What happens in a 2-week AI Operations Sprint

The question everyone gets stuck on

You know AI could improve your operations. You have seen what competitors are doing. Maybe you have even tested a few tools yourself. But you are stuck on the same question every business owner in the GCC eventually hits: where do we actually start?

That is exactly what an AI Operations Sprint is designed to answer. In two weeks, you go from "AI could probably help us somewhere" to a concrete, prioritised plan with real numbers attached. No theory. No 80-slide deck that collects dust. A working roadmap you can act on immediately.

Here is exactly what happens, day by day, so there are no surprises.

Week 1: Discovery and mapping

The first week is about understanding your business as it actually operates — not how it looks on an org chart.

Stakeholder interviews (Days 1–2)

We sit down with the people who run your operations. Not just leadership — the team leads, the operations managers, the customer service supervisors. The people who know where the real bottlenecks are.

These are structured conversations, typically 45–60 minutes each. We cover:

• What does your day-to-day actually look like?
• Where do you spend time on work that feels repetitive or low-value?
• What breaks most often? What causes the most rework?
• What information do you wish you had but currently do not?
• What have you already tried to fix and what happened?

We typically interview 6–10 people across departments. The goal is to get an honest, ground-level view of operations — not the polished version.

Process documentation (Days 2–3)

Based on the interviews, we map your key workflows end to end. Not every process in the company — just the ones where AI could realistically make a difference. We document:

• Current steps, tools, and handoffs
• Time spent at each stage
• Error rates and rework frequency
• Decision points — who decides what and based on what information

This is where patterns start emerging. You will often find that three different departments are doing variations of the same manual task, or that a single bottleneck is creating cascading delays downstream.

Data audit (Days 4–5)

AI runs on data. So we need to understand what data you have, where it lives, what condition it is in, and what gaps exist.

This is not a deep technical audit — it is a practical assessment. We look at:

• What systems store your operational data (CRM, ERP, spreadsheets, email)
• Data quality — is it structured, consistent, and up to date?
• Data accessibility — can it be extracted and used, or is it locked in silos?
• Privacy and compliance considerations — especially relevant under UAE data protection law

The data audit often reveals the single biggest factor in whether an AI project will succeed or struggle.

Week 2: Analysis and roadmap

With Week 1's findings in hand, we shift to analysis and planning.

Opportunity scoring (Days 6–7)

We take every potential AI application we identified and score it against four criteria:

1. Impact — How much time, money, or quality improvement would this deliver?
2. Feasibility — Given your current data and systems, how realistic is this?
3. Complexity — How long would it take to build and deploy?
4. Risk — What could go wrong, and how bad would it be?

Each opportunity gets a clear score. No gut feelings, no hand-waving. This forces honest prioritisation and prevents the common trap of chasing the most exciting idea instead of the most valuable one.

ROI modelling (Days 8–9)

For the top-scored opportunities, we build simple but specific financial models. Not theoretical — grounded in the numbers we collected during Week 1.

A typical model includes:

• Current cost of the process (staff hours multiplied by loaded cost, error rates, delays)
• Projected cost after AI implementation
• Implementation cost (development, integration, training)
• Time to ROI

For a logistics company in Dubai, this might look like: "Your manual shipment classification process costs approximately AED 380,000 per year in staff time and error correction. An AI classification tool would cost AED 95,000 to build and AED 2,500 per month to run. Payback period: under four months."

Real numbers. No fluff.

Implementation plan (Day 10)

The final output ties everything together into a sequenced plan:

• Quick wins — things you can implement in 2–4 weeks with minimal risk
• Medium-term projects — higher-impact work that takes 1–3 months
• Strategic initiatives — larger transformations that require 3–6 months and more investment

Each item includes scope, estimated cost, expected return, dependencies, and recommended sequence.

What you actually get

At the end of the two weeks, you receive a single deliverable — a comprehensive but readable document that contains:

• Process maps of your key workflows with AI opportunity zones highlighted
• Data readiness assessment with specific recommendations
• Scored and ranked list of AI opportunities
• ROI models for the top 3–5 opportunities
• A phased implementation roadmap with timelines and cost estimates
• Risk assessment and mitigation strategies

You also get a 90-minute walkthrough where we present the findings, answer questions, and discuss next steps. Everything is yours to keep, regardless of whether you continue working with us or not.

What it costs

Netary runs AI Operations Sprints as fixed-scope engagements. You know the price before we start, and it does not change. No hourly billing, no scope creep surcharges, no surprise invoices.

The exact price depends on the size and complexity of your operations, but you will have a firm number in writing before any work begins. We find this is the only honest way to work — both sides know exactly what they are getting.

Who should do this and when

An AI Operations Sprint makes sense if:

• You are spending on manual processes that feel like they should be automated by now
• You have tried AI tools in isolation but they did not stick
• You are about to invest a significant budget in AI and want to make sure it goes to the right place
• You are growing fast and your operations are starting to crack under the volume

It does not make sense if you already know exactly what you need built and just need someone to build it. In that case, skip the sprint and go straight to implementation.

The best time to do it is before you spend money on AI tools — not after. Two weeks of structured analysis up front can save you six months of trial and error.