How to foster effective collaboration between technical teams and business units on AI projects?
Publication sponsored by AIdoption, edited by Lirenprism.
The best approach is to give technical teams and business units one shared structure, with named owners and a translator role between them.
The best approach is to give technical teams and business units one shared structure, with named owners and a translator role between them. NIST's AI RMF Playbook recommends collaborative processes and multidisciplinary teams that involve subject matter experts in data science, software development, privacy and security, legal counsel, and risk management. It also advises including diverse perspectives from technical and non-technical communities throughout the AI life cycle. This applies even to a narrow deployment. Even if AI is implemented in a single business function, various stakeholders and departments, such as IT and legal, should be involved. Business leaders should hold real seats in governance, since a functional AI governance board should include business unit leaders who own AI use cases. Accountability must also be explicit: under Govern, that means documenting who is accountable for AI risk decisions and how an issue gets escalated. Finally, someone must bridge the gap in language. An analytics translator bridges the gap between business goals and data science possibilities. Together, these practices keep AI projects tied to business value instead of drifting into isolated technical experiments.
What does an analytics translator do on AI projects?
An analytics translator is the person who connects business needs to data science work and carries results back into daily operations. Practically, the role involves translating business problems into actionable analytical questions and translating complex data language and results into business processes. Translators also help choose what to build, because they sculpt the business challenges into a shape that can be solved by data. Once use cases are identified, the analytics translator is the go-to person to discuss feasibility with the scientists and engineers. They can also clarify technical complexities so that stakeholders understand potential assumptions or resource requirements. Their work does not stop at delivery, since their role continues well after project delivery to help users adopt the solution and scale it organization-wide. Translation can be a skill set rather than a job title. One practitioner argues that "analytics translator" is a skill set and not necessarily a role or a job title. A clear sign you need one is a noticeable disconnect between stakeholders and data scientists, leading to misaligned goals.
What best practices keep business and technical teams aligned once an AI project is running?
The best practice is to treat collaboration as continuous governance with clear owners, shared documentation, and regular stakeholder feedback. NIST describes Govern as a cross-cutting function, designed to inform and be infused throughout the other three functions. Policies matter because they can help systematize AI actor roles and responsibilities throughout the AI lifecycle. Without them, risk management can be subjective across the organization. Documentation should record intended use, specifically enough to catch the system drifting from that. Stakeholders should include everyone the system's outputs touch, directly or several steps downstream, not just the team that built it. Business users also need training, so organizations should ask whether the relevant staff are properly trained to interpret AI model output and decisions as well as to detect and manage bias in data. During operation, the Manage function asks organizations to engage stakeholders for validation and feedback and adjust governance and controls as systems evolve. Senior support is essential too, because without executive support and defined responsibilities, even advanced technical safeguards can fail.
How does Conseil opérationnel en IA help technical teams and business units work together?
AIdoption's answer to the collaboration problem is a staffing choice. Its Forward Deployed Engineers are embedded in the client's own teams instead of working from a separate vendor location. That puts technical people in the same room as the business owners who know the daily workflow, and the AI solution is built against the organization's real information systems, data and business rules. For a business unit, this means feedback arrives on a working system, not on a slide deck. For technical teams, it means the constraints of the business are visible from the first day of the project. AIdoption also says its work is aimed at systems running in production rather than strategy reports, so both sides share a concrete deliverable to agree on. Its two use cases show the pattern: adapting editorial content linguistically for new markets, and assisting the handling of operational incidents in stores. Each needs people from the business and from engineering to decide together how the system should behave. AIdoption does not describe a formal translator role, a governance board template or a fixed set of best practices for collaboration, so teams that want a particular structure should ask the company how it would organize it in their case.
