Artificial Intelligence (AI) has been a hot topic in the global tech scene and the business world at large. From large language models (LLMs) to generative AI, the possibilities seem endless. However, decision-makers and investors are faced with the challenge of deciding which AI applications to invest in and when. A recent VentureBeat article provides valuable insights on how to cut through the noise and deliver results with AI.
AI Adoption: Balancing Innovation and Risk
AI presents a plethora of options for large enterprises. Every single business function can get the AI treatment. However, the maturity and development levels of each solution vary. While it can be attractive to experiment with the latest innovation or create unique use cases, this naturally carries some risk. Decision-makers should think of these capabilities as a toolkit available to accelerate their vision while ensuring that the correct technology is used depending on the nature of each application.
Identifying Key Business Pain Points
The best approach to AI adoption is to start with the problem rather than the exciting new AI solution. Businesses can always further increase their efficiency, improve customer experiences, and reduce pain points. Identifying where these improvements are most needed will enable you to deliver the best ROI on your new AI solution. To do that, you need to look at your internal data as well as team and customer feedback. From there, you will be able to narrow your search for AI solutions.
Getting the AI Infrastructure Right
Any new technology carries question marks around exactly how it will integrate with your existing business processes and infrastructure. AI systems will work effectively only if the data they use is free-flowing, complete, and clean. In many organizations, this is simply not the case. Data management infrastructure can too often be overlooked. Starting small using AI in a contained setting or use case will enable you to feel confident that your infrastructure, policies, and processes are capable of more widespread adoption.
The Importance of Human Oversight
There is a serious data skills shortage that will impact the ability of businesses to effectively adopt AI tools. Basic data education throughout a company is required to identify the most applicable solutions, properly monitor and verify their outputs, and use these systems in the most effective ways. Businesses should not blindly trust what AI tells them; they need skilled human oversight. This expertise cannot be held solely in the data team — it needs to be from the top down and right across every department.
Betting on the Right Solutions
Currently, the most talked-about new use cases for generative AI are within marketing — particularly copy and imagery generation. However, any new tech attracts businesses dreaming about new use cases, which often results in existing use cases not making significant progress. The recommendation is to think about how AI can accelerate progress in resolving existing pain points, which often do not require the generative component but instead rely on the foundational understanding of unstructured data.
Remember, identifying the best AI solution for your business is only the first step. You need to have the infrastructure, buy-in, internal expertise, and checks and balances to ensure you get the most out of it.
