Scoping AI Projects Effectively: The Key to Unlocking Business Impact

Laying the Right Foundations to Maximize the Impact of Artificial Intelligence in Business

Artificial intelligence is emerging across all sectors as a major driver of transformation. Yet, behind the enthusiasm, many companies face the same reality: their AI projects fail to meet expectations—or never get past the prototype stage.

The reason? Poorly defined or misaligned project scoping.

A well-scoped AI project starts from a real business need, relies on solid data, and involves the right stakeholders from the beginning.

In this article, we share best practices for effectively scoping an AI project, based on our hands-on experience across multiple industries.

1. Start with the Business Need, Not the Technology

The first common trap is starting from technology.

“What if we used ChatGPT to build an HR assistant?”

This tech-driven approach often leads to projects disconnected from day-to-day realities and real pain points.

In contrast, good scoping always begins with an analysis of the existing business process. The goal is to identify where AI can bring value. Tools from lean management or service design—such as SIPOC or waste mapping (muda)—can be particularly useful.

Key elements to look for in a process:

Repetitive, low-value tasks
Friction points that generate errors or rework
Waiting times or bottlenecks, where processing is slow or interrupted
Manual actions, often prone to variation or oversight
Cognitive overload, where users must handle an excessive volume of information

Once these issues are identified, the central question becomes:

What business problem are we trying to solve, and why is it a priority now?

2. Assess the Available Data

An AI project is 70% a data project. Many companies only realize this once they’ve started. Without relevant, high-quality, accessible data, no AI model can deliver reliable results.

Key questions to ask:

What are the relevant data sources? Structured (databases, files) or unstructured (PDFs, emails, reports)?
What is the volume of available data? How recent is it?
What is the data quality: complete, consistent, annotated?
What are the usage constraints: GDPR, security, ownership, third-party dependencies?
Who has access, and at what level of granularity?

And if the data is insufficient? Possible strategies include:

Collecting new data over a short period (forms, process instrumentation, document transcription)
Enriching existing data with third-party sources or analytical tools
Using synthetic or simulated data to kick-start a proof of concept
Segmenting the project to focus on a use case where data is available

Addressing data early saves time, avoids dead ends, and builds a solid foundation.

3. Define Clear AI Project Objectives

Every AI project must be tied to a clear, measurable business objective. The goal is not just to demonstrate technical capability, but to deliver tangible benefits to users or the organization.

Typical objectives include:

Reducing task processing time
Improving accuracy or decision quality
Automating part of a process
Generating recommendations or actionable insights
Making internal information or documentation easier to access

It’s essential to define success metrics upfront, even simple ones:

Average time saved per user
Number of queries processed automatically
User adoption or satisfaction rates
Comparison of AI results vs. manual processing

4. Involve the Right Stakeholders

AI impacts business, technology, data, and sometimes governance. Scoping should align these stakeholders and create the conditions for effective collaboration.

Key roles to identify:

Business sponsor – owns the need and guarantees impact
Data lead – documents and opens relevant sources
Technical lead – ensures integration and operational maintenance
End users – who must use, validate, and trust the AI solution

An overly technical or “top-down” approach often leads to internal resistance. Successful scoping engages the right people from the start.

5. Choose the Right Technological Approach

Once the problem is clear, the data assessed, and objectives defined, it’s time to select a technical solution. This choice should be driven not by hype, but by alignment between need, data, and constraints.

Examples:

Structured information extraction → NLP models + ETL pipeline
Document summarization or querying → LLM + vector search engine (RAG)
Email/document classification → Supervised model with tailored training
Marketing or HR text generation → Generalist LLM with business rules

The choice also depends on constraints such as:

Latency (acceptable response times)
Confidentiality (local vs. cloud hosting)
Inference costs
Maintainability and explainability

6. Plan the Project Steps

A classic project structure, to adapt based on scope and resources:

Scoping (1–2 weeks): identify needs, data, objectives
Data preparation (2–3 weeks): extraction, cleaning, structuring
Prototype/POC (2–4 weeks): functional demo on a reduced case
Evaluation: user validation, KPI measurement
Progressive deployment: tool integration, change management

This phased approach demonstrates value quickly while limiting risks and ensuring stakeholder engagement throughout.

7. Common Pitfalls to Avoid

Frequent traps during scoping include:

Starting from technology without a clearly defined business problem
Launching a POC “just to test AI” with no deployment plan
Underestimating the effort required for data preparation
Failing to involve end users in the design process
Overlooking integration with existing tools

Good scoping avoids these pitfalls by setting clear, realistic boundaries.

Conclusion: Good Scoping Is Already 50% of Success

An AI project is not a technology experiment—it is a transformation of business practices. Success depends less on model performance than on the ability to:

Understand the need
Mobilize data
Engage users
Build a deployable solution

At AI-Partner, we support companies in this strategic and operational scoping work—the essential first step to making AI a true driver of performance.

Do you already have a use case in mind? Do you want to structure your AI projects? Let’s talk.