What makes an AI project successful? In some ways this is a fairly subjective question and one that will have a different answer depending on the business context. However, it’s an important one to consider early on because it’s vital to define your metrics for success when you’re investing in something like AI – and to capitalise on all the expert support you need to make it happen.
There is huge enthusiasm for integrating AI projects into business workflows and objectives in the coming years and, as a result, the AI market is predicted to have $190.61 billion market value in 2025. However, the learning curve can sometimes be steep – if you’re keen to achieve the success you want for your AI projects these are the key mistakes to avoid.
- Not having a clear objective. What’s the problem you’re actually trying to solve? So many organisations begin AI projects just hoping to stumble across genius solutions rather than having clearly defined goals from the very beginning.
- Shooting for the moon. Be ambitious, sure, but don’t create unnecessary issues and obstacles by trying to transform everything you do internally with AI overnight. It’s crucial to develop a process for experimenting and validating what you’re doing with AI – this is much easier to achieve if you’re not trying to build Rome in a day.
- Problems with data. It’s easy to forget that AI is entirely reliant on data. The AI that you work with is really only as good as the data that it consumes. Data should be reliable, available in sufficient volumes and well structured data sets. Otherwise it may not actually add that much value.
- Siloed AI solutions. Any AI projects that you pour investment into should be part of a broader transformation strategy – or at least connected in some way to wider initiatives otherwise scalability and return on investment are going to be pretty restricted. Try to focus on projects that will add AI capability for the business as a whole rather than one tool for a single team.
- Moving forward without those metrics for success. As mentioned, this needs to be a priority for your AI projects. This is science that you’re working with here so the right approach is to establish your hypothesis and then test it and evaluate the results. The only way you’ll be able to see what works – and where there is room for improvement – is if you’ve got something to measure your progress against.
- A lack of internal collaboration. AI projects require cross-team collaboration to ensure standardised development processes, share learnings and deploy solutions at scale. If your data science team is working on this in isolation then the chances of failure shoot up.
- Working without the right expertise. As an area that is evolving at a breathtaking pace, AI represents plenty of challenges and blind spots for those who aren’t living and breathing it every day. If you don’t have the expertise for AI projects in-house, working in partnership with those that do can help you to avoid a whole range of costly and time consuming mistakes that come from inexperience or ignorance.
Of course, our top piece of advice for any organisation focused on ensuring AI projects are successful is to work with Samuylov.ai. Our team has a wealth of experience in fostering collaboration between teams, defining clear objectives and finding data solutions. We can also bring the insight and expertise to steer you along exactly the right path for the individual needs of your enterprise and ensure that you avoid the most basic pitfalls.
