AI strategy for companies: Eight steps to digital transformation

• Association of the Internet Industry demands early distribution of planned AI billions
• Only a digital ministry can do justice to an overarching AI strategy
• AI study describes eight dimensions of making the digital shift

Artificial intelligence is an important factor for the future growth of the German economy. This is the finding of the joint study of the Association of the Internet Industry and the management consultancy Arthur D. Little, supported by the Vodafone Institute. The study also provides information on how companies can further digitalise their business model using AI-based technologies. However, Oliver J. Süme, Chair of the eco board, is certain that, before anything else, the right economic policy course must be set.

Distribute AI billions now: Digital Ministry could overcome bureaucratic hurdles

“The economic stimulus package adopted by the German federal government and the German AI strategy were important political signals for further promoting artificial intelligence in Germany – but action must now also follow,” says eco Chair Oliver J. Süme. For example, of the three billion Euros of the German AI strategy planned since 2018, only one billion has so far been allocated to the federal ministries. The vote for the third tranche is still pending.

Furthermore, the eco Chair hopes that the distribution of the planned AI billions will be in line with an overarching strategy. “To revive the economy after the initial Corona shock, we naturally need short-term solutions first, but we must not lose sight of visionary technological developments,” Süme continued. He therefore urges the establishment of a digital ministry to set the political course for digital transformation in Germany and Europe, not only in the short term but also in the long term.

Süme also appeals to companies themselves to show courage in relation to digital transformation, and not to fall back into the pre-crisis state: “If companies do not begin to make consistent use AI-based technologies now, they will be at a considerable disadvantage compared to competitors who use artificial intelligence to achieve a quality or cost advantages.”

Making the digital shift: How companies establish artificial intelligence in their business model

The joint AI study by eco and Arthur D. Little describes four different strategies for the digital transformation of companies – depending on the current and desired level of digitalisation of AI. For the majority of the companies, what is currently most relevant is a strategy that strengthens their own value creation in the long term. The focus here is on the use of AI applications to realize cost saving potentials.

1. Corporate management and governance

First, management must establish and support AI competencies. Depending on the corporate structure, this can be done centrally, as a separate unit, or by setting up responsibilities throughout the company. Once the responsibilities are established, the existing governance must be adapted so that AI solutions can support decision-making in processes.

2. Staff and culture

In order to create awareness of and further expertise relating to artificial intelligence within the company, it makes sense to set up a dedicated AI team that simultaneously supports the introduction of AI-based technologies. A mix of in-house recruitment of employees and the involvement of external experts can also be useful here.

3. Communication and change

In order for employees to receive optimal support from AI applications, extensive communication is necessary. To this end, a change management system should be established that removes existing hurdles and informs employees transparently about the effects of AI. In addition to communication, it is necessary to create company-wide measures to build AI competencies for employees.

4. Approach and roadmap

For the introduction of AI to have a lasting impact on your own value creation, it is necessary to proceed in a structured way. It is important to define a target vision for the entire company from the very outset. On the basis of this, the roadmap for the introduction can then be defined and implemented. The target vision should remain flexible over the following few years and be validated, especially on the basis of initial experience. An agile approach – in which sprints are defined and iterations accompany the introduction of AI for all processes – is recommended.

5. Data and Analytics

If AI is used in processes, the data basis is essential. To achieve this, companies must implement a data strategy and data governance that allow efficient data management.

6. Technologies

In addition to actively following technological developments within and outside of AI, it is important that companies establish an appropriate framework for the rapid introduction of AI technologies. Only when AI supports the value creation in the company as a whole does it make sense to optimise the platform and select the right strategic partners. This ensures that technology decisions in the introductory phase do not negatively influence the advantages or feasibility of individual use cases.

7. Processes and ecosystem

Artificial intelligence can support your own value creation in various ways. AI can be used as a tool to increase the quality of processes through the provision of better information, and especially through its strength in forecasting. Furthermore, AI can perform individual process steps automatically and act as an enabler for other process automation such as Robotic Process Automation (RPA).

8. Monitoring and control

In order to create the desired added value, the introduction of AI applications should be monitored and steered from the very beginning in terms of adding value. For processes, relevant key performance indicators (KPIs) must be defined and methods of control established. A transparent and timely monitoring of the costs and benefits of AI supports the communication measures and the ongoing planning of further introductions.