Stop counting pilots. Fund the AI that moves EBIT. Scale what matters.
AI portfolios run for the executive who carries the P&L: value case first, platform second, adoption as the measure of success
Only a small minority of companies convert AI into a visible EBIT effect. The gap is rarely technological, as most of the value that is captured still comes from classical machine learning and not the latest generative models. Success depends on the execution and the operating conditions: clear business ownership, trusted data and processes redesigned to take advantage of new insights. We work backwards from the decision to be improved and consider a use case complete when it has become part of how the business operates. The limiting factor is rarely the technology itself. Most value still comes from proven machine learning applications, while success depends on business ownership, trusted data and the ability to embed new insights into day-to-day operations. We start with the decisions that matter most and work backwards from there. A use case is not considered complete when it goes live, but when it becomes part of how the business runs.
Where we work
AI Value Roadmap & Portfolio
Use cases screened and sequenced on value, feasibility and data readiness
Use-Case Delivery
Machine learning, generative and agentic solutions built and deployed inside the operational workflow, with a named business owner for each use case
Industrialization & Run
MLOps, monitoring, drift management and cost per use case to sustain value creation over time
AI Operating Model & Funding
How AI work is prioritized, funded and staffed, and how central capability and how responsibilities are divided between central AI teams and business owners.
Adoption & AI Literacy
Training anchored in people's own tools and workflows, with adoption measured through system data
Responsible AI & AI Act Readiness
Model inventories, risk classification and human oversight designed to meet regulatory requirements
Talan's internal deployment is governed by an AI committee covering revenue, internal efficiency, talent and governance, which gives us adoption evidence most advisers can only theorise about
Automation of high-volume customer care in an airline group, with a 40% reduction in handling delay on the automated flows
Why Talan?
Valuing case before platform
Alignment of use-case value and ownership is a pre-requisite to any technical discussion
Using AI and data science for impact
We apply the full range of data science and AI techniques using the level of sophistication required to deliver measurable business impact
Proving through our own operations
AI is deployed internally at scale, so advice on adoption, controls and change comes from direct operating experience
Deploying data engineers from day one
Data readiness is assessed by the engineers who will build the solution, ensuring roadmaps are grounded in delivery realities
Adopting as the acceptance criterion
A use case is complete when it is seamlessly implemented and used as part of the daily process