Check out your news feed. Open an app on your mobile device. Chances are, there’s a little Artificial Intelligence (AI) powering it. Like many other technologies, AI has weaved into the fabric of our daily lives and work. It is transforming our world and its potential continues to grow.
Many industries are racing to implement AI and reap benefits – lower costs, find efficiencies, and increase the bottom line. This is the new gold rush. But the technology itself is complex, and it is one that is still in rapid evolution. Businesses embarking on an AI journey need to be mindful – there are several pitfalls when it comes to investing in this technology.
In building our Data Pulse study, we gathered insights from industry leaders and experts to study how businesses can be successful with AI implementation. Here are the top three tips we discovered from our panel of experts:
Handling unprecedented amounts of data successfully requires an organisation-wide data-first strategy
Success with AI depends on how much value businesses can extract from data. Not just with AI, data can absolutely make or break businesses. It is therefore imperative for organisations to adopt a data-first approach – having decision-making from infrastructure to talent acquisition all ladder up to support the data value chain.
A solid data infrastructure is a strong foundation, not just to handle the exponential growth in data volumes, but also to ensure data stays shielded from unauthorised access. The amount of data required to gain actionable insights from AI will mean your exposure to security risks will get wider. For example, as we increase our use of external cloud services to build AI models, the process of uploading and downloading data may expose us to new risks. This includes new forms of man-in-the-middle attacks, where attackers secretly intercept and alter data.
Do everything to source for the right talent, but don’t forget to improve your internal talent too
Finding good talent to deploy new, exciting technology is always a challenge. It wasn’t easy to find help with analytics just a decade ago. AI’s potential is much greater, though, and organisations must do all it can to search for the right people to run it.
The first step involves knowing what type of skillsets to look out for. AI is a big buzzword these days, so there’s a good chance for a flurry of applications to come through – but how many of these will be able to drive your business forward with AI? Look out for candidates who are skilled in math, statistics or programming. Working with AI means a lot of testing. AI experts at applying algorithms on real systems and making constant adjustments until they get to the eventual application of the concept.
Do note, however, that these talents are in high demand so it’s worth considering how to attract them. Try profiling the organisation as an exciting place to work with AI. You could build a presence in AI data science meetups, or hackathons, raising interesting challenges that give potential talents a taste of the company’s vision and what they could potentially work on. Another way is to showcase best-in-class computational architecture – the kinds that top AI talents seek out for the opportunity to work with the most advanced technologies in their industries.
While the search for top talent never ends, don’t forget that upskilling your existing talent pool is equally valuable to the organisation. Investing in your staff and junior talent can breathe new life and inspire new ways of solving AI challenges. Train them with the prerequisite skills of AI, and launch incentives that encourage them to drive the organisation’s AI capabilities forward. A mix of both experienced and fresh talent would ensure that the business is ready to tackle new and increasingly complex AI challenges.
Evaluate the success of AI by its impact to bottom line growth
AI strategy will differ in every organisation depending on culture, level of technical maturity, and nature of business. The pace of innovation in AI also means there is no ‘one size fits all’ approach to implementation. That said, to get started with AI, you’d need to demonstrate success in terms of profitability, revenue growth, or increased savings. AI is a large undertaking and the board needs to see returns in dollars.
Start by identifying top business challenges and figure out how they can be solved mathematically. Look for areas that can make the most impact in the least amount of time. Some of these challenges could be: noticing variations across large data sets so the company is in a better position to act on changing circumstances; improving customer service to enhance overall customer satisfaction; or boosting operational efficiencies.
Finally, it’s also worth looking long-term. As the business becomes more reliant on AI, data volumes will very quickly increase beyond what the company’s current systems are able to handle. It’s important to demonstrate a robust infrastructure roadmap to meet the organisation’s AI needs for years to come.