Solving the right problems with generative AI

In an environment when budgets are restricted but expectations are high due to advances in generative AI technology, how can CxO ensure they are doing the right thing?

One of the strengths of generative AI is its ease of use and broad range of applications. As Greg Brockman of OpenAI recently quoted:

“every company, every individual, every business is a language business, So if you can add a little bit of value in existing language workflows, then it will just be able to be adopted so broadly.”

When there are so many problems to solve and so many possible solutions how do you prioritise the right ones for the greatest impact??

Understand your business - It sounds obvious but gaining a deep understanding of the processes that run your business is key. I come across a lot of businesses where the actual work involved is not understood or only by small pockets of individuals who have only part of the overall picture. Even fewer businesses actually measure the key processes involved. An exercise in service mapping will highlight the constraints within the system and provide a benchmark for any future improvements.

Deal with the hard stuff first - I speak to a lot of companies which are prevented from seeing big improvements to their efficiency through constraints in the process. Most of these are what I call Zero Value Historic Constraints. They are steps in a process that deliver no value but slow everything down. They tend to be hard to remove due to historic attachment. They are sometimes put in place to deal with a risk that no longer exists or are a legacy from a previous technical approach. These are the steps that should be automated or removed first. When I launched a new TV app about 10 years ago the business we were working in had lots of ZVHCs. It was a battle to remove them but the effort was worthwhile as it improved performance and actually improved quality and lowered risk. Without removing these historic constraints it will be hard to see gains from gen AI.

Work small and fast - The best way of working with any technology is through an incremental and iterative process. This approach is the best way to continually learn and adapt. This is especially important when adopting a new technology such as generative AI where we are still in the process of understanding the capabilities and risk. By working in small increments we can also ensure put in place the guardrails required to maintain high levels of quality, such as automating test frameworks and improving visibility of performance metrics. This will give us insight into any impacts the new technology may have on the existing environment.

Adapt skills - People will need to adapt their skills to make the best use of these technologies. The key skills will be those that focus on people’s ability to identify problems and constraints and allow them to work collaboratively and in an incremental manner. In a fast moving environment knowledge of specific tools will be less important than the ability to learn and adapt to solve specific problems.

Make data easily accessible - The success of generative AI heavily relies on the quality and accessibility of data. Data is the fuel that powers these AI systems, enabling them to learn and generate valuable insights, content, or solutions. Companies need a well-structured data management strategy in place. This involves not only collecting and storing data efficiently but also ensuring its cleanliness, accuracy, and security. Data silos can impede the potential of generative AI, so fostering a culture of data sharing and integration across departments can lead to more comprehensive and impactful AI applications.

Collaborate across functions - Generative AI has the potential to transcend departmental boundaries and provide value to various parts of your organisation. Encouragin cross-functional collaboration among teams like marketing, sales, research and development, customer service, and more will lead to enhanced processes and optimised customer experiences.

Monitor and assess continuously - As generative AI evolves and integrates into your operations, it's crucial to establish a feedback loop for continuous monitoring and assessment. Regularly evaluate the impact of AI-generated outputs, measure their alignment with business goals, and solicit feedback from both internal stakeholders and customers. This iterative feedback process will allow you to fine-tune the AI models, adapt to changing business needs, and address any unexpected challenges effectively.

Conclusion:

In the dynamic landscape of modern business, where budgets are often constrained and expectations are soaring due to advances in generative AI technology, CxOs must be strategic and deliberate in their approach. Generative AI presents an impressive array of possibilities to drive efficiency, innovation, and customer engagement, but success requires a thoughtful and informed strategy.

By understanding your business processes deeply, addressing historic constraints, embracing an incremental approach, fostering skill adaptation, making data easily accessible, and promoting cross-functional collaboration, you can position your organisation to leverage the full potential of generative AI. This isn't a one-size-fits-all endeavour; it's a journey that requires ongoing monitoring and adaptation. The true value of generative AI lies not just in its technology, but in the strategic decisions and leadership that guide its integration to shape a more productive and innovative company.