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Operational and economic industrialisation of AI in IT delivery

Industrialising AI in IT delivery requires rethinking the operating model, practices and economic steering

How can you industrialise AI across your delivery chain to generate measurable value?

Delivering value from AI in IT delivery means regularly evolving practices, adapting the organisation, governance, skills and economic management of the production chain.

At both the operational and economic levels, IT delivery is better off building a continuous AI industrialisation approach than letting each team experiment in isolation.

To keep pace with the growing maturity of AI, and of agents in particular, the delivery chain must move from individual, anecdotal use of AI tools to a model where AI is natively embedded at every stage of the pipeline, with defined roles, governance and economic management.

Designing and implementing this model means addressing the following areas:

  • Rethink the organisation of the delivery chain around AI

    The delivery chain must redefine the contribution of each role (Product Manager, Business Analyst, Software Engineer, Tester) as AI agents take on a growing share of execution tasks. Cross-functional teams, equipped with function-specific agents and differentiated access rights, are gradually replacing the individual, uncoordinated use of AI tools.

  • Evolve the working culture

    The new world of work requires a culture of systematically validating AI-generated outputs, a tolerance for controlled experimentation, and learning by doing rather than through training alone. A network of internal champions, tasked with mentoring and spreading the practices that work, supports this change in mindset.

  • Adapt processes and governance models

    Agentic AI requires rethinking governance of production access (dedicated accounts, read-only rights, explicit authorisations for agents) as well as the IT department’s operating model. Organisations decentralised by subsidiary prove structurally incompatible with a coherent AI strategy. An AI Authority and EU AI Act-compliant traceability complete this set-up.

  • Revisit delivery methods and tools

    Production practices are moving towards spec-driven development, where structured specifications replace narrative user stories as the reliable entry point for agents. The CI/CD pipeline becomes the execution loop for these agents, sequenced by role to keep the documentation and code produced consistent.

  • Put in place measures to steer AI by value and cost

    Tracking AI adoption across a delivery chain requires moving from anecdotally measured usage to structured steering, combining value indicators (velocity, quality, adoption rate by profile) and cost indicators (consumption per task, per agent, per pipeline, up to TCO per feature).

  • Control costs and steer the value of AI

    Industrialising AI in IT delivery means controlling its full cost, beyond the price of a licence or a model alone. Depending on the deployment mode, notably on-premise, managed cloud or SaaS, spending items and their dynamics vary (infrastructure investment, consumption, licences, environment costs and running costs). Cross-cutting costs must also be factored in, notably IS integration, data quality, security and change management.

How can we help?

  • Assess the maturity of your delivery chain with respect to AI

    Evaluate your maturity level and the adoption barriers specific to your organisation, by team and by profile

  • Support the technical industrialisation of your delivery

    Set up the pipelines, agents and practices (spec-driven, agentic CI/CD) suited to your context

  • Adapt your operating model and governance

    Define your target operating model (Hub & Spoke, AI Authority) and the access governance suited to agentic AI

  • Implement a FinOps approach for SaaS AI models

    Track licences and usage-based costs, identify underused services and define suitable optimisation levers

  • Build cost models for hybrid AI adoption

    Compare SaaS, managed cloud and on-premise scenarios by cost type. Model costs by usage profile and architecture choice

Why Valthena?

3 key strengths to support you 

  1. A dual expertise: change management and DevOps/FinOps engineering handled by the same team
  2. A method combining 2 axes: horizontality (the entire delivery chain) and verticality (each profile, its own journey)
  3. Constant market watch, tested against our engagements, to distinguish what really works from what remains a promise