The hype surrounding AI being leveraged in businesses has reached fever pitch, to the point where many businesses are panicking about AI implementation.
Charlene Smith, sales director at Insight Consulting
They fear being left behind, worrying that their competitors already have a competitive advantage.
In our engagements, we’ve encountered many businesses that “want AI” but don’t know what they want it for – as long as they can report to the board that the business has invested in AI. In many cases, AI becomes a box-ticking exercise.
Understand your why
This is unfortunate, because when understood as the transformative technology that it is, AI can become a powerful enabler across a business. This means that businesses must first take a step back and, to use a strong human analogy, breathe and ask why.
Too many businesses rush to implement AI without understanding why they are doing it. Unless there is a clear why, the how becomes fairly futile. Of course, the fear of being technologically obsolete means everyone should absolutely be having the discussion but it should never lead to hasty, panicked decisions.
Working with an expert partner, strategic implementation of any technology, especially AI, trumps panic-driven adoption.
Beyond the buzzwords
Artificial intelligence means computers think and act in ways that seem intelligent. This can range from simple calculations to complex problem-solving – at scale. It all starts with data. The better the quality and relevance of the data, the better the AI solution, as it depends on the data.
Machine learning is how computers are taught to learn from data without needing to be explicitly programmed for each single task. The computers are able to find patterns and then make predictions or decisions based on the data.
Deep learning is an advanced type of machine learning that uses artificial neural networks with many layers, hence the word “deep”. These networks are able to learn highly complex patterns from vast amounts of data, often used for functions such as image and speech recognition.
Another word you’ll hear a lot is “algorithm”. An algorithm is best described as step-by-step instructions that a computer follows to solve problems or complete tasks.
Training data is the specific set of data used to train an AI model. An AI model is basically the brain of an AI system. It is the result of training an algorithm on data. The model is then used to make predictions or decisions on new, previously unseen data. One will regularly encounter the word “inference”, which is the process of using a trained AI model to make predictions or decisions on new data.
Understood this way, it becomes apparent that AI is a problem-solving, efficiency-enhancing tool. Tool being the operative word. It is not a magical solution. And so, in the rush to “implement AI”, businesses must ask: What problems do I need to solve, what efficiencies do I need to gain and how can I deploy this tool to address these?
Fancy an Uber?
Think back to the time before Uber. Sure, there were metered taxis for private one-on-one commuting, but their use was nowhere near as prevalent as the modern-day use of e-hailing. Uber, as a platform, opened up an entire mobility ecosystem and created demand that, quite simply, wasn’t there before. Can you remember work trips before Uber? International travel? Going out for a meal and possibly a drink?
AI should be seen in the same way. As a technology, it is transformative as it is able to solve multiple problems across an array of different contexts. In addition to this, it is – by virtue of existing – creating new demand for new functions while transforming existing processes.
Be practical
There is little use in throwing the kitchen sink at a business and hoping something sticks and something else improves. Businesses need to be practical with their AI implementation strategies. Start small, with targeted use cases. Work closely with an expert partner to highlight low-risk entry points.
These allow the business to focus on efficiency and a reduction in errors. For example, a focus on daily process improvements will lead not only to better business outcomes, but it’s likely to reveal more use cases. Personalisation is a key strength of AI, and when deployed strategically it can radically overhaul a business’s effectiveness.
It’s important to see an AI strategy as a continuous evolution and not a one-time implementation. Discard the check-list. One needs to continuously identify areas to improve in the business, and then adapt the AI solutions as business needs evolve and change. In many ways, it is about building an organisational culture of constantly building and adapting.
Partner wisely
When doing due diligence on potential partners to guide you along your AI journey, technical capabilities are obviously important. However, that’s not the end goal – it should be the starting point. Look beyond the partner’s technical capabilities.
Knowing everything you do now about what AI is, and how it should be implemented, seek out a partner that endeavours to deeply understand your specific business and its challenges, and who can help you to uncover areas where AI can add value to your business.
Prioritise expert partners who prioritise the importance of a customised, problem-solving approach because that is playing into the strengths of what AI actually is, and does. Finally, precisely because your AI journey will be an ongoing evolution, look for a partner that has a proven track record of building an ecosystem of ongoing support and innovation.