Operations leaders

What are we doing with AI, and what has it saved?

For COOs and heads of operations at organisations of 50 to 500 people, and at multi-site groups such as clinic groups, franchises and property portfolios.

The question you are asked

iSystematic works with operations leaders who are asked what the organisation is doing with AI, and what it has saved. An honest answer needs a baseline taken before anything is built, and a record of who approved each system.

Where most start

Most start with the Automation Assessment: two weeks, fixed fee, ten candidate tasks scored on frequency, effort, data sensitivity and risk, three recommended solutions, and a baseline measurement plan.

A different start fits two cases. Healthcare groups touching patient information take the same Assessment with a privacy review of each candidate task before design. If a board is asking whether AI is under control, the readiness route goes first.

What we deliver

One path, and you can stop after any step.

01

Assessment

Two weeks. Ten candidate tasks scored, three recommended solutions with their levels, and a baseline measurement plan.

02

Pilot

Six weeks. One solution live for one team, measured before and after: task time including review, acceptance, corrections, escalations, duplicates, failures and cost. See Pilot.

03

Build

The solution in production with its gate records. Scoped after the pilot.

04

AgentOps

Monthly: monitoring, evaluation re-runs, vendor and model change checks, a monthly note, and retirement of unused agents. See AgentOps.

What you hold at the end: a fictional sample

This fictional extract from an Automation Assessment report is for a fictional clinic group with five sites.

Candidate taskHow oftenEffortData sensitivityRiskRecommendation
Answering repeat patient questions across five sitesDailyHighPatient informationMediumS3 Front Door Agent, after a privacy review
Weekly site operations briefing for the COOWeeklyMediumBusiness confidentialLowS1 Leadership Briefing Desk
Chasing missing intake formsDailyMediumPatient informationMediumS4 Intake to Onboarding
Assembling the monthly board packMonthlyHighBusiness confidentialMediumNot yet: the inputs have no named owners

Fictional. A full report scores ten tasks and adds the level for each recommendation and a baseline measurement plan.

One gate record, fictional

This fictional approval record is for the briefing desk recommended above.

FieldEntry
GateG3 Approval
SystemLeadership briefing for a fictional clinic group
Named approverThe chief operating officer
Evidence relied onThe G2 validation report, the approved source register, and the spend cap with its log
DecisionApproved for one team, with a review date

What it is built on

Every solution passes through the same lifecycle and leaves the same records, so a later governance review finds what it looks for. The full stack is on How we build, and the method in full is in the books.

FrameworkIn this workYou keep
Five-Gate Deployment Model™Every system moves through five gates, from G1 Data and Design to G5 Operation, each signed by a named person.Five gate records, each naming a person and the evidence relied on
BOE DeclarationEach control is declared as its boundary, its optimiser and the evidence that it held.One BOE Declaration per control

Buying from us

You contract with iSystematic Inc., in Canada. Build work is delivered by the studio's team of 10+ expert builders; enterprise readiness work is led by Nabeel Khan personally.

First steps, such as the Automation Assessment and a Pilot, are fixed fees, shared on a short call. A build is scoped after the pilot, and AgentOps is monthly.

What the research says

These are published studies, not our results. Ours will come from documented pilots.

25.1% faster
Consultants using GPT-4 on tasks inside the AI's capability finished 25.1% more quickly, completed 12.2% more tasks, and produced work rated more than 40% higher in quality than a control group.
758 Boston Consulting Group consultants in total, in a preregistered experiment, randomly assigned to no AI, GPT-4, or GPT-4 with a prompt-engineering overview. These figures come from the 385 who worked on 18 realistic consulting tasks inside the AI's capability, such as developing new product ideas.
The gains held only for tasks inside the AI's capability. On a task outside it, consultants using AI were less likely to be correct (E3b). One firm, GPT-4 as it was in 2023, and a working paper.
Dell'Acqua et al., Harvard Business School working paper, 2023 ↗
19% slower
Experienced open-source developers took 19% longer to complete tasks when AI was allowed, though they estimated afterwards that AI had cut their completion time by 20%.
16 experienced developers, with about five years on their projects, working 246 real issues in their own mature repositories; each issue was randomly assigned to AI allowed or not allowed. The main tools were Cursor Pro with Claude 3.5 and 3.7 Sonnet, February to June 2025.
Early-2025 tools, and expert users on familiar, complex work. METR says the result does not show that AI fails to speed up most developers. In February 2026 METR gave the 95% confidence interval as 2% to 39% slower, said developers are likely more sped up now, and said its newer data give an unreliable signal because developers opted out of working without AI.
METR (Becker, Rush, Barnes and Rein), 2025 ↗

Published studies, not our results. Ours will come from documented pilots.

Conformance is self-declared; no regulator endorses this work.

Start

Start with a baseline

Ten tasks scored and measured before anything is built.