Critical information lives in personal folders and inboxes.
What it costs: Work stalls when one person is out, and nobody can say with confidence which version is current.
Capability
Technology implementation
Get more from the systems, data, and automation your team already pays for, and give leadership a governed approach to AI, with use cases, controls, and results measured against the workflows they change.
When this is the work
Signs your systems need attention.
Each of these has a cost that rarely shows up in a budget line. If several sound familiar, the problem is structural, and more effort from the team will not fix it.
Spreadsheets are doing the system's job.
What it costs: Re-keying, version conflicts, and different teams reporting different numbers for the same thing.
Monthly reports are rebuilt by hand.
What it costs: Senior people spend days assembling data instead of acting on it.
AI tools are being approved without a view of the process.
What it costs: Licenses bought and pilots launched, with no measurable change in how the work gets done.
No one can say which system is the record.
What it costs: Decisions wait while teams reconcile their numbers.
Automations exist, but no one owns them.
What it costs: A flow fails quietly and the work falls through until a client or executive notices.
What the research shows
Returns come from redesigning the work.
Recent research from McKinsey and Gartner points in the same direction: organizations that get measurable returns from AI and automation change how the work is done first.
3 in 4
Nearly three in four AI high performers report fundamentally redesigning workflows. Among other organizations, the share is about one in four.
McKinsey, The State of AI, August 202637%
Organizations that attribute any EBIT impact to their use of AI, roughly unchanged from the year before, even as use keeps growing.
McKinsey, The State of AI, August 202640%+
Agentic AI projects Gartner predicts will be canceled by the end of 2027, citing rising costs, unclear business value, and inadequate risk controls.
Gartner, press release, June 2025That is why every engagement starts with how the work actually runs. Automating a step that is not yet standard only makes the inconsistency faster.
Our approach
Process, then automation, then AI.
Automation that skips the first three steps rarely holds. We confirm the foundations are in place, fix the ones that are not, and automate only what is stable enough to hold. AI is applied where a step is measurable and a person can review the result.
| Tool-first projects | Process-first, our approach | |
|---|---|---|
| Starts with | A product demo | How the work actually runs today, including the exceptions |
| Success is measured by | Licenses deployed and go-live dates | Cycle time, error rates, hours returned, and adoption |
| AI is used | Wherever it can be added | Where the step is stable, measurable, and a person reviews the result |
| After launch | Ownership defaults to IT, or to no one | A named business owner, a runbook, and measures to watch |
| Typical result | A new system running the old process | Fewer steps, fewer handoffs, and numbers leadership trusts |
What we build
What the work can include.
Every engagement is scoped in writing. Most combine two or three of these, sized to what your team needs and can maintain.
Requirements and system selection
Real scenarios, volumes, and exceptions written down before anyone sees a demo, so you can compare vendors on the same terms and hold them to what was promised.
Workflow automation
Intake forms, routing, approvals, reminders, and status updates automated in the platforms you already license, with a named owner for each flow.
Reporting and dashboards
Reports that refresh on their own, with one agreed definition for each measure and a clear line from the number to the decision it supports.
Information architecture and records
Repositories, naming, permissions, and retention that make the current version easy to find and the record defensible to keep.
AI readiness and use-case selection
An inventory of where AI could help, scored on value, risk, and data readiness, narrowed to the few use cases worth piloting first.
AI governance and acceptable use
A usable policy, review steps, and clear roles, aligned to recognized frameworks such as the NIST AI Risk Management Framework and sized to your organization.
Data handoffs between systems
Where systems need to share data, the fields, owners, timing, and exception handling agreed and documented before anything is connected.
Adoption, training, and ownership
Role-based guides, short training, and an owner for every workflow and report, so what launches keeps working after the project ends.
Measurement after launch
Baseline measures taken before the change and tracked after it: cycle time, error rates, hours returned, and adoption.
Where it shows up
Common workflows by function.
The same discipline applies across the organization. These are the workflows that most often return time once they are designed and automated properly.
Legal and compliance
- Contract request intake and routing
- Matter, deadline, and obligation tracking
- Outside counsel invoice review workflow
Finance and investment operations
- Board and committee materials assembly
- Recurring management reporting
- Approval and signature routing
Operations and shared services
- Service request intake and triage
- Vendor onboarding and renewals
- Procedures and knowledge base
People and HR
- Onboarding and offboarding checklists
- System access requests and reviews
- Policy acknowledgments
Executive office
- Leadership meeting preparation and actions
- Priority and commitment tracking
- Decision logs
Federal programs
- Contract deliverable tracking
- Monthly status reporting
- Risk, issue, and action logs
How a build runs
Phases and outputs.
Map
Trace the workflow with the people who run it: steps, volumes, exceptions, and where the data comes from.
OutputCurrent-state map and baseline measuresDesign
Agree the future-state workflow, requirements, controls, and who owns it before anything is configured.
OutputSigned-off design and requirementsBuild and test
Configure in your tools and test against real cases, including the exceptions that break most automations.
OutputWorking build and test recordLaunch and hand over
Train the team, monitor through an agreed stabilization period, and hand over documentation and ownership.
OutputRunbook, owner, and measures in use
Results
Selected results.
Near real time
Executive decision-support reporting built in SQL, Power BI, and Tableau, replacing reports that previously ran several days behind.
~40%
Faster onboarding after knowledge, documentation, and workflows were reorganized, with information retrieval cut from days to minutes.
Ways to engage
Start where it makes sense.
Start here
Operating Diagnostic
Three weeks and a fixed fee. A clear account of what is slowing the work down, what it costs, and an action plan with owners.
How it works Defined engagementScoped project
A written scope, agreed deliverables, and acceptance criteria. Delivered with your team and handed over when it is running.
Discuss a project OngoingFractional leadership
Need senior ownership of this work now? Bring in a fractional AI and automation program lead on a defined mandate, with a clear reporting line and a planned handover.
Fractional rolesQuestions
Business systems and automation questions.
Do we need new software to automate our work?
Usually not at the start. Many organizations already license capable tools, often within Microsoft 365, that are underused because the process behind them was never defined. We map the work first and use what you own wherever it fits. If a new system is justified, you will have written requirements before any purchasing decision.
Which platforms do you work in?
Most often Microsoft 365, Power BI, Tableau, and SQL-based reporting. The approach is platform neutral: requirements come first, and configuration is coordinated with your IT team and any vendors involved.
How do you handle AI risk and confidential information?
Before any pilot, each AI use case is reviewed for the data it touches, who checks its output, and what happens when it is wrong. Policies are aligned to the NIST AI Risk Management Framework and to your own legal, privacy, and security requirements. Client information is only used in tools your organization has approved.
How does technology implementation work with our IT team?
IT owns the platforms, security, and access. We work on the business side of the process: requirements, workflow design, testing with real cases, training, and adoption. Responsibilities are written into the statement of work so nothing falls between teams.
What happens after a system or automation launches?
Every workflow and report has a named owner, a runbook, and measures to watch. An agreed stabilization period covers the first weeks of use. Ongoing support, including a fractional AI and automation program lead, can be scoped separately.
Not sure this is the right starting point?
The Operating Diagnostic is a three-week, fixed-fee look at how the work actually runs. It tells you whether the fix is a process, a decision, a system, or all three, and gives you an action plan either way.