In our modern and highly technological world, artificial intelligence (AI) has become an essential driver of digital change. It is now one of the key technologies that transforms our working and social life in the future. Beginning as a minor innovation, AI has developed into a gamechanger and starts to settle in the sports industry as well. While its use leads to increased sales and cost reductions, most clubs still struggle to establish the technology in a value-adding manner and therefore miss out on a competitive advantage.
An important reason for this is the lack of an AI strategy on an organizational level which should harmonize with the club’s vision and mission. To ease the process of developing an AI strategy, we have identified 6 key elements that clubs need to consider for their AI strategy.
While data is the is the core of any successful AI initiative, the handling of such is a major challenge to most organizations. The systematic collection and management of data must be considered a strategic asset and treated accordingly. Oftentimes, the problem it is not the quantity of data, but its quality, and traceability. Clubs must engage in measures to collect and use their data in order to generate any kind value from AI in the future.
2. Use Cases
To scale one’s own AI initiative, a process for identifying, prioritizing, and implementing AI Use Cases is inevitable. And while the use of AI is broad, a distinction is often made between three scenarios: (A) development of new, data-driven business models, products and services based on AI, (B) improvement of existing products and services through AI and (C) optimization of internal processes through AI. For starters, use cases should always be prioritized accordingly to one’s own AI maturity level, beginning with smaller AI use cases and building up experience.
It is necessary to provide a flexible technical and organizational IT infrastructures to scale AI throughout the club. Professional IT and data management can make an essential contribution to the organization-wide success. The requirements ultimately result from the identified use cases, but many companies are pushing into the cloud, in which various infrastructure scenarios for AI initiatives can be provided at a reasonable cost (but also prototypical). Beyond that AI platforms allow to develop, deploy, and scale projects on a single point of truth.
4. Team and Skills
Assembling a talented team that is aligned with the organization’s AI maturity level is crucial to the initiative’s success. Paramount are the roles of the data scientist (primarily responsible for developing AI models) and the data engineer (responsible for acquiring, preparing, and providing the required data). Furthermore, the team should be completed by business analysts, who act as translators between the business department and the AI team and product owner roles, responsible for the development of AI solutions from PoC to deployment. Equally important as the external recruiting is the internal education to secure a company-wide endorsement of the technological change.
Agile processes on all levels and the anchoring of data-driven decisions in the culture of the club are vital for a long-term success of any AI initiative. Most important is the organization of AI teams, which can be observed in three types: (A) a central AI function and cost center operates across departments, (B) a decentralized AI function provisions AI teams and infrastructure within each of the different departments as separate cost center and (C) a hybrid AI function establishes a cross-functional AI unit with shared costs. In addition, a standardized AI project process, which serves as an orientation or scale as well as soft factors, such as change management for an organization-wide acceptance of the changes that AI brings as a technology, contribute to the initiative’s success.
To ensure that the technology can be safely scaled and anchored across the club in the long run, AI initiatives must be subject to comprehensive governance. In addition to regulation and documentation, results and decisions of AI models should be made transparent and comprehensible. Furthermore, specific requirements imposed by the sport industry, as well as ethical guidelines, which must be maintained throughout the development of AI models, can affect strategic considerations.
Overall, artificial intelligence will transform the sport industry in many ways. While some clubs already adopted AI into the organization, most are still in a phase of pre-operationalization. Nevertheless, constant adaptation will be necessary for them to stay compatible. Even though helpful, the dimensions outlined above can and will change over time and require a permanent adjustment to the club’s AI vision and maturity level.