A business model encompasses many dimensions: from target groups and their needs to resources and channels through to cost and revenue streams. The task of a business design is to revise, validate or completely redesign these different fields as part of innovation and transformation projects. The ultimate goal is to create a balance between profitability, feasibility and market demand.

When we think of the new possibilities offered by the use of artificial intelligence, the focus often immediately turns to the question: “How can we make our business more efficient?” In practice, this means automating activities and substituting resources.

“Innovation Track": new opportunities through artificial intelligence

On the Business Model Canvas (see illustration), however, this perspective only affects the left half, the cost side. We therefore call this approach the “Efficiency Track”. Here, the output is easy to calculate, the use of resources is manageable and existing AI tools can often be used for the respective use case. In short: process X is taken over by tool Y and thus saves us sum Z.

This systematic scanning of optimization potential in your own business model will be necessary in order to remain competitive in the medium term and should therefore be part of every AI strategy.But if you want to be successful in the long term, you shouldn't ignore the right side of the business models: how can we address new target groups using AI? Which new channels can be opened up? Where are new revenue streams and perhaps even completely new value propositions?

We call this perspective the “Innovation Track”. Here, the output is not always calculable and the risk is comparatively higher. The process is less tech-heavy and more innovation-driven, which is why the focus is on experience and business design with methods such as creative ideation sessions or futures thinking. However, only those who have the courage and vision to rethink their own offerings instead of simply replacing existing ones will be able to exploit the full potential of artificial intelligence and turn from adaptors into pioneers.

Between Efficiency and Innovation:

Practical ChallengesIn day-to-day project work, however, this clear division is not always easy, as our most important practical challenges show:

1. Innovation and Efficiency are mixed up

Workshop participants often find it difficult to detach themselves from existing roles and processes, making it difficult to identify new opportunities off the beaten track. We therefore advocate a clear separation into two separate project tracks.

2. Data and Interfaces are not known

The basis of every AI project is the question of available or collectable data. If this is not known, it can easily remain a concept that never reaches the implementation phase. This is why we often ask our customers to list data and interfaces as a small “homework assignment”.

3. Opportunities are being missed

Hardly any other topic is currently changing as quickly as artificial intelligence. Opportunity windows open and close again a short time later. Only those who act quickly and with foresight can stay ahead of the game. The same applies to the up-to-dateness of tools that are still state-of-the-art today and could be outdated tomorrow.

4. Lack of Technical Know-how

The close involvement of “techies” with AI experience is essential in both the efficiency and innovation tracks. For this reason, our experts actively accompany every step and are available to answer questions about the technical background or feasibility.

Networking discussions revealed that many creatives are taking away similar learnings from their projects and that the industry is developing rapidly in terms of artificial intelligence. Let's look forward to a positive future! If you would like to find out more about efficient business modeling, send us a message using our form:
#createpositivechange