Building an AI Strategy: Steps for Businesses to Get Started

In this series of blog posts, we’ve been covering how much of what is badged “AI” is actually based in: machine learning, deep learning, statistical analysis and mathematical modelling.  When these techniques are used in small business along with automation, they can help drive efficiency, innovation, and growth.  

If you’re wondering where to start, look at this 10 step checklist to help you develop and implement a successful AI strategy. 

1. Define Clear Objectives:  Firstly, decide what it is you want to achieve: from improving customer service with chatbots, predicting sales to enhancing supply chain logistics with optimised routing.  Defining clear and measurable objectives will guide your strategy and help quantify success. 

2. Assess and (if needed upgrade) your data infrastructure.  Machine learning and deep learning model are only as good as the data they are trained on.  If you are going to develop these algorithms, you need to invest the time to understand the data you have available and if they are suitable for the algorithms you’d like to use.  That means you also need to critically look at your data collection (do you need more data before you can start), data management, data access and data processing capabilities. 

3. Prioritise data quality and preparation.  To make sure your business gets the best from algorithm development, make sure you focus on developing a high-quality, relevant, and properly formatted data set.  Don’t underestimate the time this can take, but make sure you invest time here.  It might sound a bit “dull” but it is essential to maximising the rewards of developing solutions.  Talk to your data science team about the steps involved in creating the data set such as data cleaning, normalisation, and feature extraction to make it suitable for use in computer algorithms. Investing in this phase is crucial, as it lays the foundation for effective model training.  It’s also a great opportunity to upskill your team so moving forwards, they all understand the importance of data. 

4. Select solutions aligned with business needs.  Again, don’t rush but spend time exploring the solutions that fit your business objectives.  Do you need custom-built models or could you use off-the-shelf software?  It’s important to think about time, cost and consider factors such as ease of integration with your existing systems, scalability, and user-friendliness.  Fundamentally, you need to make an evidence-based decision on your current business needs and future anticipated growth. 

5. Plan for talent acquisition and training.  A key decision you will need to make is whether to develop the models and technology in-house, use external expertise or off-the-shelf- software.  Maybe to do all three you need to develop a training plan for your inhouse data science team (or perhaps build your data science team from scratch).  Either way, it’s important that you provide training for your team to ensure they are “AI-literate”.  Can they understand the basics of the technology you’re using/deploying: do they know its limitations, use cases how it should be maintained, what important ethical and legal conditions there are about the data used / collected and how the technology adds value to the business. 

6. Set a detailed and realistic budget.  It’s easy to forget that you need to set aside a budget for data storage, technology integration, software costs, hardware updates (if needed), training and talent acquisition (if needed).  By determining these costs and therefore the true cost of the technology, you can assess the return on investment of implementation and decide if you should use it. 

7. Initiate a feasible pilot project.  Once you’ve determined what technology solutions are best for your business objectives, start with a small-scale pilot project.  Remember to document everything you do, and determine the KPIs that will allow you to assess the impact, gather and use insights, and understand the challenges of technology “AI” integration without overwhelming your resources.  This will allow you to develop the foundations for scaling.

8. Develop a comprehensive scaling strategy.  Aligned with the feasibility study, it is important to develop an approach to scaling the use of the technology to other priority areas within your business.  Develop and establish a clear roadmap for integration including timelines, milestones, and key performance indicators (KPIs). 

9. Continuously monitor performance.  As with all areas of your business, it’s important to monitor performance to allow you to adapt your strategy based on feedback and results.  With your team, make time for regular reviews to identify opportunities for further optimisation of results, new use cases and refine the roadmap for scaling. 

10. Stay informed about compliance and ethics.  Although this is last on the checklist, it is absolutely critical to the use, role out and maintenance of technology solutions.  Make sure you are aware of and compliant with all data privacy laws and ethical standards.  Consider working towards regulations such as ISO27001 for your company and for your team standards such as accredited researcher or data science practitioner.  By staying informed about the latest regulations and industry best practices will ensure your AI and technology strategy respects customer privacy and promotes trust. 

By following this detailed checklist, your small business can approach AI integration with confidence.  Remember, the success doesn’t solely depend on technology—it’s about strategic alignment with your business goals, company culture, and customer needs.  As you embark on this journey, focus on the value technology can bring to your business and let it be a driving force for innovation.  Don’t forget to let us know how you get on. 

By Dr Sophie Carr

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