In the rush to implement AI, businesses must ask, what problems do I need to solve, what efficiencies do I need to gain, how can I deploy this tool. This is a continuous evolution and not a one-time implementation. You need to continuously identify areas to improve in the business, and then adapt AI solutions as business needs evolve and change. In many ways, it is about building an organisational culture of constantly building and adapting says Charlene Smith at Insight Consulting.
The hype surrounding AI being leveraged in businesses has reached fever pitch, to the point where many businesses are panicking about AI implementation. They fear being left behind, worrying that their competitors already have a competitive advantage. We have encountered many businesses that want AI but do not know what they want it for, if they can report to the board that the business has invested in AI. In many cases, AI becomes a box-ticking exercise.
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 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. Collaborating with an expert partner, strategic implementation of any technology, especially AI, trumps panic-driven adoption.
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 can 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 can learn complex patterns from vast amounts of data, often used for functions such as image and speech recognition.
Another word you will 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 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?
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 an entire mobility ecosystem and created demand that, quite simply, was not there before. Can you remember work trips before Uber? International travel? Going out for a meal and a drink?
AI should be seen in the same way. As a technology, it is transformative as it can 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.
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. Collaborate 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 is 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 is 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.
When doing due diligence on potential partners to guide you along your AI journey, technical capabilities are obviously important. However, that is 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.
