What really makes Data and AI teams perform
Data and AI teams are under increasing pressure to deliver real value beyond AI promises.
For many organizations, this challenge is less about technology than about collective responsibility.
Generative AI has changed the landscape.
It has accelerated ideas, lowered the barrier to experimentation, and multiplied promises.
But it has also shifted a fundamental question that is often left unanswered:
Who actually makes the promise hold?
Over the past months, much attention has been given to leadership and direction roles: Product Leaders, Data Product Managers, Product Coaches, AI Strategists.
These roles matter. They create alignment. They give direction. They shape intent.
Yet no Data and AI transformation succeeds on intent alone.
It succeeds—or fails—on proof.
And proof is built by the team that delivers.
Data and AI teams performance is not about roles in isolation
A high-performing Data and AI team is not a collection of strong individual profiles.
It is a chain of shared responsibilities, clearly articulated around a product that exists in the real world.
When this chain is implicit, value fragments.
When it is explicit, value flows.
In that chain, technical roles are not “execution roles”.
They are the quiet guarantors of reality.
Data and AI teams: the role of software engineers in product reality
Software engineers do not “implement features”.
They make products usable, maintainable, deployable, and resilient.
In Data and AI contexts, their responsibility is decisive:
- turning experiments into real services
- integrating models into complex systems
- managing technical debt
- ensuring performance and reliability over time
Without this responsibility being fully recognized, AI remains fragile.
With it, trust becomes possible.
Data and AI teams: the role of data engineers in trust and reliability
Without reliable data, AI is only a sophisticated illusion.
Data engineers do not just build pipelines.
They make data:
- accessible
- understandable
- traceable
- testable
- reusable
They carry the responsibility of the invisible foundation on which decisions are made.
In mature organizations, this role is strategic—not supportive.
Data and AI teams: making models useful over time
A model that works once is not a product.
Data scientists and ML engineers are responsible for:
- experimentation without over-promising
- validation without overfitting
- monitoring drift and degradation
- explaining limitations as much as performance
Their work does not end with a metric.
It begins when the model meets real usage.
Data and AI teams facing generative AI and no-code: shifting responsibility
Generative AI and no-code tools have dramatically changed access to building.
They allow faster prototyping and broader participation.
This is a real breakthrough.
But simplicity does not remove responsibility.
It relocates it.
Risks become subtler:
- data exposure
- opaque dependencies
- unmaintainable artifacts
- decisions based on unobserved outputs
High-performing teams do not reject these tools.
They frame their usage, set lightweight guardrails, and take collective responsibility for what goes into production.
Connecting roles: why Data and AI teams—not roles—perform
Individually, each of these roles can deliver visible results.
But performance does not come from their juxtaposition.
It comes from the explicit tension between their responsibilities, focused on a shared product.
The Data Product Manager does not “own value” alone.
Engineers do not guarantee quality in isolation.
Data engineers do not secure data independently of usage.
Data scientists do not validate models outside of product context.
Performance emerges when these roles respond to one another—on assumptions, trade-offs, constraints, and real effects in production.
A high-performing Data and AI team is not an expert team.
It is a team that accepts collective accountability for what it puts into the world.
What really makes Data and AI teams perform
A high-performing Data and AI team is not defined by the sophistication of its models.
It is defined by its ability to turn intent into product reality—and to stand behind it.
This requires:
- a clear understanding of what is actually being built (a product feature, not just a solution)
- continuous observation of real usage and impact
- the ability to adjust—or remove—what no longer creates value
- shared responsibility for production outcomes
In that sense, performance is not about delivery speed.
It is about sustained responsibility.
This ability to move from experimentation to durable product reality is not about tools.
It reflects a level of collective maturity widely observed in engineering and delivery practices.
Organizations such as ThoughtWorks regularly highlight this distinction in their Technology Radar, showing that real performance comes less from innovation itself than from how teams integrate, observe and take responsibility for it over time.
https://www.thoughtworks.com/radar
Data and AI teams: direction roles without replacing execution
Product Leaders, Data Product Managers and Product Coaches remain essential.
Their responsibility is not to take over execution, but to:
- maintain coherence between intent and constraints
- protect teams from unrealistic promises
- ensure that value remains meaningful and explicit
They do not “drive” production.
They make it possible.
In conclusion
Data and AI transformation is neither about tools nor about individual brilliance.
It is about collective accountability, embodied by teams willing to respond for what they produce.
Generative AI expands possibilities.
Only responsible teams turn those possibilities into durable value.
Value does not emerge from a role.
It emerges from a team that takes responsibility together for what it puts into production.
For Data and AI teams, sustainable performance does not come from technology alone, but from collective responsibility in production.
This article is an English adaptation of a piece originally published on In Imago.
It reflects my work as a product coach and organizational practitioner, supporting Data and AI teams in turning intent into sustainable product reality.
To explore related articles on product, data and AI roles:
Actors, Product & AI – In Imago