Gartner predicts that 50 percent of companies will not get measurable business value from AI by 2028, because their operating models have not been designed for AI-enhanced delivery. At the same time, putting AI to use is becoming more clearly both a leadership and a people and skills question. According to Gartner, 51 percent of CIOs see the gap between changing AI skill needs and available skills as their biggest barrier, yet only 40 percent of organisations have a strategy for preventing skills erosion.
The figures highlight the core problem: AI value is not unlocked by acquiring more technology. New operating models, skills and ways of organising collaboration between people and AI are needed.
AI first does not mean only automating current processes
The starting point of AI first development is to define what you want AI to achieve. Are you primarily aiming for efficiency, or building competitive advantage?
If current processes are taken as given and kept as unchanged as possible, AI easily ends up speeding up individual work steps. In an AI first model, the focus moves from single tasks to entire processes and to how work should be divided between people, AI and systems.
Three maturity stages from an AI-assisted model to AI first business
1. AI-assisted business
People do the work and AI helps with it. AI can, for example, produce drafts, summarise material, retrieve information or speed up individual tasks. The underlying process and division of work remain largely unchanged.
2. AI-enabled business
AI starts automating entire work steps and parts of a process. AI no longer only helps a person do a task faster; some of the work can be handed to systems.
3. AI-first business
Processes, systems, roles and possibly also the business model are designed from the outset for collaboration between people and AI. AI is not a tool added on top of an existing operating model, but one of its design principles.
A sales proposal process illustrates how AI first thinking differs from speeding up individual work steps with AI. In a traditional approach you might ask whether AI could write the proposal. AI then accelerates one step of an existing process, but the process itself stays largely the same. From an AI first perspective the question is broader: how should the entire proposal process be rearranged? AI could, for example, gather information about the customer, analyse the need, find suitable references and prior knowledge, form an initial solution proposal and prepare the proposal materials. Expert work focuses on the stages that require business judgement, a deeper understanding of the customer's situation, or actual decision-making. The greatest benefit then does not come from speeding up one task, but from changing the division of work and the process as a whole.
The starting point is the business goals and how work should be organised to reach them. AI expands the ways those goals can be achieved. Information retrieval, risk identification, analysis, operational optimisation, decision support and even entire work stages can be shifted to AI in ways that were not previously feasible because of cost, capacity or practical constraints. At the same time, a company can gain capacity and expertise that would previously have required more staff or external services. This can reduce the direct dependence of business growth on headcount and enable higher revenue per employee.
A company's own data and context determine the value of AI
General-purpose AI is readily available to everyone. Harder-to-copy competitive advantage is created when AI is connected to the organisation's own context.
For AI agents to genuinely do work on an organisation's behalf, they need to be able to use, for example, customer data, documentation, company systems, business rules, historical information and decision-making principles.
The key question is therefore not only how much data the organisation has. In an AI first model, what matters is the diversity and usability of information: which sources form the needed context, where the information lives, and how AI can use it in a controlled way as part of the process.
When AI takes on more responsibility, security requirements also grow
In an AI first model, AI is no longer a separate tool whose output a person always checks before any action is taken. The more processes, information handling and system actions are shifted to AI, the more serious the consequences of errors, incorrect access rights or poorly defined operating limits can be.
The more responsibility AI is given, the more precisely its operating limits must be defined. The organisation needs to define which data and systems AI can access, what it may do independently, and at which stages human approval is required. At the same time, confidential information must be handled securely and AI's actions must be traceable. This is not only about the security of a single AI solution, but about the rights and operating possibilities given to AI as part of the company's IT environment and business processes.
Dependencies also create risk. If a core process is built on AI, the organisation needs to know what happens when the solution does not work as expected, the information it uses is incomplete, or connections between systems fail. An AI first operating model therefore also needs clear boundaries, controls and alternative ways of working, not only automation taken as far as possible.
This places new demands on the organisation's IT partner. The partner needs to understand not only AI solutions but also their impact on access rights, integrations, information management and the security of the entire IT environment. Technical delivery of an AI solution is not enough; its role has to be designed as part of the wider IT environment and its governance.
AI first change needs a governance model
You do not become an AI first organisation through a single technology project. The change is built from several connected areas: ground rules for AI, mapping current processes and use cases, data management, concrete AI solutions, and developing people's skills.
We can support your organisation at different stages of this development, from drawing up an AI Policy and an AI mapping to data management, implementing AI solutions and tailored AI training. The goal is not only to bring in new tools, but to help build the conditions for growing AI's role in a controlled way as part of the business.
The core of AI first development is therefore not the adoption of new solutions, but how the organisation's operating model is built around them. If AI is used only to speed up current ways of working, the result is mainly efficiency gains. The real potential of an AI first model is that processes, capabilities and even the business model can be redesigned from the starting point of people and AI working together.
Want to find out where your organisation stands on the AI-assisted–AI first journey? In an AI mapping we identify the processes where AI can deliver the greatest business benefit, and the conditions for implementing them. Get in touch!
