Whether you work in retail, banking, transport or the public sector, AI will be an integral part of the way you do business in the future as it has huge potential to improve decision-making, increase efficiency and power new ways of working; the pace of innovation is currently faster in AI than in any other field.
This means that every business, no matter what their sector or size, should be starting to prepare for their AI-powered future. Unfortunately, the world of AI is complex, and riddled with confusing jargon and hype that can lead companies astray.
Here are a few first steps to help get your business AI-ready.
Learn the Fundamentals of AI
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. It’s therefore essential that you educate your organisation and senior management about AI and its potential.
This doesn’t mean everyone has to be or become an expert, and you won’t suddenly need to know the intricacies of neural networks and deep learning algorithms. 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.
At the same time, company-wide basic knowledge of AI will be hugely valuable.
Simply introducing these basic concepts around AI may encourage certain changes in the organisation and data management practices. So, to succeed with AI, knowledge and strategy are best distributed top to bottom to avoid misunderstandings and conflicting approaches to the technology.
Identify the problems in your business that AI could realistically tackle
Potential AI projects should be orientated around core business problems, opportunities or challenges. It’s important to bear in mind that just because AI can fulfil a wide range of tasks, it doesn’t mean it can do everything. There are circumstances for which AI is not the best solution.
To create business solutions with AI, the problems being solved must be well-defined and specific. If the problem is too vague, it will be impossible to implement an AI model that works. The task itself can be difficult to solve, but it must be easy to understand, and have a clearly demarcated objective. For instance, the objective can’t simply be ‘make my company more profitable’.
A great place to start when identifying which problems you want to use AI to tackle is by listing as many problems as you can, considering the following questions: What are your most pressing problems right now? What parts of your business generate revenue but currently have low profit margins? Where would you like to cut costs? Where do you make a high percentage of errors in your work? Looking at the measurable aspects of your business operations will stand you in good stead.
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. Automation and acceleration via AI could make certain revenue streams more profitable and increase employee satisfaction by performing repetitive and annoying tasks.
AI can also help you better understand what generates costs and identify areas that could be optimised or changed to reduce them, and a well-trained AI model has the capacity to perform with far less margin of error than humans.
The Kinds of Business Challenges AI is Already Capable of Solving
Let’s cut through the hype: AI is not capable of mimicking human intuition, and isn’t capable of making multiple decisions off-the-cuff – it has no ‘gut feeling’ like we do.
AI is all about specific applications and is already part of our daily lives in search engines, voice recognition or the tailored suggestions you receive from your music and video streaming services. But when it comes to solving business problems, that’s only the beginning.
Sales can be massively impacted by the implementation of AI. By pulling data from sales tools, an AI model can accurately predict a business’s sales using patterns found in historical sales data.
Manufacturing is another area that can be transformed and made more efficient with the use of AI and machine learning applications. For example, there used to be no way of telling when a machine would wear down and start making errors. But with the right data, AI can predict any errors before they even happen, potentially saving a company thousands of man hours and millions of pounds.
In fact, its benefits can be reaped across all industries. Another example is how AI can help farmers by using satellite imagery and weather data to deliver accurate predictions, helping them to know what to plant, how often to water it, and how to fertilise it, saving a lot of guesswork and money.
What you’ll need to implement AI effectively
AI tools used to be geared towards academic research and proof of concepts. But, now a new generation is emerging that provide end-to-end AI allowing organisations to operationalise the technology at speed and for a reasonable cost.
You should choose AI tools with the consideration of your goals, budget and available in-house competencies. This will subsequently help you to work out the amount of time it will take you to develop and AI model bespoke to your specific needs, and that all important total cost of ownership. After all, since software and hardware used for AI is going through rapid development, it’s important to make sure the solutions you choose are scaleable and future-proof to avoid costly maintenance later on down the line.
Additionally, review the technical capabilities of the platform. Traditional Machine Learning can solve far fewer problems than Deep Learning. Since AI is bound to affect multiple aspects of your business, go for a tool that makes collaboration across the organisation possible. This will require going for the most versatile option available, without overstretching your expertise or exceeding your budget.
Finally, data is the fuel to any AI solution and can present itself in unexpected places. So, the next step is to seek out possible sources of relevant data for each problem on your list. You may not have enough of the proper data for all of the problems on your list. If that’s the case, think about how you can create, find or even buy that data. The mere act of looking for data in your organisation can help spark helpful practices that yield exactly what you need to get an AI project off the ground.
