AI is not an isolated strategy. What does ‘transformation’ mean?
How digitalisation, data and AI interact within a company’s strategic framework
The terms ‘transformation’, ‘AI- transformation’ and ‘transformation strategy’ are currently on everyone’s lips. But what exactly do they mean? Are they referring to a new strategy, the use of a particular technology, or the changes that result from it?
An assessment based on practical experience of transformation
Many companies are still unsure how to assess the significance of AI for their own business: what impact will AI have on their performance, their future competitive position and, possibly, even their long-term viability? What role should AI play within this overall business context – and how can the topic be integrated into existing strategies? In this complex situation, the board and/or senior management expect their responsible executives to provide an initial set of ideas and guidance. At the same time, the IT department is tasked with examining potential applications. But which decisions belong at which level? Anyone who selects an AI tool first and only then looks for a purpose for it is starting with the solution before the objective has been clarified.
This blog aims to contribute to conceptual and strategic clarity regarding ‘transformation’ as a task
It distinguishes between corporate strategy, business unit strategies, IT strategy, data strategy and AI strategy. It also explains when the use of AI actually leads to transformation – and why not every change requires an additional ‘transformation strategy’.
Digitalisation and AI as a potential component
Digitalisation refers to the broader transformation of services, processes and business models through digital technologies. AI is a potential component of this development: it can make targeted use of digitalised processes and data sets for decision-making, automation or new offerings. Whether AI creates added value depends on the AI strategy in conjunction with corporate, business unit, IT and data strategies. The following section illustrates this relationship across the individual strategic levels: from corporate strategy through business unit strategies to IT, data and AI strategies.
A. Corporate strategy
Corporate strategy takes a view of the overall portfolio of business areas. It determines the businesses in which the company intends to operate, how these fit together, and how capital, staff and management attention are allocated. It is also at this level that decisions are made regarding the broader future of individual business areas: which ones should be developed or strengthened? Which ones will be continued as before, realigned, sold or discontinued? Corporate strategy is therefore more than the sum of individual business unit strategies. It makes decisions from the perspective of the company as a whole. This results in binding top-down guidelines for the business units – such as investment limits, company-wide priorities or requirements regarding shared resources and capabilities. These guidelines are essential: what may appear attractive for one business unit may represent a misallocation of (already scarce) resources for another.
The key question when developing corporate strategy is: in which business units do we wish to operate, what future do we envisage for them, and how do we deploy our resources most effectively across the entire portfolio?
B. Business Unit Strategies
A business unit strategy sets out how the company intends to succeed in a particular market. It clarifies which customers the business unit aims to serve, what benefits it offers and how it differentiates itself from the competition. In doing so, it operates within the parameters of the corporate strategy. However, business unit strategies are not simply ‘broken down’ from a ready-made corporate strategy. Both levels are integrated and aligned using a bottom-up/top-down approach: senior management sets out objectives and constraints. The business units assess these against their knowledge of customers, markets and their own capabilities. They develop proposals and report back to senior management on opportunities, conflicting objectives or feasibility issues. This exchange constitutes strategic sparring. If the feedback indicates that a top-down directive is preventing important opportunities or is unworkable, senior management can review and amend it. Until that happens, the directive remains the binding framework.
The key question for a business unit strategy is: How do we intend to succeed in our market within the company-wide guidelines – and how will we measure the successful implementation of this strategy?
C. IT-Strategy
The IT strategy is a functional strategy. It is derived from the requirements of the corporate strategy and the business unit strategies. Its purpose is to define the role IT plays in implementing these strategies: which applications, data platforms, interfaces, infrastructure, security measures and technical skills are required to achieve the strategic objective
In doing so, IT must bring together the requirements from several business units. It shapes the technological framework. Where does the company need shared systems? Where are different solutions required? And what technological capabilities need to be developed to make strategic initiatives possible?
The key question for the IT strategy is: What contribution must IT make to the implementation of the corporate and business unit strategies? What innovations do we, as a specialist department, need to implement in order to offer our internal customers the best possible service?
D. Data Strategy
A data strategy does not merely view data as technical assets, but examines its significance for services, decision-making and work processes. It asks: How do we turn data into a valuable asset for the organisation? Value is not created simply by storing as much data as possible. What matters is whether suitable data of sufficient quality is available and how it can be used for analytics, AI applications and business-relevant decisions. To this end – regardless of corporate, business unit and IT strategy – the following questions must be clarified: How is data from different systems integrated (data availability)? How should it be organised within a suitable data architecture? Is the data complete and reliable? Who is authorised to use it and for what purpose, and how are data protection and security ensured? The data strategy ensures that scattered data sets are transformed into usable foundations for analytics and AI.
Equally important is the establishment of company-wide data governance guidelines. These set out, for example, who is responsible for data sets, how their quality is verified, what access rights apply and how data is protected. IT provides the technical infrastructure. The business units and specialist departments then assess the data’s relevance and suitability for a specific purpose.
The key questions underlying the data strategy are: What data do we need to achieve our strategic objectives – and how do we ensure that this data forms a reliable and responsibly usable basis for strategic analysis and the profitable use of AI?
Not every company needs a comprehensive, standalone data strategy for this. Nevertheless, fundamental decisions regarding data should be explicitly made and aligned with other strategies.
E. AI-Strategy
The AI strategy sets out: How can AI positively transform our value creation, our work processes and our products? It highlights where artificial intelligence should contribute to the implementation of corporate and business unit strategies – and where its use does not promise sufficient benefits. The starting point is not the available AI tools, but the strategically relevant objectives: can AI, for example, improve a service for customers, support employees or make processes more effective?
To this end, the company identifies specific AI use cases, evaluates them according to their expected benefits and prioritises them: what problem is being solved? Is suitable data available? What costs and risks are involved? Another key strategic question is ‘develop in-house or buy in?’ – in other words, whether an AI solution should or must be built internally, sourced from a supplier or developed with a partner (e.g. Fraunhofer).
An AI strategy also includes AI governance: clear responsibilities and rules for the selection, implementation, review and ongoing use of AI applications. This includes, for example, determining when results are checked by humans and when a process must be handed over to a member of staff. The AI strategy must therefore be aligned with the IT and data strategies.
Two possible use cases illustrate the scope:
A service provider could use a bot based on a large language model to answer frequent customer service enquiries and forward complex cases to staff.
A manufacturing company could introduce AI-supported quality control that analyses image data and flags potential defects for a member of staff to check.
In the first example, customer interaction may change; in the second, a key production process. Whether a particular solution makes sense depends on its demonstrable benefits and the specific requirements.
The key questions for an AI strategy are: Where does AI create added value for our strategic objectives, and how do we decide on and manage its use? A good AI strategy explicitly allows for the option to say ‘no’ if a simpler solution fulfils the task better.
A summary example from the mechanical engineering industry
A machinery manufacturer is assessing whether services – such as the maintenance of the customer’s machinery via a central control centre – should play a greater role in future.
A. At corporate level, a decision is made regarding the place such services will occupy within the overall portfolio and how much can be invested in this area.
B. The relevant business unit develops a strategy, including a business plan, for the new maintenance offering within these parameters. It contributes its assessment of demand, competition and profitability. If the allocated funds are insufficient for a viable solution, it reports this back to senior management. Senior management reviews the proposal from the perspective of the overall portfolio and decides whether to amend its guidelines. In this way, top-down guidelines and market feedback work in tandem.
C. The IT strategy clarifies how machine data can be technically collected, transmitted and made available at the customer’s site.
D. The data strategy specifies which data is required for maintenance purposes, how its quality is assured and who is responsible for its use.
E. Finally, the AI strategy assesses whether AI offers added value when analysing this data via a central control centre compared to simpler methods (such as a service engineer on site), and what conditions apply to its use.
The table below provides an overview of the key issues and the focus of each strategy:
Strategy | Core Question | Focus |
A. Corporate Strategy | What should the overall portfolio of business areas look like? |
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B. Business Unit Strategy | How do we achieve success in a particular market? |
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C. IT-Strategy | What infrastructure and systems are required to implement the corporate and business unit strategies? |
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D. Data Strategy | How do we turn data into a valuable asset? Providing the raw materials for analytics and AI. |
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E. AI-Strategy | How can we use AI to make meaningful changes to our value creation, products and processes? |
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