Generative AI helps companies reduce costs, create workplace efficiencies and improve the customer experience. How do they take the next step of putting their AI strategy into operation?
No longer simply a technology, artificial intelligence (AI) has quickly evolved into a culture expectation. Regardless of the industry or size of a company, employees and customers now expect organizations to use AI throughout their products, processes and interactions. A Forbes Advisor survey found 64% of business owners believe AI will improve customer relationships.
Companies across the spectrum have spent the past 18 months creating a strategy for using generative AI to reduce costs, create workplace efficiencies and improve customer experience. These organizations have the budget needed to invest in AI technology. However, many are struggling to take the final and critical step: putting their strategy into operation.
Why does the gap between strategy and operation exist?
Many organizations make the common mistake of viewing AI as being comprised of one bucket — the large language models, such as Chat GPT and Bing. However, AI includes two additional buckets: private foundational models and workflow/processes. Companies that overlook these are often unable to identify the best use cases to transform the organization. Without the right use cases, they are unable to take the important step from strategy to operation.
When companies don’t bridge that gap, they unnecessarily spend significant money that could be saved through AI use cases. Additionally, organizations not embracing AI are not going to disrupt their business model with new ways of growing revenue. As their competitors successfully bridge the gap, those organizations stuck in strategy mode will quickly fall behind — and it’s not likely that they will be able to catch up.
Best practices for bridging the gap
After working with clients of all sizes and industries, we’ve developed core best practices that help organizations successfully move into the operations phase of using AI to transform their business.
- Create a prioritization framework: The first step is to determine what outcomes the organization wants from AI. Are you looking for cost savings? Incentives? To improve customer experience? Disrupt your business model? Generate revenue? By establishing a list of desired outcomes, the organization then can create a prioritization framework.
- Evaluate use cases: Using the framework, the organization can review potential use cases to determine the best projects to deploy. C-level leaders can then use their understanding of their business, without needing extensive knowledge of AI, to decide where they need to focus the company’s resources to help solve their most pressing business problems.
- Evaluate the data state: The results of an AI project largely depend on the quality and structure of the organization’s data. By getting the organization’s data ready in terms of data management and processes, you can create a data model. The team should make the data context-aware for industry knowledge as it relates to the relationship with the foundational model. Additional access rights to specific data for users may also need to be provided.
- Work with model orchestration: By using a line chain to create more than one large language model, the team can then orchestrate each model, such as for prompt engineering or multi-agents. At this point in the process, the team also addresses the relevancy and validation of the model.
- Consider scalability: The true benefits of AI are seen when it’s used across an organization to disrupt multiple processes. Throughout the process of moving from strategy to operations, scalability must stay front of mind. For example, if you are using an AI-dependent use case, then a high volume of calls to the API increases the costs.
Preparing for the future of AI
Each time a conversation regarding AI begins, the biggest concern typically revolves around AI replacing humans. As we’ve moved into the adoption phase of AI, it’s increasingly clear that this fear will not become a reality. Humans will always have a significant role in all aspects of business. AI simply cannot replace humans, even on a small scale. However, the humans who use AI are apt to replace the humans who do not embrace the AI technology.
Rather than replacing humans, AI provides the innovation and ability to process large volumes of data so it can be used to successfully disrupt current and long-standing business models. However, these results only come to reality if companies focus on moving from conceptual ideas to concrete utilization. By bridging this gap, companies can transform their business by reducing costs, creating workplace efficiencies and improving the customer experience.