AI Priorities for the New Year: The Questions Every Executive Team Should Settle in January

An executive team sets its AI priorities by settling which business outcome should change, which initiatives start for it, who owns them and how success is measured. Everything else follows from these decisions, the budget included.

This article appears between the holidays and is meant for the first working day in January. It poses eight questions that an executive team and department heads can answer in half a day, arranges the answers in a quarterly grid and shows the criteria by which you decide between buying and building. This article provides an overview and does not constitute legal advice.

Where do companies stand at the turn of the year?

At the turn of the year around one in eight German companies uses AI, and the distribution is uneven. The Federal Statistical Office reported in November 2023 a share of 12 per cent among companies with ten or more employees, with 10 per cent among small and 35 per cent among large companies with 250 or more employees. Companies without AI named a lack of know-how as the most frequent obstacle.

The documented effect concentrates on single work steps. In a field study of 5,179 customer support agents, the number of issues resolved per hour rose on average by 14 per cent with an AI assistant, and by 34 per cent for less experienced staff. An experiment with 453 professionals, published in Science, found writing tasks completed 40 per cent faster. How these findings should be read is set out in the AI review of 2023.

For your planning this gives a starting position with two sides. The benefit of single applications is documented, and pressure from shareholders and competitors is growing. At the same time many companies lack the ability to bring applications into operation. How to handle the expectations of owners is described in the article AI pressure from shareholders.

Which questions should the executive team settle in January?

The executive team should settle eight questions in January that together make up the year's AI plan. Each question has a criterion by which you can tell whether it has been answered.

The first three questions come first, because they fix goal, sequence and accountability. Questions four to seven clarify whether the applications can arrive in daily work. Question eight links the plan to the company budget. If three or more answers are missing, the plan is not yet ready for a budget release.

How many AI initiatives should a company start in its first year?

A company should start one to three initiatives in its first year and begin the next only when the first runs in steady-state operation. Each initiative ties up attention in the business unit and capacity among the few data and development specialists that a mid-sized company has. The Federal Statistical Office survey names lack of know-how, at 72 per cent, as the most frequent reason for non-use, which confirms the bottleneck at this point.

How the pressure to do a lot now can be turned into a robust plan is described in the article AI activism. The capacity calculation behind it is simple: count the available hours of specialists and business units per quarter and deduct daily business. What remains determines how many initiatives are possible at the same time.

What does a quarterly grid for AI look like?

A quarterly grid places each initiative in four stages with one executive decision each. The grid follows the phases of understanding, building and rolling out and can be adapted to the size of your company.

The grid treats operation as a step of its own with its own decision. An initiative that ends in the second quarter has served the purpose of the grid, because it tested and ruled out the economics before the rollout.

Buy, configure or build?

For most use cases, buying or configuring a market solution is enough in the first year. Building pays off only when the application creates a competitive advantage or no market solution meets the requirement. The availability of AI functions in existing software, for instance with Microsoft 365 Copilot since November 2023, widens the options for buying. You must still plan rollout and benefit. The model race is secondary for this decision, as the article Gemini versus GPT-4 shows.

Three criteria help with the choice. The first is uniqueness: the more an application sets your business apart from competitors, the more building is worth considering. The second is the data position: the more sensitive and proprietary the data, the more closely you check where it flows. The third is operability: without a team for upkeep and further development, a self-built application becomes a burden.

Which rules do employees need?

Employees need a short, understandable usage rule that sets out which tools are permitted and which information does not belong in public AI services. Such a rule makes usage visible and lets the executive team know where AI is used. Legal questions in individual cases are for qualified lawyers.

AI priorities with Niehues Consulting

Niehues Consulting is a consultancy for AI management and organisational design from a delivery perspective, based in Düsseldorf. Founder Marin Niehues brings AI into operations, in projects from VC-funded scale-ups to DAX 40 groups and in more than 20 countries on six continents. In the service area of AI management, the work runs from business analysis and requirements through the business case to the selection and sequencing of applications and the steering of the portfolio.

The approach follows the Niehues Delivery Model with its three phases. Three test questions matter for the priorities of the year: Which business outcome should change measurably? Who decides, who delivers, who uses? Who is accountable for operations from day one? Scope and terms of an engagement are discussed in the free initial consultation, which you can arrange in 30 minutes through the contact form.