The Biggest Risk in an AI Project Isn’t AI

See how a structured process helped build a team and maintain delivery stability

When building an AI project, it is easy to focus on architecture, models, or access to the right specialists. However, even the best-planned project will eventually face change – whether in priorities, requirements, or the way the team operates.

At that point, project success depends on how quickly you identify early warning signs and respond before they begin to affect delivery. To do that effectively, you need a single, up-to-date source of truth about the project.

At j‑labs, we use the Agile Delivery Framework (ADF) – a project delivery methodology developed and refined over the years. It helps Delivery Managers identify risks early, improve collaboration, and keep projects on track.

In this article, we show how the j‑labs methodology worked in practice in an AI project delivered for a telecommunications client. See how it helped the client first build an AI team and then maintain delivery stability.

How a single source of truth for the project and recruitment streamlined the process of building an AI team

A telecommunications client needed to quickly build a team responsible for developing an AI-powered customer service platform. The project required specialists in Azure AI, NLP, machine learning, microservices, and systems integration.

The biggest challenge, however, was not simply building the team. It was managing all the moving parts of the process: priorities, responsibilities, recruitment stages, and decisions.

As part of the Setup stage of the Agile Delivery Framework, we prepared a report that brought together:

• key project objectives,
• required roles and the order in which they should be filled,
• responsibilities on both the client and j‑labs sides,
• the status of each candidate,
• next steps and risks.

What did the client gain?

Above all, greater transparency, faster decision-making, and a single, up-to-date source of truth for the project and recruitment.

The client knew which roles were the highest priority, where each candidate stood in the recruitment process, and who was responsible for the next steps. This reduced the need for additional meetings and manual information gathering, while lowering the risk of delays.

The Setup stage also made it possible to tailor the recruitment approach to specific positions. For the Project Manager role, the focus was on screening a large number of applications, while for technical roles, the priority was reaching scarce specialists with the right expertise.

The results included:

• hiring a Lead Engineer — the first role to be filled,
• hiring a Project Manager — the second role to be filled,
• preparing a plan for further team scaling, followed by the recruitment of four additional team members,
• reducing the time required for coordination and reporting.

The Setup Report was therefore more than just a document. It became a practical tool that structured collaboration, accelerated decision-making, and made the launch of a strategic AI project more predictable.

The value of the collaboration was confirmed in the client satisfaction survey:

• A fast recruitment process providing access to high-quality candidates.
• A process focused exclusively on presenting well-matched candidates, without overwhelming the client with irrelevant profiles.

“At the end of June, I had serious concerns, but today I’m glad that we managed to fill two key roles: Tech Lead / AI Architect and Project Manager. There are also several strong candidates still in the pipeline.” – we read in a message sent after the client had received the report and the two key roles had been filled.

How a single source of truth in the Delivery Report helped mitigate project risks

During the AI project, it was essential not only to monitor progress but also to quickly identify risks related to the team, competencies, and delivery continuity.

The regular Delivery Report brought together information on team health, changes in responsibilities, recruitment needs, people-related risks, and the client’s plans. As a result, status meetings could focus on making decisions and agreeing on next steps rather than collecting information from multiple sources.

What did the client gain?

Above all, greater control over the project and the team.

The report made it possible to:

• quickly identify declining satisfaction or the risk of engineers leaving the project,
• respond when assigned tasks did not match team members’ skills or expectations,
• identify people ready to take on greater responsibility,
• plan replacements and recruitment before competency gaps could affect delivery,
• clarify responsibilities and priorities following changes to the team structure,
• discuss future budgets and team composition optimization well in advance.

One example was the early launch of a replacement process for a key role. The report provided visibility into the planned departure date, requirements for the replacement, and the current recruitment pipeline.

Client feedback highlighted the need for greater predictability and transparency in project delivery. Following an analysis, the team moved from Kanban to Scrum, improving planning and giving the client and other stakeholders better visibility into progress and priorities.

The Delivery Report also showed that dividing the team into smaller groups and clearly assigning responsibilities improved the way work was organized. In the following months, it enabled us to monitor the impact of these changes, team members’ development, and areas requiring attention, such as excessive responsibility or dissatisfaction with the nature of assigned tasks.

Confirmed value for the client

The results of the collaboration were confirmed in the client satisfaction survey:

• Delivery Management support, communication, and responsiveness: 5/5,
• effectiveness of ADF in supporting collaboration: 5/5,
• willingness to recommend ADF and j‑labs: 4/5.

The Delivery Report was therefore more than a monthly summary. It became a tool for identifying risks earlier, making decisions faster, and maintaining team stability before issues could begin to affect the pace or quality of delivery.

Summary

Every project is different, but every project can face challenges and unexpected situations. When they arise, you need a partner with a proven approach to responding quickly and getting the project back on track.

Are you planning the development of a business-critical system, facing growing team needs, or running a project that requires experienced engineers? If you are looking for a partner that can provide the scale, quality, and expertise required in an enterprise environment, let’s talk about your project.