Develop your AI strategy
Artificial intelligence (AI) has emerged as a disruptive technology across industries, providing prospects for increased efficiency, better decision-making, and improved customer experiences. To fully leverage the power of AI, organizations must ensure their readiness to incorporate and integrate AI solutions across their operations.
AI adoption (using the broader category) seems easier for startup companies as they can start on a clean slate, while existing corporations may have to examine their pipelines and identify use cases before retrofitting AI initiatives into their operations. Regardless of the type of organization, developing a comprehensive AI strategy is vital for successful AI implementation. This strategy should align with the organization’s overall business objectives and address specific use cases where AI can deliver the most value. The AI strategy should consider factors such as resource allocation, technology infrastructure, talent acquisition, and change management.
Identify your Use Cases
Identification of use cases is probably easier than qualifying use cases to be retrofitted. There will be many lofty ideas, but prioritization should align with the organization’s AI strategy. Many times, organizations have gone as far as selecting some ideas for implementation, sandboxing them, and killing the projects for many reasons ranging from obvious to non-obvious and even political. The caution here is not to get stuck in the overthinking phase.
What I have witnessed so far in working with teams on both sides of DevOps and IT risk is excitement and hesitancy, respectively. These are all justified and valid feelings. It is similar to the early days of cloud adoption. Certainly, AI implementation comes with its own set of challenges and risks. Enterprises need to address these challenges to ensure successful AI adoption. As long as we do not make rash decisions out of the fear of missing out (FOMO), AI adoption as a means of innovation will pay off in the long run.
Retrofit Operational Areas
According to GSA’s Artificial Intelligence Center of Excellence, the under-listed operational areas within the organization should be examined and prepped for AI readiness: people, infrastructure, security, machine learning models, software development, and data.
Conclusion
In reality, an organization may not have the complete expertise for some elements in these functional areas. It is okay to outsource some of the operations to vetted external providers, however, the organization must have good oversight of its supply chain pipeline.
Over the next couple of series, we will explore each of these functional areas.
Have no fear; innovate!
