoice processing
Reduce manual handling, route exceptions intelligently, and strengthen auditability across recurring finance workflows.
London-based, globally oriented
We help organisations of all sizes that need practical advisory support.
Business strategy plus IT perspective
The work connects operating priorities with the systems, data, and controls needed to execute them well.
Built around controlled execution
Automation is paired with human judgment, oversight, and measurable operating rules rather than unconstrained experimentation.
Why act now
The risk is not just missing a tool. It is keeping an operating model that becomes too slow to compete.
Companies that respond well do not start with enterprise-wide transformation programs. They choose one valuable workflow, redesign it, pilot it with discipline, measure the outcome, and expand from there.
What acting now creates
- Faster operating response without surrendering accountability.
- Clearer governance before ad hoc automation spreads across teams.
- Internal capability that compounds rather than one-off experimentation.
- Stronger product direction for firms ready to turn AI capability into usable products and implemented outcomes.
FAQ
Common questions from leadership teams
A few practical points that often come up in early conversations.
Where should a company start? +
Start with one workflow that matters commercially, has visible friction, and can be measured. A narrow pilot creates evidence, operating patterns, and internal confidence faster than a broad AI program.
Do you replace teams with AI? +
No. The focus is redesigning how work moves so people spend less time on coordination, rework, and routine analysis, and more time on judgment, exceptions, and improvement.
What makes this different from tool implementation? +
Tools matter, but most bottlenecks sit inside operating design. We look at decisions, handoffs, controls, and management rhythms so AI fits the way the business actually runs.
How long does a first pilot take? +
A first pilot is typically scoped for 30 to 60 days, depending on workflow complexity, data readiness, and stakeholder availability.
Choose one important workflow and make it work better.
A focused assessment or pilot is often enough to show where AI-enabled operating redesign can create practical value.