aistrategy · 9 min read
AI creates strategic advantage when it strengthens something customers value and competitors struggle to reproduce. Leaders must decide where to differentiate, what to own, and how learning from real-world results can keep them ahead.
AI investment priorities must reflect expected business value, the evidence behind it, and the scarce capacity required to deliver it. Leaders need to compare complete investment paths, fund learning deliberately, and withdraw support when the case no longer holds.
AI value is realised through changed actions and measurable business outcomes. Leaders need to establish the improvement beyond today's approach, account for intervention costs, and distinguish released capacity from financial savings.
aistrategy · 10 min read
Scaling enterprise AI requires repeatable delivery, proportionate controls, and dependable operation. Leaders need to establish what can be reused, what needs local validation, and who owns the capability throughout its operating life.
AI data readiness depends on the decision, intervention, and evidence required to evaluate results. Leaders need to test meaning, availability, permission, and outcome quality before committing to a capability or a broad data programme.
Supplier choice shapes AI cost, control, and flexibility. Leaders need to identify the capabilities they must retain, test the effort required to change providers, and distinguish a credible exit route from an expensive duplication of the entire service.
Opinion · 12 min read
AI technology companies invest heavily in products, platforms and technical expertise, yet often overlook the role connecting technology with domain expertise and business economics. When adoption disappoints, the problem may be organizational design.
What I Work On · 5 min read
Turning qualified AI opportunities into working capabilities through data, features, models, architecture, and production integration.
What I Work On · 4 min read
Making AI adoption systematic by connecting business priorities, opportunity discovery, AI execution, and repeatable paths to production.
Strategy · 9 min read
Enterprises have invested heavily in the capability to run AI. Far fewer have developed the capability to determine where AI should be applied.
Strategy · 6 min read
Enterprise Design Thinking was built to solve a specific problem in product development. It is a legitimate methodology for that problem. Applied to AI strategy discovery, it produces the wrong output for structural reasons that have nothing to do with how well it is facilitated.
Article · 7 min read
Enterprise AI is often presented as requiring organisations to redesign business processes and operating models. But is large-scale transformation always the best place to start? This article explores an alternative in which organisations improve economically significant business decisions within existing processes before embarking on broader transformation.
Article · 5 min read
Agentic AI is transforming how organisations automate and orchestrate business processes. But does better orchestration automatically produce better business outcomes? This article explores the relationship between Process Intelligence and Decision Intelligence, arguing that autonomous workflows ultimately depend on the quality of the decisions they execute.
Determining which AI opportunities are worth pursuing by connecting improvements in business decisions with measurable economic outcomes.
Practice Area · 5 min read
How a distinct class of enterprise AI emerged from recognizing the importance of transaction processing systems in the AI era.
Understanding industries, business journeys, and decisions deeply enough to discover the AI opportunities that matter.
Brief · 5 min read
In most enterprises, AI is owned by the people least accountable for the outcomes it is supposed to deliver. That single structural fact explains more about why AI programmes fail than any technical consideration.
Two behaviours define the opening of most AI strategy conversations. The client wants to know what competitors are doing. The vendor asks what use cases the client is interested in. Neither addresses the actual problem. Both guarantee the wrong outcome.
Article · 6 min read
Ask the infrastructure team how the system is performing and they will show you nine nines of availability across more than 10 billion transactions a year. Ask the business owner how much the system is costing them and they will show you $450 million in annual losses. They are describing the same system. Neither is wrong.
Most enterprises have AI projects. Very few have AI operations. The distinction is not semantic. Once AI is continuously influencing business decisions, it is operational infrastructure. It needs to be governed, sustained, and measured as such. Most organisations have not made that transition.
Whitepaper · 14 min read
A strategic framework for identifying, sizing, and prioritising AI investments that deliver measurable business outcomes. Most AI initiatives fail not because of the technology but because of how organisations approach the problem — starting from preselected solutions rather than systematically discovering where AI can create the most value.
Whitepaper · 20 min read
How Transactional AI on IBM Z transforms high-value decisions into measurable business advantage. AI delivers the greatest impact when it operates at the heart of the business — inside the high-volume, high-stakes decisions that determine revenue, cost, and risk.
