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Your digital ambitions- Your digital ambitions
- Your digital challenges
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- Your digital ambitions
- Your digital challenges
Your question
Data-to-AI-to-Value journey (Part 2)
By Tim Beswick
We are often asked whether becoming an AI-driven enterprise requires something different than becoming a data-driven enterprise. In this series of blog posts Anderson MacGyver shares her point of view on this topic. For those who want to start with part 1, you can read it here: How do we become an AI-driven enterprise? Now, let’s dive into part 2 in this blog.
Common good practices
You may recall an earlier series of blog posts: Did we just meet the modern Don Quixote?, where we discussed the following four common good practices:
- Vision, Goals and Strategy: Activate and focus the change effort by ensuring that all stakeholders have a clear and collective understanding of a relevant vision, goals and strategy.
- Data Value Opportunities: Build tangible bridges between business value and data capabilities by defining and prioritizing Data Value Opportunities.
- Data Value Delivery & Data Foundation: Use the prioritized Data Value Opportunities as the guiding stars for balancing Data & AI Value Delivery and Data Foundation efforts.
- If you focus your budget and efforts too much on Data Value Delivery, you may end up with solutions that do not meet requirements, fail to comply with regulations, cannot be integrated in your architecture, do not scale for production usage, do not provide sufficiently accurate insights et cetera. Great experiments in a dark and cold cellar, that will never see the light of day.
- If you put too much emphasis on the Data Foundation, you will end up doing lengthy and costly work without making an impact on the business. You spend a lot of time and budget without visible impact, you will lose momentum, business will lose their focus and limit their contribution, executives will become impatient and eventually the plug is pulled.
- Undercurrent of people change: Pay attention to the undercurrent of people change by deliberately working on addressing people related aspects such as motivations, emotions, beliefs, behaviors, symbols and rituals.
Note:
Data Value Delivery encompasses all activities and associated capabilities that deliver data products that are used by the business and either constitute or directly contribute to, business value.
Data Foundation covers all activities and associated capabilities that enable Data value delivery, but do not directly contribute to nor constitute business value.
Organization of both sets of activities and capabilities is captured in a data operating model that defines way of working, processes, organization structure, people resources, governance, systems & technology and sourcing approach.
What is specific for Data-to-AI-to-Value journey?
AI requires specific capabilities in knowledge domains such as Machine Learning, Artificial Neural Networks, Natural Language Processing, Computer Vision, Cognitive Computing and Autonomous Systems. We will not delve into the details of these, but rather investigate the extent to which the earlier mentioned common good practices apply to a Data-to-AI-to-Value journey.
Putting it very straightforward: the stated good practices are equally applicable to situations where AI is a major focus in your transformation journey. We have found that there are four additional themes that are often underestimated and will prove to be pivotal in your journey towards being an AI-driven enterprise:
- The generative- / general-purpose AI model buzz.
- Business process redesign requiring even more attention for people change.
- Additional risks and different measures.
- Even greater challenges in accessing AI talent.
We discuss each of these themes separately in the next four blog posts. So, stay tuned!
Interested in further insights into this topic? Join our CIO Masterclass on becoming a scalable, AI driven enterprise on the 13th of November.