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AI ConsultingFind Where AI Actually Pays
Most AI budgets get spent on the wrong workflow. We sit with the people doing the work, map where the hours and errors actually go, score each use case against return and data readiness, and then build the first automations ourselves. You get a ranked plan you can act on, not a slide deck.
Riyadh · United StatesArabic and English DeliverySDAIA & PDPL AwareEngineers, Not Account Managers
INNOVATION
SCALE
Global Reach
Overview
Most AI Projects Fail Before Anyone Writes Code
Access to good models is no longer the hard part. Any team can call a frontier model this afternoon. The hard part is knowing which of your workflows is worth pointing one at, and that decision gets made long before an engineer opens a terminal.
The evidence on this is uncomfortable. MIT's NANDA initiative, in its 2025 State of AI in Business study, found that roughly 95% of enterprise generative-AI pilots produced no measurable effect on the P&L. Gartner has forecast that more than 40% of agentic-AI projects will be cancelled before the end of 2027, mostly for unclear business value rather than technical failure. In other words: the models worked and the projects still died.
They died because nobody wrote down which workflow was being changed, which number was supposed to move, who owned that number, and what the data would need to look like for the system to be trusted. That is what an AI consultation is for. Ours starts with your operations and finishes with a ranked plan and working automations, so you can judge the idea on a result instead of a forecast.
What an AI Consultation Actually Covers
Workflow Mapping
We shadow the actual process, not the documented one. Every handoff, every copy-paste between systems, every step where somebody waits on somebody else. Time-on-task and error rates get written down before anything is proposed.
Use Case Scoring
Each candidate gets scored on three axes: annual hours or riyals recovered, technical feasibility with your current stack, and data readiness. Anything that scores low on readiness is not a use case yet, and we say so.
Data Readiness Review
Most blocked AI projects are blocked by data, not models. We check what you actually have: where it lives, who owns it, how clean it is, whether it can legally leave your tenancy, and what it would cost to fix.
Build vs Buy vs Wait
For each use case we tell you whether an off-the-shelf tool already solves it, whether it needs custom work, or whether you should wait six months. We lose revenue on the first and third answers and give them anyway.
Governance and Compliance
For Saudi deployments we design against SDAIA's AI Ethics Principles and the PDPL from the start, including in-Kingdom data residency where the workload requires it. For US work, the equivalent review covers your sector's rules and your customer contracts.
Handover and Enablement
Your team gets the runbook, the prompts, the evaluation set and the failure modes. The goal is that you can change the system without calling us. Consultants who make themselves permanent are not consultants.
WHEN TO CALL
Six Signals You Need This Conversation
None of these are hypotheticals. They are the six openings that show up most often in the first call, and each one has a specific diagnosis behind it.
Your pilot worked and nothing happened next
The demo impressed everyone and then quietly stopped. Almost always this means no baseline was recorded before the pilot, so nobody could prove it beat the old process, and no owner was named to defend the budget.
Every department wants AI and you must pick
Five requests, one budget, and no shared way to compare them. Scoring on return, feasibility and data readiness turns a political argument into an arithmetic one.
You bought licences nobody uses
Seats were rolled out without changing a single workflow, so people opened the tool, found no obvious place for it in their day, and stopped. Adoption is a process design problem, not a training problem.
A vendor quoted you a number you cannot check
You have a proposal and no independent way to judge whether the scope is right, the timeline is real, or the architecture will survive your compliance review. A second opinion costs a fraction of the mistake.
Your team says the data is not ready
Sometimes true, usually true only of some of it. The useful question is which specific tables and fields block which specific use case, not whether the estate as a whole is clean.
The board asked for an AI strategy by next quarter
You need something defensible with sequencing, dependencies and run costs, that survives being questioned by a CFO. Not a list of buzzwords with a maturity curve behind it.
HOW WE ENGAGE
Three Ways to Start
Every engagement is fixed-scope with a written deliverable at the end. We do not sell open-ended retainers, and the scoping call is free because we would rather turn down bad-fit work early.
Workflow Audit
One department, mapped end to end, with every AI opportunity in it ranked by return and readiness. The fastest way to find out whether there is anything here worth funding.
- ✓On-site or remote shadowing of the real process
- ✓Time-on-task and error-rate baseline you can hold us to
- ✓Every use case scored on return, feasibility, data readiness
- ✓An explicit list of what not to automate, and why
AI Roadmap
Multiple departments, plus a sequenced twelve-month plan with a run-cost model. Built for the version of this conversation that has to survive a board meeting.
- ✓Everything in the Workflow Audit, across several departments
- ✓Twelve-month sequence with dependencies made explicit
- ✓Run-cost model: inference, storage and people, per use case
- ✓SDAIA and PDPL review for Saudi deployments
Build and Embed
We build the top use cases from the roadmap and put them into production inside your systems, with the evaluation harness that tells you when they start to drift.
- ✓Automations live in your production environment
- ✓Evaluation set and accuracy thresholds you control
- ✓Integration with your CRM, ERP or ticketing stack
- ✓Runbook, training, and a named owner on your side
HOW IT RUNS
Four Steps, No Discovery Theatre
01
We Sit With the People Doing the Work
Not the org chart, the operators. The person who reconciles the spreadsheet every Thursday knows where the time goes better than anybody in the leadership meeting, and they are usually never asked.
02
We Score Instead of Guessing
Every candidate use case gets a number on return, feasibility and data readiness. The ranking falls out of the scores, so the argument is about the inputs rather than about whose department wins.
03
We Build the Smallest Thing That Moves the Number
One workflow, in production, measured against the baseline we recorded in step one. If it does not beat the baseline we say so, and you have spent the price of an audit rather than the price of a programme.
04
We Hand Over Ownership
Runbook, prompts, evaluation set, known failure modes, and a named owner on your team who can change it. Your accounts, your data, your repository, throughout.
WHERE IT PAYS FIRST
The Workflows That Score Highest
Across the audits we run, the same handful of processes keep coming out on top: high volume, rule-heavy, and already generating the text or documents a model needs.
Customer Support
Ticket triage, first-response drafting, and deflection of the repeat questions that make up most of the queue. Scores well because the history of past tickets is already a training set nobody was using.
Finance and Back Office
Invoice extraction, three-way matching, reconciliation exceptions and expense review. Rule-heavy, high volume, and the errors are expensive enough that even modest accuracy gains pay.
Sales and Revenue
Lead qualification, CRM hygiene, proposal drafting from prior wins, and call summaries that actually make it into the record. The bottleneck is usually data hygiene, which the audit surfaces first.
Field Operations and Logistics
Dispatch and scheduling, exception handling, document capture from the field, and predictive maintenance where sensor history exists. Strong in Saudi industrial and logistics operations specifically.
Healthcare Administration
Intake, coding support, prior-authorisation paperwork and discharge summaries. High return, and also the area where governance review has to come first rather than last.
Legal, Compliance and Procurement
Contract review against a clause library, obligation extraction, vendor questionnaire responses and policy Q&A. Works because the corpus is finite, structured and already yours.
TWO MARKETS
Saudi Arabia and the United States
Saudi Arabia
Vision 2030 and the National Strategy for Data and AI have made AI adoption a board-level expectation rather than an experiment, and that has produced a great deal of procurement with very little scoping. We work in Arabic and English, design against SDAIA's seven AI Ethics Principles and the PDPL from day one, and architect for in-Kingdom data residency when the workload calls for it.
- ✓Delivery and documentation in Arabic
- ✓SDAIA AI Ethics Principles applied pre-deployment
- ✓PDPL and in-Kingdom residency designed in, not retrofitted
- ✓Riyadh presence, Sunday to Thursday working week
United States
The American conversation has moved past whether to use AI and on to why the last three pilots did not pay for themselves. That is a scoping problem, and it is the one we are set up to solve. You get a senior engineer on the call, not an account manager relaying questions to a delivery team in another timezone.
- ✓US business-hours overlap with senior engineers
- ✓Sector and contract review before any data moves
- ✓Post-mortem on stalled pilots as a starting point
- ✓Fixed-scope engagements, no open-ended retainers
Go Deeper
AI Workflow Audit
The ten-day method in detail: what we shadow, what we measure, and exactly what lands in the report.
Read
AI Readiness Assessment
The five dimensions we score, the four maturity levels, and a self-check you can run before calling anyone.
Read
AI Workflow Automation
What actually gets automated, function by function, and the workflows that look automatable but are not.
Read
AI Consulting in Saudi Arabia
SDAIA's seven principles as a pre-deployment gate, PDPL obligations, and in-Kingdom data residency.
Read
Build an AI MVP in 30 Days
When the audit says the answer is a new product rather than an automation inside an existing one.
Read
AI Voice Agents
The use case that scores highest most often: inbound calls, bookings and qualification handled end to end.
Read
AI Consulting Questions
A useful one maps your existing workflows, measures where time and errors actually accumulate, scores each candidate use case on return, technical feasibility and data readiness, and hands you a ranked plan with a run-cost model. The output should tell you what not to automate as clearly as what to automate. If the engagement produces only a strategy deck with no baseline measurements and no cost model, you bought a seminar rather than a consultation.
Not for technical reasons. MIT's NANDA initiative found in 2025 that around 95% of enterprise generative-AI pilots produced no measurable P&L impact, and Gartner expects more than 40% of agentic-AI projects to be cancelled by the end of 2027, principally for unclear business value. The recurring pattern is a pilot with no recorded baseline, no named owner of the metric it was supposed to move, and data that was never checked for whether it could support the task. All three are decisions made before any code exists, which is why the fix is scoping rather than a better model.
We build what we recommend. The consultation exists because we were repeatedly handed strategy decks by other firms and asked to implement plans that turned out to be technically impossible against the client's actual stack. Scoping done by people who then have to ship the thing stays honest, because an unrealistic recommendation becomes our problem in six weeks rather than someone else's.
The audit takes about two weeks and ends with a ranked plan and a baseline you can hold us to. If you move to a build, the first automation is usually live in production within six weeks of the audit finishing. We deliberately scope the first build to one workflow so the result arrives while the budget conversation is still fresh, rather than a year later.
Yes, and for Saudi deployments both are part of the design rather than a review at the end. That means a pre-deployment pass across SDAIA's seven principles, fairness, privacy and security, humanity, social and environmental benefit, reliability and safety, transparency and explainability, and accountability, alongside PDPL obligations on lawful basis, data minimisation and retention. Where the workload requires it we architect for in-Kingdom data residency, encryption in transit and at rest, and a retention policy that covers model artefacts and prompt logs, not only the source records. We are an engineering firm rather than a law firm, so your counsel should review the final position.
Usually not all of it, and this is where most programmes stall for a year. You need the data behind the one workflow you are automating to be good enough, not the whole estate. The audit tells you which specific tables, fields and sources block which specific use cases, and what each remediation costs, so you can fix the narrow path to the first result instead of funding a data programme with no visible end.
Yes. Most clients already have something: a Copilot rollout, an agent platform, a half-finished retrieval system, or a stalled pilot from last year. Reviewing what exists is usually the fastest part of the audit and often the most valuable, because a stalled pilot is a use case somebody already believed in and a baseline somebody already tried to move.
In the workflows that score highest, the honest answer is that it removes tasks rather than roles: reconciliation, triage, first drafts, data entry between systems. We ask you to decide the redeployment question before the build rather than after, because teams that have not decided it tend to quietly withhold the process knowledge the system needs, and the project stalls for reasons nobody will state in a meeting.
No, and we have no reseller relationship steering the recommendation. Model choice falls out of the requirements: accuracy needed, latency tolerated, cost per call at your volume, and where the data is legally allowed to be processed. For regulated Saudi workloads that last constraint usually decides it before anything else does.
Mid-market and enterprise teams, plus funded startups with real operational volume. The engagement needs a process that runs often enough to measure. If a workflow happens twice a month, an audit will find that automating it is not worth the integration cost, and we would rather tell you that on a scoping call than in a report you paid for.
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