To go from idea to production with AI is both a science and an art. This is the issue with AI today, and the industry is struggling with deploying AI successfully. There are numbers being thrown about – how 70, 80, even 90% AI POCs end in failure. If this is true – why is this happening?
How can we ensure success with AI? Is there a playbook to help us do so?
We have built a playbook that we follow for success. You can follow it too – its here, its for you to use. Talk to us if you want more details.
Having built AI, ML, GenAI and Agentic AI executions successfully in large and small Enterprises, we have a pretty good grasp of what works and what doesn’t. Therefore, we’ve distilled that into the DAI-POC playbook.
The key ingredients of the DAI-POC approach are D-Data, AI-Artificial Intelligence, P-People, O-Outcomes, C-Culture
We start with D for Data. This is about the knowledge and the information in the Data we have access to, not just the Datasets themselves. We need to really understand and be able to relate to the level of trust we have in this data, the correlations and causations of things. We need to be able to have confidence in the Data. This can be done automatically or with human input, although the vast majority today are human driven.
AI stands for Artificial Intelligence. This is not just whether to use AI, ML or Analytics. Not just about which LLMs to use – whether single shot prompting will be sufficient, etc. This is a key role which requires the ML leadership to think beyond the immediate. Decisions may be taken on ML and AI models/algorithms.
P stands for People. AI is a People problem. To succeed, we need to ensure that all our people are AI capable, from the Leadership team to the junior most people on our frontlines. Data and AI literacy is a key part of the solution.
O is for Outcomes. We must start from our stakeholders and the key performance indicators (KPIs) that they are committed to solving. These KPIs are the target outcomes that the AI team must aim for. A corollary is – the AI strategy is simply the strategy of the company applied to AI for execution. More on this another day!
C – Culture is a critical piece of the puzzle. Many organizations are not ready to take on the changes that AI might bring. So, everyone must pull together to enable the thinking that is pro-AI and open to learning about AI, using AI in their everyday work and being able to understand how to do Responsible, Impactful AI.
There is a lot more to talk about, but this is the set of basic components that create the foundations for success in AI.
For more details, please ping the author at [email protected] and we’ll be happy to chat. Or put your comments in the section below. What did you like, or did not like?
If you like this, and you decide to use this, please acknowledge Data-Hat AI, and KK’s role. Thank you in advance!
