How to make artificial intelligence work for your business is one of the most pressing questions facing companies worldwide. It’s also an unavoidable question, with those failing to consider it at risk of being left behind and compromising their competitive edge.
While it’s been talked about for years, the practical applications of artificial intelligence in business are slowly being realised and it’s not a decision for organisations to leap into headfirst.
Don’t believe in the hype either; while AI adoption is certainly heating up, corporate adoption is still lagging behind, giving organisations the time and space to experiment and pilot AI.
AI can inform business strategy, revolutionise customer service and improve recruitment and retention, but with so much noise surrounding AI at the moment it’s difficult to know what to invest in and what to avoid…
The time is now
Last year saw the publication of the first novel to be written solely by AI and while it won’t be winning any literary prizes it certainly illustrates AI’s arrival as a reality of the modern world.
For decades, AI has played the role of the villain in Hollywood blockbusters. A far cry from the handy virtual assistants most of us keep in our kitchens.
Just as AI has integrated seamlessly into our daily lives, it’s doing so in business too. We’re already seeing AI playing a role in customer experience, process automation and predictive analysis. In short, AI is a time-saver and a problem solver.
AI can help all sizes of businesses with data processing and completing manual tasks. In fact, it’s estimated manufacturing and sales and marketing will account for over two-thirds of AI business opportunities.
However, a desire to be seen at the cutting edge of technology doesn’t necessarily correlate with intelligent business strategy.
It will be the organisations able to harness AI to achieve their long-term business goals, without getting caught up in the hype, who will reap the rewards.
To buy or to build
Once you’ve accepted AI is an enabler of business growth and isn’t to be feared, you need to decide how best to use the technology.
Does it make more sense to build technologies from the ground up, or purchase third-party infrastructure as and when you need it? The likelihood is a hybrid of the two.
Building AI infrastructure allows businesses to customise technology to your exact needs. Compared with investing in pre-packaged solutions, it allows for flexibility to modify key functionality when you need to.
The general standpoint in the industry seems to be that, unless you’re a dedicated AI product company, if the product already exists, buy it and invest time and money in aligning your business strategy to maximise its potential.
One major problem with building AI is that it’s a historically academic field, with clued-up employees few and far between. Just like in cyber security, the sector is experiencing a skills-gap due to the time it takes to train and recruit AI specialists.
However, as the skills gap closes and the application of it in business becomes the status quo, the future is looking bright.
Making a smart investment
Research shows 84 percent of businesses agree using AI platforms to solve burning issues will see them race ahead of the competition, but it’s important to understand it’s a marathon, not a sprint.
Just like most investments, simply throwing money at AI won’t create business value. No single technology will transform your business overnight. Research and experimentation are key in identifying and implementing a strategy that performs well for you.
AI is only worth investing in if it serves a purpose for your business. Which processes can AI streamline or make more efficient? Which tasks can AI automate, freeing up staff to spend more time on other more important things? Applications like Artificial Neural Networks and Machine
Learning can save time and money across departments including but not limited to sales, finance, recruitment, legal and marketing.
Building, rather than buying AI, means blazing the trail, so expect speedbumps along the way. Success will come with learning from mistakes and ironing out hiccups to make platforms and applications as efficient as possible.
A good example is how AI is being used in the banking industry which is infamous for time-consuming administration. When it comes to identifying fraudulent activity and transactions, machine learning technologies can detect unusual behaviour to prevent accounts from being compromised, giving customers a greater sense of security and reducing the arduous task of manually reviewing requests.
The evolution of AI
AI as we know it is built on complex maths. It’s revolutionised the way companies operate and will continue to do so, but not at the rapid rate we’ve experienced in the past decade.
While we’re still at the beginning of the AI journey, much of the critical groundwork has already been laid. Future advancements will be a result of continued machine learning and deep learning, plus problem-solving.
It’s positive news for smaller businesses, though, who can focus efforts on refining AI platforms rather than having to rip-up and replace every few years.
One of the biggest barriers to artificial intelligence development in business is the amount of data needed to fuel deep learning. Researchers are working on ways to fast-track deep learning using smaller amounts of data. If possible, this will unlock the potential of AI to assist in a wider scope of tasks.
The biggest fear with AI is that it will replace human employees and automate jobs. However, the opposite is true; AI has the capacity to empower every employee to achieve more with less but, as the saying goes, you have to be in it to win it.
By Craig Lodzinski, chief technologist for developing technologies, Softcat