Artificial Intelligence is a powerful tool that has the potential to fuel new ways of working, improve decision-making, and increase efficiency in businesses across the board. In fact, innovations in AI are happening so fast that the future is nearer than it ever has been before, so the adoption of this new technology is key to future-proofing your business.
However, because everybody’s talking about AI, it can be hard to make out who is speaking the truth…and who’s just hyping it up. This can make it difficult to know where to start when deciding the ways in which AI can help your organisation’s operations, and cloud any judgement on where to start.
Know which problems AI is best placed to tackle
A common misconception surrounding AI is that it describes some kind of superintelligence that can be applied to any situation and, like a human can, have an intuitive idea of what needs to be done, with limited information. This is simply not the case. AI is only good at solving really specific problems, and you have to map out a clear target towards which it can be trained.
This is why the problems you choose to tackle with AI must be well-defined and goal-orientated. The task itself can be complex and difficult to solve, but it must have a clearly defined objective, with clearly defined parameters. It’s important to be able to measure the success of the AI algorithm according to specific KPIs.
What’s more, AI needs vast amounts of data to learn from. The amount of data you have must be one of your first considerations when defining the problem you want to tackle. On one hand, if there isn’t enough data produced by the operation towards which you are deploying your AI, then there won’t be enough data for it to be taught the minute differences and combinations that it needs to be able to accurately identify patterns and make predictions. On the other hand, if there aren’t enough data-points in the process in the first place, then it might not be the right place for you to implement AI, as it won’t be able to offer you any meaningful insights.
Taking the time to consider the problem and data involved first will save you money and time on experimentation and development further down the line. This doesn’t mean that you should just limit the scope to the easiest problems, but it means defining your goals to the point that you don’t waste time training an AI algorithm that misses the point of what you’re trying to achieve.
Potential AI projects should be orientated around minimising the costs and making strong predictions for core business problems, opportunities or challenges. Just like any other technology in business, AI should be viewed as a tool that can help make your organisation more effective, profitable or streamlined.
For example, when it comes to sales, an AI model can accurately predict a business’s sales using patterns found in historical sales data. In manufacturing, AI can predict any machine malfunctions before they even happen, potentially saving a company thousands of man hours and millions of pounds. In farming, AI can use satellite imagery and weather data to deliver accurate predictions that tell them what to plant, how often to water it, and how to fertilise it – saving a lot of guesswork and money.
Choose the right AI solution for you
A new generation of tools is emerging that provide end-to-end AI, allowing organisations to operationalise the technology at speed and for a reasonable cost. Whereas before, AI had to be developed in labs by highly-skilled technicians, this new generation of tools means that much of the nebulous technical work is done for you. Using an accessible interface, you can set the parameters, input your data, and define the desired goal that you want your model to be applied to.
The rapid development happening in AI software and hardware means that you want to choose a solution that’s scalable and future-proof. At the same time, there’s no point in implementing an AI that’s not bespoke to your enterprise. It’s therefore important to invest in an AI platform that’s flexible and capable of adapting to your specific needs. That’s why it’s important to review the technical capabilities of the platform and have a good idea of the different types of AI itself. For example, traditional Machine Learning can solve far fewer problems than Deep Learning, so opt for the latter if you want a viable solution that lasts for posterity.
In-house competencies must also be a top consideration – while it’s important to pick a solution that you can tailor to your own needs, you’ll also want to make sure that your tech team are able to manage and maintain your AI tools without the need for too much extra training or external consultancies.
Budget of course underpins all of the above considerations. Not only must you consider the future costs of running your AI solution, but you also want to make sure that the solution is robust and won’t start requiring expensive hardware – in this case, cloud-solutions are your best bet to keep the total cost of ownership to the minimum.
Spread the word
If you think that AI has the potential to transform your company, educate your organisation and senior management.
Because of AI’s complexity, demonstrating its possibilities to non-technical audiences and those working in other departments within your organisation may seem like a daunting task. But taking the time to make sure your colleagues are informed will not only lead to the faster adoption of AI by decision-makers, but it will also improve the efficiency with which you implement AI into your company once the decision has been made.
Company-wide basic knowledge of AI will be hugely valuable and will encourage certain changes in data practice management. This doesn’t mean everyone has to be or become an expert. The point is to begin to shape the mindset and strategy of the organisation around the core principles that allow AI to work, and what it’s particularly good at doing.
To harness the real power of AI, you can’t just go to an AI consulting firm and ask them to optimise your profits. The process of building an AI model must start within the organisation itself. To be more specific: it must start at the top.
This article was originally posted on –
Luka Crnkovic-Friis, CEO and co-founder, Peltarion
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