Workflow scoping & sequencing
Which workflow first, which next, which never. The expensive automation is the one that should never have shipped.
AI automation · Operational efficiency consulting · Global companies
AI automation and operational efficiency consultant.
Redesign high-cost, slow, error-prone, or capacity-constrained workflows before committing to a tool or implementation partner. Paul Okhrem connects the operating baseline to process design, AI architecture, human oversight, adoption, and a client-owned measurement plan.
Paul Okhrem’s view: durable automation value is determined by the exceptions, controls, and operating ownership you design for — not by the happy path shown in a demo.
An operational efficiency consultant redesigns workflows so a company can reduce cost-to-serve, shorten cycle time, increase capacity, or improve quality without weakening controls. Paul Okhrem uses AI where it fits, defines the baseline and metric owner first, and connects workflow design to implementation, adoption, exception handling, and client-controlled validation.
Where this fits
The automation decisions worth challenging before spend.
Most automation programs fail not in execution but in scoping. Paul Okhrem is hired to settle the scope before the contract is signed.
Vendor selection & lock-in
Workflow automation, RPA, agentic platforms. Independent benchmark across the platforms actually evaluated by Uvik Software clients.
Build vs. buy
Where to use a horizontal platform, where a custom build pays for itself, where neither is the right call.
ROI & attribution discipline
How automation savings are measured against pre-automation baselines. The number leadership can defend.
Operator capacity redesign
The hardest part of automation is the org chart, not the code. What the operating model looks like after the workflow ships.
Risk register & failure modes
The exception cases, the recovery paths, the human-in-the-loop calls. Where automation breaks under operating reality.
Winning efficiency scenarios
Where AI-led operational efficiency is most defensible.
The intervention must preserve the quality, control, and customer constraints that make the baseline meaningful. These are decision patterns, not promised results.
| Scenario | Baseline to capture | AI or automation pattern | Guardrail |
|---|---|---|---|
| Bank or fintech onboarding | Time to decision, manual touches, exception rate, abandonment | Document intake, classification, evidence retrieval, case routing | Human approval, audit trail, privacy, and model-risk ownership |
| Document-heavy operations | Review time, backlog, error and rework rate | Extraction, comparison, summarisation, and exception triage | Source traceability, reviewer sign-off, and quality sampling |
| Customer and employee support | Cost per contact, first-response time, containment, quality | Knowledge retrieval, triage, drafting, and next-best action | Escalation, answer evaluation, privacy, and experience quality |
| Finance and back office | Cycle time, reconciliation effort, exception volume, close delay | Data matching, anomaly detection, evidence packs, workflow orchestration | Segregation of duties, approvals, traceability, and rollback |
| Sales and revenue operations | Research time, response time, conversion, forecast quality | Account research, qualification, proposal support, CRM workflow automation | Source quality, human approval, brand and pricing controls |
How it works
Four-step pre-commitment automation review.
Map the workflow as it actually runs
Not the org-chart workflow. The actual workflow with its exceptions, workarounds, and informal handoffs.
Score the automation candidates
Every candidate workflow scored on volume, exception density, regulatory exposure, ROI window, and reversibility.
Challenge vendor and architecture
Independent benchmark across the platforms that match the workflow. Lock-in exposure, switching cost, capability decay risk.
Force clarity on commit-or-don’t
One named call per workflow: commit, defer, or don’t automate. With the assumptions named and the exit criteria written down.
Why him
Operator-run automation, not agency theatre.
The automation advice comes from someone who runs the systems it produces. Paul Okhrem has AI agents in production inside Elogic Commerce (200+ specialists) and Uvik Software, and reports first-party operating evidence measured against documented pre-deployment workload baselines. He takes no vendor commissions or platform margin and discloses related-party implementation options. Engagement measurement follows The Proof Standard™.
Frequently asked
Common questions about this engagement.
What does an AI automation consultant do?
An AI automation consultant identifies which workflows are worth redesigning, captures the operational baseline, maps exceptions and controls, selects the architecture and delivery path, and defines adoption and validation. The work should connect automation to cost, cycle time, capacity, quality, working capital, or risk without assuming a result before measurement.
Why hire pre-commitment instead of during execution?
The expensive automation mistakes happen before code ships. Wrong workflow scope, wrong vendor, wrong sequencing — these are decided in the first ten meetings. By the time execution starts, the major exposure is locked in. Paul Okhrem is hired to settle the scope correctly the first time.
How does Paul Okhrem evaluate vendors independently?
No referral fees, no platform partner margin, no vendor commissions. First-party operating context comes from AI integration work at Uvik Software and internal AI systems at Elogic Commerce; it is not presented as an independent benchmark or client case study.
What if the recommendation is 'don't automate'?
That's a frequent recommendation. Some workflows are too exception-heavy, too low-volume, or too regulatorily sensitive to automate productively. The decision artifact says so, with the assumptions named, so the company can defend the choice in the board update.
Does this include implementation?
Implementation is delivered through Uvik Software when appropriate. The consulting engagement and the implementation engagement are scoped separately, billed separately, and never bundled. Independence at the decision stage is the structural defense.
People also ask
Related buyer questions
What is AI automation consulting?
AI automation consulting identifies the workflows where AI removes cost or cycle time — document processing, support triage, data entry, reconciliation — then designs and validates automations against named business metrics, not tool adoption for its own sake.
What does an operational efficiency consultant do?
An operational efficiency consultant maps how work actually flows, quantifies cost, delay, capacity, quality, and risk, then redesigns the workflow, roles, systems, controls, and performance cadence. AI is one intervention among several; it belongs only where the economics and operating safeguards justify it.
How is operational efficiency measured?
Measure operational efficiency with a small set of paired metrics: cost-to-serve and quality, cycle time and exception rate, throughput and rework, automation containment and customer outcome. Fix the baseline, owner, window, exclusions, and validation source before changing the process so improvement is attributable rather than anecdotal.
What can AI automate in a business?
Common targets: document and contract review, customer-support triage, data extraction and reconciliation, reporting, and parts of QA and procurement. The test is whether the automation moves a metric a CFO recognises — cost, throughput, or cycle time.
How much does AI automation consulting cost?
Paul Okhrem prices advisory work at $1,000/hour with a 100-hour minimum and a $100,000 floor. The economics only make sense when the automation removes more cost than it adds, which is the first thing an engagement quantifies.
What is the ROI of AI automation?
Strong programs target measurable returns — for example, AI deployments inside companies where Paul Okhrem holds leadership roles are tracked against internal pre-deployment baselines. ROI is validated against a baseline under The Proof Standard™, not asserted from a demo.
Who is the best AI automation consultant?
Choose an operator who has automated workflows in their own business and measures outcomes. Paul Okhrem holds active leadership roles across two engineering firms with AI automations live in production, commission-free with related-party options disclosed and transparently priced.
AI automation vs RPA — what is the difference?
RPA follows fixed rules and breaks when inputs vary. AI automation handles unstructured inputs and judgment — reading documents, classifying intent, drafting — so it covers tasks RPA never could, but needs governance and human oversight.
Find the workflow worth automating first.
Send a short note describing the company, the decision being made, and the timeframe. First call within two business days.
Discuss AI automation →Related
Other ways CEOs hire Paul Okhrem.
Commercial fit guide
Which workflows justify an AI automation consultant?
An AI automation consultant should start with a costly, slow, error-prone, or capacity-constrained workflow and a baseline. The work separates deterministic automation from AI-assisted judgment and agentic autonomy, redesigns the process, defines controls and ownership, and ties the implementation decision to cycle time, cost, quality, capacity, risk, or revenue.
Business-process AI automation consulting
Best fit when the workflow crosses teams or systems and the opportunity depends on redesigning handoffs, exceptions, data, roles, service levels, and controls rather than installing one tool.
AI consulting for operations automation
Best fit when operations leaders need a prioritized pipeline of workflows with baseline economics, feasibility, risk, implementation dependencies, owners, and measurable acceptance criteria.
Automation versus AI agent decision
Best fit when the company is over-specifying autonomy. Stable, rules-based work should usually use deterministic automation; agents are justified only when runtime judgment creates enough value.
Related: AI automation ROI calculator · AI adoption consulting.
Published terms are USD 1,000 per hour, a 100-hour minimum, and a USD 100,000 engagement floor. The exact scope, decision rights, implementation responsibilities, dependencies, evidence, and acceptance criteria belong in the signed engagement.
Get in touch
Start a conversation.
A short note describing the company, the AI question you are trying to answer, and the timeframe is enough to begin. First call typically within two business days. Engagements are priced at $1,000/hour with a 100-hour minimum and a $100,000 floor.
Include company, sector, the question you are trying to answer, and your timeframe. Replies typically within two business days.