Today, the term artificial intelligence (AI) is thrown around rather generously. As businesses around the world become more open to making waves and ditching legacy technologies in their quest to become data-driven, an ever-increasing number of tech deployments are claiming to use AI or machine learning (ML). But, frankly, it’s often not true AI that is being used. The problem is, AI doesn’t have a widely recognised definition, so it’s hard to draw a line between what is AI and what isn’t.
Advanced analytics vs AI
In recent years, multiple businesses have invested in tools and technologies to help them understand their data, ultimately looking to maximise efficiency and provide the best possible experience for their customers.
At the moment, many organisations are utilising technology that constantly monitors their systems and uses past metrics to identify patterns. This is a prime example of something that is often branded as AI or ML — and while these systems may be drawing information from patterns and forwarding insight to someone who can act on the information, in reality this is not AI, but predictive analytics.
I’m not saying that advanced analytics of this kind is fruitless. It’s a powerful set of tools that gives businesses advantageous consumer insights and allows them to make lasting and impactful decisions. However, as an industry we can’t be complacent. To maintain progression and deliver the ever more personalised approach that consumers are demanding, businesses need to go the extra mile and reap the benefits of greater efficiency, real-time data analysis and automated decision making.
E-commerce businesses are a great example: as consumers, our search and purchase history is analysed by retailers to generate a wide range of recommendations for our next purchase – but some are completely off base, as you’ve probably experienced. The stage we want to get to is the ability to accurately tell customers what they want, before they know themselves. And the way to get here is by taking significant strides towards true AI.
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Harnessing the full potential of big data with AI
Whatever the organisation, consumers insist on seeing instant results – with personalisation being ever more important. If this isn’t happening, businesses will start seeing ‘drop off’ as customers seek an alternative, which, in today’s competitive market, could prove disastrous.
There is an opportunity now for businesses to combat this by implementing true, bespoke AI models that can sift through vast amounts of data and make its own intelligent decisions. After all, the amount of data being generated across the globe is skyrocketing, and organisations are continuing to share their data with one another – so organisation and analysis at this level is a must.
However, it’s important to note that AI isn’t for everyone. The move to AI is a huge leap, so businesses must consider whether they actually need AI to achieve their goals. In some cases, investing in advanced analytics and insights is sufficient to help a business run, grow and create value.
So, if advanced analytics does the job, why invest in AI? Most AI projects fail because there is no real adoption after the initial proof of concept. Many organisations adopt AI because they are swayed by the term, not because it fulfils a business need.
Once a business has weighed up the costs vs benefits and decided AI is for them, the first step is to clearly define what changes it wants to make, and the desired outcomes of these. Much like every other business transformation initiative, there needs to be a clear roadmap in place for delivering automation within an organisation. My advice is to start with the application of AI for internal operational effectiveness, then you can progress into use cases that directly impact customers.
For businesses to reap the full benefits of AI in the long term, its intelligent model must be scalable. On an operational level, businesses simply can’t afford to have their models slow down with growth. The point of investing in such automation using AI is to elevate efficiency, but without scalability, long term efficiency is far-fetched.
We’ve already seen several organisations implementing this cutting-edge AI, using model driven insights to deliver compelling customer experience, ensure compliance to regulations, monitor operations, and help with business decision making and forecasting.
A great example is LungLifeAI, a pioneer in LiquidBiopsy® technology. LungLifeAI uses machine learning and artificial intelligence to enable life-saving early diagnosis of lung cancer. AI algorithms have reduced analysis time by nearly 70% thereby accelerating LungLife’s efforts to greatly reduce the impact of a disease that claims around 400 lives per day.
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Implementing AI Responsibly
As so many of our future decisions will be made by AI, it’s vital that all businesses make the effort to implement AI responsibly from the get-go. This is even more critical for any organisations making decisions with moral implications.
When you are deep into data processing, you have to factor in and address ethics and bias ahead of any business goal. If not implemented cautiously, faulty or biased AI applications risk compliance breaches and can end up causing not only reputational damage, but societal damage as well.
A big part of a business’s responsibility is to ensure that AI is explainable. In other words, AI must always be able to justify how and why a decision has been made. This is essential to ensure humans aren’t giving up full control and we can still refine, challenge or alter any decision.
Finally, responsibility cannot be brushed under the carpet once the AI model is deployed. Organisations need to monitor this continuously, taking on board real-world performance and user feedback to ensure their use of AI remains ethical.
There’s no denying that the future of business revolves around AI. Some industries are already deploying AI to automate business processes and gain in depth insight from their data. Now, to avoid being left behind, it’s time for organisations spanning all industries to follow suit and start implementing true AI – as long as it makes business sense to do so.
