AS Enterprise AI Advisory

AS Enterprise AI Advisory LLC · Garland, Texas

Most AI advisors produce slide decks. This one produces working systems.

I am Samir Shukri Mohammed, an independent AI transformation and technology operations advisor. I take a business pain point, design the solution, build the working prototype myself with AI coding tools, specify the stack end to end, and hand a demonstrated system to the engineers who take it to production. Thirty years of running enterprise infrastructure is what makes those decisions credible.

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Principal-led. Remote. Serving clients internationally.

A corridor between rows of dark server cabinets in an enterprise data centre, lit with warm light

What I do

Four areas of work. Each one ends in something you can use, not just something you can read.

  • AI strategy and operating model

    Value cases, target operating model, governance, risk tiering, adoption approach and roadmaps. I deliver these as interactive multi-scenario models rather than slide decks: configurable scenarios, resourcing, cost and total cost of ownership, use-case specifications with acceptance criteria, and a reference architecture carried through to a procurement-ready bill of quantities. Change an assumption and the resourcing, the cost and the plan move with it.

  • AI program delivery

    Taking use cases from pain point to production, working through teams that do not report to me. The sponsoring department owns the outcome and the engineers own the build. I get each use case to a demonstrated proof of concept and then direct the build to a system that is live, measured and governed, with metering and human-in-the-loop controls in place from the first day.

  • Technology operations

    Infrastructure, availability and continuity, service operations, vendor and contract governance, and the executive metrics that show whether any of it is working. This is the discipline I have practised for thirty years. It is also why the stack decisions inside an AI programme, from hardware and GPU sizing to operating systems, databases, security and data management, come from someone who has run them at scale.

  • Security and data governance

    Cybersecurity operating models, risk registers, data ownership, data quality and lineage frameworks, and audit readiness. An AI system inherits every weakness in the data and controls beneath it, so I treat governance as part of the architecture rather than a review at the end.

How I work

From pain point to working proof of concept, in five steps.

The five-step method Five connected steps in sequence: pain point, design, prototype, stack specification, and handover of a demonstrated system to engineers. 1 Pain point owned bythe business 2 Design architecture, controls,acceptance criteria 3 Working prototype built by mewith AI coding tools 4 Stack, end to end hardware and GPUsthrough to data management 5 Handover a demonstrated system,to engineers

“I set the technical bar by getting to a working thing first, not by describing one.”

Samir Shukri Mohammed

An architect's drafting table with technical system drawings in navy ink on ivory paper, a brass ruler and a fountain pen

  1. Start with the pain point, not the technology

    I sit with the people who own the problem, understand what it costs them in time, risk and effort, and agree what a solved version looks like and how we would know. Every later decision is measured against that.

  2. Design the solution

    Architecture, data sources and integration points, the risk tier and the human-in-the-loop controls that tier requires, and the acceptance criteria the system has to meet. The design is written to be built, not to be presented.

  3. Build the working prototype myself

    Using AI coding tools, I build a working prototype against real documents and real data wherever governance allows. The prototype is the argument. The people who own the pain point use it, and the conversation moves from whether this could work to what it needs before it goes live.

  4. Specify the stack end to end

    Hardware and GPU sizing, operating systems, databases, security, data management, model routing and cost metering. Thirty years of running enterprise infrastructure means these choices are made by someone who has been accountable for keeping systems like them running.

  5. Hand over a demonstrated system

    Engineers take a working, demonstrated system to production, not a document that describes one. The specification has already been proven by the prototype. I stay involved to direct the build, hold the technical bar, and keep the outcome tied to the pain point we started with.

The distance between a decision and a live system gets shorter. The engineers who take it to production are not interpreting a vision. They are hardening something they have already seen work.

Track record

An AI programme in production, and thirty years of operations behind it.

Ten AI workflows in production across nine business functions

Over roughly two years I designed and led an AI adoption programme at a large research university that put ten AI workflows into production across nine business functions. Every one of them shipped into a department that did not report to me. The platform has been sustained in production since October 2024. It is not a pilot.

$0.015 per query, all-in model cost, metered per query from day one

5.16 billion tokens processed for $2,215 in total model spend.

  • 30,761users
  • 151,950queries
  • 99.38%positive feedback
  • 367governed documents
  • 5delivery channels

What changed

  • Service operations moved from days to minutes on routed requests.
  • Document-heavy review dropped from more than thirty minutes to under five.
  • Demand forecasting is now used by 25 departments for planning.
  • 367 governed documents are served through a single role-aware gateway across five channels.

Representative workflows

  • An institutional answer gateway over governed policy documents, with citations.
  • AI-native admissions screening using vision, OCR and document parsing.
  • An advising and course-planning engine running against live student records.
  • Procurement split-request detection using similarity analysis and graph clustering.
  • An AI service desk that drafts every response and sends none. A human approves every reply.

Thirty years of mission-critical operations

Telecommunications service operations and information security at national scale. ICT and trunk infrastructure for a major Gulf development programme. Most recently, the full technology estate of a research university: 600+ servers across on-premise data centres, 12 research centres, 390 laboratories, 7,000+ staff and faculty, and a US$41M budget. Security maturity was raised from 75% to 88% under formal assessment. An enterprise data management programme was built from zero, and two formal audits were passed.

  • 600+servers, on-premise data centres
  • 390laboratories
  • 7,000+staff and faculty
  • US$41Mtechnology budget
  • 75→88%security maturity, formally assessed
A modern research campus atrium of concrete and glass in warm evening light

Platform and stack fluency

The AI platform above runs in-house, and that is a deliberate choice. On-premise data centres. Clustered SQL Server, including vector search. .NET applications. Microsoft 365, Teams and Entra SSO. Large language models are reached through a governed in-house gateway over direct provider APIs, 20+ models across 5 providers, with routing, PII masking, audit logging and per-query cost metering. Retrieval-augmented generation with citation. Vision and OCR document intelligence. Human-in-the-loop controls set per risk tier.

Running a full production AI platform in-house on direct provider APIs is what delivers both the data-control story and the unit-cost story. The data never leaves the institution's governance, and every query is metered against a known cost.

Credentials

Certified across security, data, service management and infrastructure.

  • CISSPCertified Information Systems Security Professional
  • CDMP MasterDAMA International, the highest examined level
  • ISO/BS 27001 Lead AuditorInformation security management systems
  • ITILIT service management
  • MCSEMicrosoft Certified Systems Engineer
  • CCNACisco Certified Network Associate
  • B.Sc. Electrical EngineeringControl Systems, The University of Arizona, 1990

Background

Three decades across telecommunications, infrastructure and higher education.

U.S. citizen, based in Garland, Texas, in the Dallas–Fort Worth area. I work remotely and serve clients internationally, with deep experience in the Gulf.

Samir Shukri Mohammed
Samir Shukri Mohammed, Principal
  1. 2026 – presentPrincipal, AS Enterprise AI Advisory LLCGarland, Texas
  2. 2023 – 2026Director of Digital Servicesa leading Gulf research university
  3. 2013 – 2023Head of Information Technologya UAE higher-education institution, full P&L
  4. 2011 – 2013Senior Manager, Information Technologya UAE technical institute
  5. 2008 – 2011Senior Project Manager, ICT and Trunk Infrastructurea Dubai development group
  6. 2001 – 2008Information Security and Quality Managera regional telecommunications operator
  7. 1995 – 2001Regional Technical Manageran IT integrator in Dubai

Contact

Start with the pain point. I will bring the working thing.

Start a conversation
An open leather notebook with a hand-drawn system diagram and a fountain pen, in warm lamplight