Implementing generative AI presents distinct challenges due to its disruptive nature and the rapid pace at which it’s evolving, touching on various vendors, applications, use cases, and permeating every aspect of business strategy and processes. To maximize the return on investment (ROI) from generative AI efforts, it’s essential to move beyond ad-hoc experimentation and adopt a strategic, systematic approach. Here are five best practices to help you gain the most business benefit from generative AI.
1. Set Your Holistic Gen AI Strategy
Defining a generative AI strategy should be an integral part of a broader approach to AI, automation, and data management. A modern data strategy needs to support the entire IT stack, addressing enterprise objectives for intelligent automation and a myriad of applications that manage transactions, analytics, and decision-making. For example, a recently launched platform like EDB Postgres AI demonstrates that successful AI implementations rely heavily on high-quality, well-managed data. Your strategy should outline strategic themes for generative AI within the organization, explaining how they will support various business objectives, such as growth, internal efficiencies, and cost savings. Additionally, identify how these strategic themes relate to your business model, processes, products, and services, and define outcomes that will drive business success.
2. Identify and Prioritize Use Cases
While generative AI has numerous potential use cases across your organization, it’s crucial to compile and prioritize a master list to ensure strategic alignment and avoid redundancy. Creating an AI collaboration area on your company intranet can facilitate knowledge sharing and coordination, allowing practitioners to share their projects and findings. This collaborative approach can help you inventory ongoing activities and identify new opportunities by mapping the use cases to customer journey maps or enterprise process maps. Whether you are well into implementation or starting new initiatives, conducting innovation workshops can help end users brainstorm and prioritize use cases. Focusing on both quick wins and must-have initiatives ensures a balanced implementation roadmap that delivers high business impact and drives strategic growth.
3. Experiment with Purpose
At the peak of the hype cycle, generative AI sees a lot of experimental tinkering. To maximize these efforts, encourage pilots and purposeful experimentation while guiding end users with corporate standards and budgetary considerations. Provide support and encouragement for end users to experiment with different AI tools and techniques, fostering a culture of innovation and ensuring alignment with organizational goals. For instance, if users are exploring AI speech-to-text tools, encourage them to compare multiple options, considering factors like cost and vendor preference. Empowering end users to act as innovators and testers can help identify the best tools and methods for the organization, driving meaningful advancements in AI capabilities.
4. Share the Guardrails
Balancing innovation with risk management is critical for successful AI implementation. Establishing organizational guardrails, such as corporate use policies and industry agreements, can help manage AI-related risks while fostering innovation. For example, the construction industry prioritizes data privacy and confidentiality, so educating users on the pros and cons of public versus private AI models is essential. Crafting corporate use policies can guide users in navigating potential risk areas, such as data privacy, while still encouraging exploratory projects. These guardrails ensure that AI implementations align with organizational priorities and regulatory standards, mitigating potential risks while promoting responsible innovation.
5. Make ROI an Early Part of the Discussion
Implementing generative AI comes with significant challenges, owing to its disruptive nature and the rapid speed at which it is evolving. It impacts a broad spectrum of vendors, applications, and use cases, influencing every facet of business strategy and processes. To harness the full potential and achieve a high return on investment (ROI) from generative AI initiatives, it’s crucial to transition from sporadic experimentation to a well-planned, systematic strategy. Here are five best practices that can help you extract maximum business value from generative AI.
First, ensure you have a clear set of goals aligned with your business objectives before diving into AI projects. Second, invest in talent and training to build a team capable of understanding and leveraging these technologies. Third, develop a robust data infrastructure to support your AI applications, as high-quality data is the foundation of successful AI. Fourth, be prepared to iterate and refine your AI models continuously. Finally, integrate AI into your existing workflows and business processes to ensure it complements and enhances your operations, rather than operating in isolation.
