⛔STOP following the same path for AI projects; as per “By some estimates, more than 80% of AI projects fail” – RAND.
The issue is if you follow the same path, you will get the same results.
🚩The typical and traditional approach of the software development lifecycle cannot be implemented in decision automation projects. For example, you should not write user stories. I mean you can, but they have no value in the context of decision automation leveraging data businessrules machinelearning LLMs.
The biggest issue with implementing AI-powered decision automation is not even the data and accuracy. CIO CTO CDO CDAO
💡It is all about:
* What outcomes does your organization need?
* How to deliver business value?
* What to automate and what technology should you use?
It is about making your stakeholders understand and become crystal clear about the value and scope of the project, specifically what problems they need to solve using AI. Additionally, ensure the team can deliver business value within the project, leveraging the right technology in the field of AI.
Yes, you've read it right…
💣AI is not a specific technique or algorithm. It is a field with various techniques and technologies.
So, to deliver a successful AI-powered decision automation, you need to work backward:
1️⃣ What outcome does the business need to see?
2️⃣ What business decisions will influence those outcomes?
3️⃣ What accuracy does the business expect for those decisions?
4️⃣ What technology do you need for those decisions?
5️⃣ What data do you need for those decisions?
6️⃣ How are those decisions influenced by other decisions?
As you answer these questions, you establish the inventory of business decisions that matter for the business outcome you need to create the solution for.
✨To tackle the high failure rate of AI projects, organizations in banking financialservices government insurance health need a new approach. Traditional software development methods fall short when it comes to AI-powered decision automation. Instead, we need to focus on the decisions that drive business outcomes.
💡This is where the Decision-Centric Approach comes in. By starting with the outcomes your business needs and working backward to define the decisions, data, and technology required, you can ensure your AI projects deliver real business value.
🚀Learn more about how this approach can transform your AI initiatives at https://lnkd.in/gcWUy9eU
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Published January 28th, 2025 at 07:30 am
