Most companies have already moved beyond the hype phase of their Artificial Intelligence (AI) journey. They already invested time to experiment and understands the true potential of AI for their business.
They now want to move up the AI maturity ladder towards large-scale operationalization of use cases to see real benefits in day-to-day operations. And thus, move their business closer towards a truly data-driven organization. However, this next step brings new challenges. Addressing them with a well-designed AI Strategy unlock significant advantages. Here are the three most important ones.
1. Know which use-cases brings early business value
Simultaneously facing lots of AI initiatives from the inside as well as success stories from the outside, most companies started their Generative AI journey with experimentation.
They conducted a number of Proof-of-Concepts (PoCs) that might or might not have succeeded. By doing so, you acquire the necessary experience and knowledge to truly understand what AI can and can’t do for your business.
As long as you are in the explorative phase, it may be totally acceptable to have as many success stories as failures. But, once you’ve reached a certain level of maturity and start to go from experimentation to operationalization, the environment changes and common AI challenges arise. To successfully make this transition, identifying, prioritizing, and selecting the right use cases to move forward with becomes critical.
Consequently, one of the most important – and often first – steps of setting up your AI strategy is the development of a clear AI roadmap (see example below). You can accurately compare different use cases by correlating the expected future value of each use case with its technical feasibility-and thereby estimating the individual risk-return-ratios.
Trough visualizing your use cases in a value-feasibility-matrix, it becomes easy to identify the low hanging fruits that bring early value at low risk before moving towards riskier use cases. This approach delivers the first major benefit of a well-designed AI strategy: you build the right thing at the right time.
2. A Scalability-Proof Organizational Structure
Once you’ve chosen the most-promising use cases, you’ll want to make sure to implement them the best way possible. A well-designed AI strategy helps you develop the organizational structure needed to pave the way for any AI use case down the road. Specifically, it enables you to get two dimensions in order:
- Structure & Processes
- Infrastructure & Tools
First, to prevent common pitfalls such as multiple data silos, parallel projects, hidden know-how or unclear responsibilities, new roles and processes need to be implemented. This helps to establish roles, responsibilities and processes to prevent uncontrolled growth, create ownership and bring your data in one place. . Because when it comes to AI, more data is better data.
Setting up aunified infrastructure for organization-wide collaboration instead of distributed competition enables you and your company to design good AI products. The keyword here is scalability. Research1 shows that companies spend more and more time on the extension and the operationalization of ML models. And therefore less time on developing and improving such models as the number of productionized models grows. By choosing the right infrastructure and tools, you will avoid falling into this deployment trap.
A well-designed AI strategy helps you to set up the right responsibilities and processes, make sure the right data is collected and choose the right infrastructure and tools. It enables you to build things right.
3. AI Product Adopted by All Stakeholders
Even the best structures, processes, tools and infrastructures can’t create value if they aren’t properly utilized. One of the key challenges in AI is convincing people to adopt it.
Transparency and early involvement ensures AI Adoption. It allows stakeholders to see and understand the true implications of AI for their work experience. Often, it’s especially those who possess the key business and process knowledge required to build an amazing AI system are those who feel the most threatened by it (e.g. Customer Service Agents vs. Generative AI-powered Chatbots). A properly defined AI strategy helps them to understand AI as a support system, not a threat.
Subsequently, fear decreases, and the stakeholder involvement increases. Once you reach this point, you can harness the valuable knowledge of your business stakeholders and integrate it into your AI products. Share your vision with them in order to get them on-board and unlock their knowledge: AI doesn’t replace but support.
From a design perspective, bringing all stakeholders to the table and integrating them into the AI development process early on allows you to investigate, understand and integrate their individual needs. This helps to prevent the ivory tower syndrome – developers building fancy stuff nobody needs – and to actually build the things your stakeholders want and need.
A well-designed AI strategy is the foundation for changing your company culture into the right mindset to really capture the value of AI. A transparent and easy to communicate AI strategy that aligns stakeholders and brings them to the table early on leads to stakeholder buy-in and opens up the door for truly user-centred design.
How to develop an AI strategy that delivers those benefits?
We have years of extensive experience in sparking enthusiasm in and aligning stakeholder, assessing AI use cases and setting up processes and infrastructure. We are both holistic data strategist and seasoned technical implementers.
This puts us in a unique position to combine both points of view:
- We co-build an AI Strategy that is focused on business value on the one hand and technical feasibility on the other. By applying ourproven roadmap methodology, we empower you to build the right things. We are capable of assessing your current AI maturity level and the future value of your potential use cases from a management point of view, and from an engineering perspective.
- As we work with infrastructure components and specific tools in our daily consulting lives, we help you to introduce tailor-made processes, infrastructure, and tools to build things right!
It is in our DNA to cooperate closely with business departments and their users. We are well experienced in addressing their concerns and coaching them to use the final AI solutions. We know very well that even the best solutions fail without stakeholder buy-in. Therefore, we put our focus on creating expectations rooted in reality, communicating the true capabilities of AI to stakeholders and even showing them hands-on why they should not be threatened by but eager to adopt AI. From theory to practice, we bring the right mindset to your company!
