In a recent survey conducted by Gartner, 73% of CIOs expressed their intention to increase their investment in artificial intelligence (AI) in the year 2024. Similarly, a Deloitte survey revealed that 66% of respondents believe that AI is crucial for the success of their organizations. However, despite the recognition of the importance of AI, only 40% of businesses currently have a formal AI strategy in place, with only 38% of those believing that their use of AI sets them apart from their competitors.
One of the challenges faced by organizations when it comes to leveraging AI for competitive differentiation is the perception that AI solutions exist at either end of a spectrum. On one side are low-level tools and APIs that may help execute plans but often lead to misaligned demos. On the other side are existing platforms that embed AI into their features, which may not fully address unique challenges and opportunities faced by individual organizations.
To address this challenge, a portfolio approach to AI can be adopted, combining different types of AI to unify efforts across the organization. This approach allows organizations to integrate vendor-specific tools with bespoke AI solutions tailored to their specific needs. Understanding the roles of different types of AI, how they address business needs, integrate with existing systems, and contribute to strategic goals is crucial for executives looking to unify their AI efforts.
One type of AI that can be included in an AI portfolio is embedded AI features, such as writing assistants in email software or chatbots in content management systems. While these tools can help individual teams increase efficiency and achieve functional key performance indicators, they may not engage comprehensively with organizational data or advance unique business strategies. Knowledge assistants, on the other hand, use retrieval-augmented generation (RAG) to query internal data sets and provide valuable responses, enhancing employee efficiency.
Another type of AI to consider is streaming AI, which adds proactivity to AI action plans by monitoring dynamic streaming data and triggering workflows across systems and tools. Unlike embedded AI features, streaming AI can utilize data from various sources and provide a more comprehensive view for decision-making. By continuously querying new data against established business objectives, streaming AI ensures consistency across AI applications and amplifies the impact of individual and functional efforts across the organization.
In conclusion, adopting an AI portfolio approach enables organizations to integrate off-the-shelf solutions with new capabilities while leveraging existing infrastructure. This approach can help address the challenges of wrangling and harmonizing data, ensuring that AI and digital teams have real-time access to necessary data to reduce costs, streamline governance, and accelerate AI application development. Businesses that successfully balance their AI portfolios are likely to outpace those that stick to a monolithic strategy and spend time diagnosing its strengths and weaknesses.
