Optimizing AI Strategy & Transformation
Optimizing an AI strategy requires a short term approach and long term vision
But AI requires humans: “The true power of GenAI comes from humans with big ideas.”
Once POCs validate the scenerios its critical for the organizational change management to kick in, show people how this will benefit them, and see when the best time to move faster with longer term goals can be envisioned.
Different departments may be in alternate stages of this voyage, all under the same AI Transformation initiative
Organizations start by understanding the baseline - exploring what AI can do and cannot do, identifying potential use cases, and building a foundation of knowledge. There will be areas for a speciic enterprise where AI will not be efficient, no matter the quality of the LLMs are. Regulated vertiacls, such as financial services and pharma life sciences MUST have human evaluation before submitting to in house regulators/ MLR as well as external regulatory resources. Too many mistakes have been made assuming that AI will not need to be evaluated.
The AI Transformation process requires an enterprise to train employees, locate data sets, data infrastructure, and address misconceptions about AI, whether positive or negative. It’s essential to know what the organization’s culture is, what previous change management instances have been like with successes and failure clearly documented. If you start with a realistic evaluation of current state, AI goals, you have a better chance of success with AI transformation. Once the basics are in place, companies can create with small-scale POC AI projects, looking for concepts that are high value, are high measurable, and have clear guardrails.
POCs aid confidence, proving the value of AI in specific scenarios, document mistakes, and allow enterprises to move forward with sucesses and document areas of AI that don’t show value in other areas.
As AI becomes more complicated, CIOs are likely going to have to do some expectation managing and level-setting for other C-suite executives. Regulations will increase, costs need to be managed, expectations made realistic, and “driving the right TCO”. What works for a startup often won’t work for a large enterprise
In healthcare, finance, and customer service, the unpredictability of real-world scenarios can confuse the most sophisticated AI LLMs. These models may excel in generating human-like “basic” responses but struggle with the depth of knowledge and context required for specialized tasks and customer requests. Ask your mobile carrier AI Chatbot “switch to temporary International Plan, they all choke.
After POCs are evaluated, KPIs shown to leadership, budgets optimized, companies can begin to have confidence in the roadmap, business value, and ultimately the technology.
As POCs are completed, AI process is documented an enterprise can drive innovation, improve long term strategies and decisions, and create a comprehensive AT transformation
