GPA Teknoloji

AI Transformation

AI Productivity Diagnosis

We analyze repetitive work across sales, finance, operations and support, then score AI automation potential in a concrete report.

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Process mining, opportunity scoring and automation board for AI productivity diagnosis

How should AI opportunities be prioritized?

Before investing in AI, you need to know where time is actually being lost — measured through a process map, not a guess. In this 1-2 week, fixed-fee diagnosis we map repetitive work across teams and separate what can safely be automated with AI from what should stay under human approval.

We score every opportunity on two axes: potential payback (time saved, error reduction) and implementation risk (data sensitivity, reversibility). High-payback, low-risk work goes on the quick-win list; higher-risk work goes into a more cautious roadmap — no recommendation is made without a risk assessment behind it.

Where we move you

Today

  • AI opportunities rest on guesswork
  • Unclear which process to automate first
  • Risk and fit have not been assessed

After this service

  • Process and workload map in hand
  • Every opportunity scored for fit and risk
  • Quick wins prioritized

How we work

01

Process and workload map

We turn this item into a concrete work package with owner, acceptance criteria and measurement point.

02

AI fit and risk score

We turn this item into a concrete work package with owner, acceptance criteria and measurement point.

03

Quick-win backlog

We turn this item into a concrete work package with owner, acceptance criteria and measurement point.

04

Investment prioritization report

We turn this item into a concrete work package with owner, acceptance criteria and measurement point.

Process mining, opportunity scoring and automation board for AI productivity diagnosis

AI opportunity + data readiness + ROI

Before turning an AI idea into a project, we measure where it will really pay off

AI investment is not just adding a chatbot. Repetitive work, decision delays, search time, document workload and data quality must be assessed together.

Research

How do we find AI productivity opportunities?

Generative AI value is strongest in customer operations, software development, sales, marketing, knowledge work and document-heavy processes.

Operating model

What we score in AI productivity diagnosis

The goal is not to say 'AI is possible'. The goal is to decide which use case goes first, which waits and which should not proceed because of risk.

Area
What is measured?
AI opportunity
Decision output
Repetitive work
Copy-paste, classification, document checks and data entry
Automation and agent support
Quick-win list
Knowledge search
Time spent finding policy, contract, pricing, inventory or customer knowledge
Enterprise knowledge assistant
RAG and knowledge-base plan
Document processes
Proposal, report, petition, minutes, contract and email generation
Template-based generation and control flow
Human-in-the-loop model
Decision support
Forecasting, scoring, alerts, anomaly and prioritization needs
Analytical model or AI recommendation system
Data readiness and model choice
Security and compliance
Personal data, trade secrets, audit trail and authorization limits
Local model or controlled cloud model
Risk and policy matrix

Roadmap

From AI diagnosis to implementation

The diagnosis produces a prioritized portfolio with cost-benefit logic, data requirements and implementation order.

01

Week 1

Process and workload analysis

Repetitive work, document load, knowledge search and decision delays are mapped.

02

Week 2

Data and security check

Data sources, access rights, sensitive fields and model constraints are reviewed.

03

Week 3

Opportunity scoring

Each scenario is scored by value, technical difficulty, data readiness, risk and maintenance need.

04

Week 4

Pilot plan

The first 1-3 pilot use cases are proposed with KPIs, human review points and architecture options.

KPIs we track in AI productivity diagnosis

Time saved per personKPI
Document production timeKPI
Knowledge search timeKPI
Data readiness scoreKPI
Risk levelKPI
Pilot ROI estimateKPI

The AI opportunity by the numbers

$2.6-4.4T

Estimated annual economic potential of generative AI

Source: McKinsey

1-2 weeks

Typical diagnosis timeline

Frequently asked questions

Could the diagnosis conclude that we should not use AI?

Yes, and that is a valid outcome. Our goal is not to sell AI — it is to give you the data to make the right call.

How long does the diagnosis take and how is it priced?

It typically takes 1-2 weeks and is quoted as a fixed fee based on your organization's size.

Let's talk about this service

Tell us briefly what you need, and we'll scope the right approach together.

Get in touch