This year the floodgates have truly opened for Artificial Intelligence (AI) and Machine Learning (ML). Generative AI and Large Language Models have been a hot topic, and the release of ChatGPT showed the world just how accessible AI/ML programmes really are. Companies like OpenAI have allowed the public to use AI and experience this cutting-edge technology first hand.
With a rise in popularity, AI tools have become essential in today's business landscape, offering companies the opportunity to enhance products, streamline processes, and unlock new revenue streams. However, deploying AI/ML solutions requires a well-defined strategy that aligns with business goals and ensures successful implementation. Today, I wanted to explore five key pillars that should help guide a company's AI implementation plan:
I am a firm believer in having to take a step back to understand what a company or team requires to succeed in their objectives. The following guidelines will help us achieve just that.
Firstly, identify and select the right use case – prioritise the most lucrative solutions that align with business goals and provide measurable benefits. This simple statement is easier said than done. It’s quite an art to be able to connect innovative perceptions to what is possible in your tech and data environments.
I have frequently found myself in a situation where the best idea for AI requires extensive investigation in terms of process, technology and data required for it to be practical.
There are several frameworks available that can assist you in ranking 'AI Initiatives' based on their complexity and potential business opportunities. To effectively utilise these, you'll need to consider several key factors that can influence the overall scoring of your AI initiative. I'm going to offer some insights on the factors that I believe play a significant role in providing a clearer understanding of opportunity versus possibility. Here are seven crucial factors that will guide you in achieving this goal:
Secondly, equip your teams with the appropriate tools and techniques - it's essential to empower your teams with the relevant technology, methodology, and training that will allow them to carry out AI/ML projects effectively.
Thirdly, guarantee that end users are able and willing to use AI - it's important to consider the usability and acceptance of AI/ML solutions by the end users. This is fundamental. There's no benefit in creating a technically flawless solution if it's not user-friendly for the intended audience.
Lastly, consider scalability and reusability - make sure to plan for scalability and reusability from the beginning of each use case. By crafting solutions with scalability in mind, we can enhance the value and lifespan of our AI/ML deployments. Moreover, there are certain components that can be reused from one AI proof-of-concept (POC) to another.
Each AI/ML project should be treated as a product or part of an enhanced offering to the market. To ensure clarity and success, the following aspects should be addressed:
Adopting a structured lifecycle approach ensures consistency and adaptability throughout AI/ML projects. You should consider these phases:
To maximize the value of AI/ML, we need to adopt a data-centric approach that leverages existing AI capabilities and accelerates outcomes. Consider these when adopting your approach:
Success in AI/ML deployment requires collaboration across different functional teams. Engage with the relevant stakeholders, such as:
Creating a successful AI strategy necessitates a comprehensive approach that considers clear direction, well-established structures, flexible lifecycles, data-centricity, and cross-functional collaboration. By aligning your AI/ML projects with these cornerstones, we can enhance our products, optimize processes, and foster innovation within your organisation. Viewing AI/ML as a strategic instrument position us for success in the constantly changing digital environment.
This guide was not written by ChatGPT nor an AI/ML application.