In the last decade, AI has primarily been the domain of tech giants, which built internal teams to develop AI for enhancing core business functions. Today, however, the landscape has changed. Businesses across various sectors are successfully adopting AI without the need for extensive in-house expertise.
Today, you don't need a massive team of Data Scientists to develop and implement AI in your organization. What's crucial is having clear goals, setting up the right team, technical guidance, and a sound measurement framework. In this article, we'll cover the basics of setting up an effective AI strategy.
An AI strategy is a well-defined plan detailing how your organization will use AI to meet business goals, improve operations, and secure a competitive edge. It's essentially a long-term plan that includes a detailed analysis of necessary steps to transition from your current state to your desired future state.
Currently, many companies lack a formal AI strategy, but you can often gauge a company's engagement with AI by their public communications. Observing your competitors' mentions of AI might give you insights into their strategies. While some industries are still experimenting with AI, others haven't started, presenting a unique opportunity to gain a competitive edge by adopting AI now. Over the next five years, early adopters of AI are likely to lead their industries.
AI strategies can be broadly classified into three types:
Operational Efficiency: This strategy uses AI to enhance process efficiency and reduce operational costs. Applications include automating tasks, improving supply chain operations, and optimizing data handling.
Product Enhancement: Here, AI is used to upgrade existing products or develop new AI-powered offerings. This could mean integrating AI features into consumer electronics or crafting innovative AI-based services.
Customer Experience: AI tools are deployed to personalize interactions, enhance service delivery, and anticipate customer needs, ultimately boosting satisfaction and loyalty.
Today, the availability of ready-to-use AI tools means that you can achieve these goals faster without needing to develop your own models from scratch.
You don't need a large team to start with AI; what you need is executive support, a cross-functional team, and someone with the technical know-how to guide your AI strategy. Most companies already have these resources internally, except perhaps someone to lead AI strategy development. This leader should ideally have a strong technical background and experience in data science and AI.
How do you know who the people within your organization are that will be a good fit for the AI team you are building? Not everyone fits, and attitudes towards AI are one of the reasons. You want to identify technology-positive people in positions that have the knowledge needed to help move the company in the direction you want.
If your team lacks AI competencies, you will not easily be able to recruit a person with the right skill set. Simply put, how will you know how if the person is fit for the job?
The people with the competence to do these jobs are sought after and know it. They generally choose from the positions available, not the other way around. So you need to make sure you have a compelling pitch if you are trying to recruit such a person.
The solution I would suggest to these issues is to initially work with an advisor, like myself, to help you set up a team, goal, initial plan, and time-tested processes. If you are interested in knowing more about my services, you can book a three-session advisory time straight into my calendar, or you can contact me through the form here.
Goals will vary by industry, and with the rapid advancements in AI, it's important to regularly reassess them. While many companies focus on enhancing operational efficiencies, they should also consider goals like improving employee productivity and reducing workloads.
Arguably, one of the most important parts of executing the AI strategy is measuring the results. Otherwise, it will not be possible to see improvements, nor when you miss the mark. The measurements should be both quantitative and qualitative. Quantitative can be internal surveys on employee satisfaction and workload before and after the new technologies are taken into use or direct numerical measurements such as the average number of tasks a team performs. Qualitative measurements can be user interviews and feedback forms, both will help you assess whether the direction you are taking is correct.
Failing to develop and execute an AI strategy is increasingly risky. In the next few years, what was once a competitive edge will become a baseline necessity. Your competitors' AI advancements will likely lead to better employee conditions, which could result in higher turnover at your company if you lag behind. In the worst-case scenario, you could lose top talent to competitors, potentially leading to a downward spiral for your business.
AI is no longer an optional technology but a crucial tool for maintaining and enhancing competitive advantage. The time to act is now, ensuring your business remains relevant and competitive in the evolving market landscape.