How AI is coming into its own

The subject of AI has long been on the lips of business leaders. But rather than simply being a subject of aspirational keynotes, it has moved from theory to practice in businesses.

It’s high time. The exponential boom in data means businesses have a huge opportunity on their hands to gain real-time, tailored insights to enhance business agility. However, they also risk being swamped by data if they don’t embrace intelligent tech soon.

Witnessing the value of AI to business is a key step towards revolutionizing the way you work. To help, I’ve gathered up some of my favorite ways AI is being used in today’s businesses:

Pattern detection is optimizing our workloads

Data is now widely viewed as the most valuable commodity, serving as a powerful tool to drive productivity and connected experiences. With the increased uptake of digital services over the last couple of years, businesses have likely accrued huge amounts of customers and employee data. For businesses chasing productivity gains and looking to revive growth following the pandemic, unlocking the full potential of the data they hold is more important than ever. That’s why we’re expecting to see business leaders investing heavily in intelligent software that detects patterns in their data and gleans valuable business insights, that helps them to see around corners.

Take HR for example: smart HR software using AI technologies will crawl existing data on staff tasks, timesheets, and workloads, before predicting the amount of time it will take to complete a certain assignment. Through this, business leaders will be able to optimize their staff’s workloads by remotely managing their time and matching different employees to the tasks best suited to them. The software will also track data on sick days, holiday requests and shift scheduling, supporting managers to balance staff leave and wellbeing whilst maximizing productivity.

AI in finance and accounting software will detect patterns in cash flow too, identifying anomalies and helping businesses to make important strategic decisions, such as where to increase investment and how to save across the business elsewhere. This isn’t about monitoring or replacing employees. Rather, pattern detection software is simply providing leaders with the accurate and timely insights needed to deliver the best decisions for their business.

When AI works right, and a task that used to take days is reduced to minutes, it’s a magical first moment for the user. Building AI to fit a use case for business is the easy part, but the other wider ethical questions that the rising tide of AI brings are broad and currently very topical. Top of mind here has to be the data privacy of individuals and the laws that protect them.

Natural language processing is becoming rooted in our day-to-day lives

Natural language processing (NLP) is software that uses AI to work out exactly what we’re asking for, even if we say it in our local vernacular. While, in many ways, we’re already using it daily by giving voice commands to home-based smart assistants, we’re expecting to see this technology increase its prevalence across our working lives from now on.

Let’s say we’re chatting with a colleague on Slack, for instance, existing solutions mean we can automate tasks within that very conversation. If we type, “Hey, I can’t find last week’s timesheet”, integrations with applications like Sage can find your timesheet automatically, saving you and your colleague time. Soon, new natural language processing solutions embedded into the collaborative software we’re using every day will be able to detect this request when issued vocally on a video call, for example. It will understand what we mean, and then find the required resource for us — all in a matter of seconds.

And it won’t just exist in our PCs; it’ll be able to travel with us, too. Our smartphones will house the natural language processing tools we use every day for work. Once connected to the central business management solutions, we’ll be able to ask our phones for updates on our next overdue invoices, our balance, or business forecast, all as if we were speaking to a super-efficient colleague.

Natural language processing is even set to transform user interfaces (UI); for instance, instead of using standard website form fields in which we enter names and addresses to sign up for a service, the software can tailor the form to become a spoken sentence; for instance, the phrase: “I am Susana, this is my email, and I’d like to create a new account.” With natural language processing software, we can reduce our workload, humanize our services and boost customer conversation rates with minimal effort.

AI is becoming more generative—and facing more regulation

It was clear many years ago that AI would be able to take on menial tasks and free up people for more innovative, strategic, and consultative work. However, we assumed truly original creativity and impactful decisions would remain solely for humans. Yet, as artificial intelligence becomes more - well – intelligent, we can see this opinion shifting.

Now, AI is proving it can create music, imagery, even entire business plans. They aren’t hugely advanced yet, but the technology is developing by the day. In addition, for the first time, machines are beginning to make decisions crucial to our wellbeing, such as screening job applications, approving a loan, or even flagging symptoms of cancer.

But with well-documented AI biases regarding race, age, gender, and more baked into many of these technologies, as a result of the AI learning from insufficient datasets, can it be fully trusted in its existing state? To manage the risks associated with handing over such power to machines, we’re seeing new regulations for AI, to which businesses must quickly and diligently adjust. For instance, in the US, the White House Office of Science and Technology Policy is already exploring a bill of rights containing principles to guard against the dangers of AI.

Globally, UNESCO has created recommendations on the ethics of artificial intelligence. Business leaders need to be ready for these incoming regulations by implementing clear, ethical AI practices today. We must use ethical instructions and diverse data to train our AI, as well as setting up in-house committees that test for biases and monitor against standards and principles. Only then can AI be trusted to make recommendations. Since machines can’t explain their decision making, it’s vital that humans assess these recommendations before factoring them into the final decision.

Experimentation is leading to crucial discoveries

Over the past couple of years, businesses have faced economic, technological and environmental turbulence. And, whilst businesses in every industry have unique hurdles to overcome, a common challenge is that we simply cannot predict what’s coming, or what our customers will want, or need, as a result. That’s why we’re seeing more businesses switching their focus from long-term planning to short-term experimentation.

Take Google, for instance. Since its early days, the tech giant has encouraged staff to spend 20% of their weekly working hours exploring ideas or trialing with projects that might not pay off immediately but could benefit Google in the long run. "This empowers them to be more creative and innovative. Many of our significant advances have happened in this manner,” wrote co-founders Larry Page and Sergey Brin back in 2004. Examples of these advances include Gmail, the world’s most popular email platform with 1.8bn users, and Google’s AdSense, which generated $147bn in revenue during 2020 alone.

The future of AI in business starts with a culture of innovation

The experimental approach allows businesses to take a step back, be creative, and come up with exciting solutions. Many won’t come to fruition, but it’s an exercise that is set to pay off in time. Just as Thomas Edison is famously quoted as saying: “I have not failed. I've just found 10,000 ways that won't work.” Perhaps, that one crazy idea will turn out to be the one we’ve been waiting for.

Experimentation will impact our products and services, too, moving forward. Instead of building a new tool or piece of software based on in-house expertise, we’ll factor in customers from the very beginning. Their feedback must inform even the initial prototypes, meaning businesses will trial ideas or solutions for needs we didn’t expect our customers to have. Remember, traditional markets aren’t used to working with the latest tech, so we need their feedback to create services that are friendly, easy to use, and help build trust with brands.

So, while we can’t predict what the world will throw at us in the future, we can be ready and prepared for it.

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