AI-Driven FinServ: The Ultimate Blueprint
A conundrum: which came first, the culture or the AI?
Great read from Fuzzy Labs and Woodhurst Consulting that provides a blueprint for building AI in financial services.
Kudos to both companies for this visually stunning and intellectually stimulating report.
Anyone who says this report is “ambitious” has a point. The authors, however, wisely keep the discussion high-level and focus on giving AI the “supportive organizational environment” it needs to succeed.
The authors nail it by stating that “culture” is the number one factor impacting AI success—not technology, data, or legacy systems.
This message parallels my first book, Innovation Lab Excellence, which focuses on building an innovation culture.
If AI and innovation were viewed like the conundrum “which came first, the chicken or the egg,” it is clear that innovation culture comes first, then AI!
👉TAKEAWAYS
The three phases of the AI Blueprint:
1. Assessment: where you ensure the organisation’s vision for AI is clearly and realistically understood and the ability to deliver on this vision is assessed
2. Inception: the preparatory phase when AI models and systems are conceived and the team and tooling are put into place
3. Development: through a process of iteration the AI solution and technical infrastructure are built and embedded within the organisation
🔹 There is no “one-size-fits-all” approach - businesses who want to adopt AI technologies with a path to success can use the AI Blueprint© as a flexible framework that can be adapted to suit their specific needs
🔹 The first stage in the framework is a thorough assessment of the problem the AI technology needs to solve
🔹 Building an MVP is critical to testing the feasibility, viability and desirability of the product or service
🔹 A phased, structured and constantly monitored project is the best way to deliver the solution and demonstrate business value
👊STRAIGHT TALK👊
There is no single document that will tell you how to build AI. Every company has a different culture and people with very different views on AI.
That said, the blueprint proposed by the authors goes a long way in giving a high-level outline of what needs to be done.
What was most important to me was that the authors recognized the cultural components required to implement AI, which is ultimately just another innovation.
This means that the ability to “fail fast” is key, and the “stigma” around failure must be removed.
In the end, finserv AI users have to anticipate more failures than successes with new AI projects, but the key is to learn and adapt continuously.
Is the culture where you work AI-ready?
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