In the rapidly evolving environment shaped by AI, many leaders are overwhelmed by the plethora of AI use cases, technology platforms, and implementation approaches.
It does not have to be this way. Here is a simple to follow playbook that works.
First, adopt an experimentation mindset. Overwhelmingly, generative AI adoption is occurring from the bottom up. 90% of employees already use AI at work. As a leader, your responsibility is not so much in promoting AI adoption as it is in collecting, filtering, and amplifying AI use cases that support the company's strategy in compliance with its security, privacy, and ethical standards.
Running an AI hackathon is the most effective way to surface high-impact use cases and win support from stakeholders. Check out IdeaMentor (www.ideamentor.ai). The tool enables you to conduct ideation tournaments at any scale while bridging organizational, time zone, and geographical boundaries.
Second, once high-impact use cases are identified, they must be scaled across the organization. For each AI use case, you must select an appropriate implementation process and platform.
As always, the tradeoffs can be summarized along the dimensions of time, cost, and impact.
Based on the matrix above, start in the lower right corner and move counterclockwise. Deploy an enterprise chatbot, monitor usage, identify high-impact use cases, and codify them on a no-code platform like AnyQuest (www.anyquest.ai). Use the platform to
- organize knowledge
- deploy a mix of models
- connect models to data sources and enterprise apps
- orchestrate consistent and repeatable AI workflows
We've seen this exact progression work across financial services, consulting, higher ed, and healthcare organizations. The playbook is consistent and repeatable.