5 Ways to Avoid Becoming an AI-Induced Corporate Casualty

AI has the potential to transform modern enterprises more profoundly than virtually any technology that has come before it. In the process, it will likely inspire a new generation of challenger businesses that reshape the industries in which they operate. But it will also create a new generation of losers.

With that in mind, here are five things all enterprises should do to make sure their company doesn’t join the list of AI casualties.

1. Change The Way They See Technology

AI and large language models (LLMs) are fundamentally different from existing technology solutions, says Scott Downes, Chief Technology Officer at Invisible Technologies.

“To date, technology has always been deterministic. Ask it what two plus two equals, and it gives you four,” Downes explains. “That’s not how AI works, nor what it’s intended for.”

Instead of relying on predetermined rules or fixed algorithms, Downes notes that AI learns and adapts from vast datasets. This means it can comprehend context, nuance, and even human emotions, allowing it to provide responses that are contextually relevant.

“AI isn’t just a one-time solution but an ongoing journey. It’s about constantly refining and improving the technology, understanding its limitations, and adapting to its evolving capabilities.”

“This isn’t just a technological upgrade; it’s a shift in mindset,” Downes explains. “To be successful, you have to approach it in a fundamentally different way."

2. Start With the Challenge, Not the Solution

Ben Plummer, Chief Executive Officer of Invisible Technologies, says many tech providers still build a solution and then look for a problem to fit it to. This has conditioned enterprise leaders to see technology in terms of its functionalities - an approach that can lead to less-than-ideal results.

"The level of hype around new tech can often be high, but the level of real-world activity can be low,” he cautions.

However, with the rise of AI, Plummer says businesses have the opportunity to change the way they apply tech to their businesses.

“Because you’re not buying software out of the box, you can start with the problem and work backwards,” he says.

“Instead of trying to keep up with every last development in the world of technology, enterprise leaders should prioritize identifying their specific challenges and needs. Then, they can work out how to address those challenges.”

This approach, he says, will encourage a problem-solving mindset, where AI solutions are customized to fit the organization's unique requirements rather than attempting to fit existing technology solutions into the organization's processes.

3. Keep Humans in the Loop

A recent Goldman Sachs report found AI could replace as many as 300 million jobs and perform up to 25% of all work-based tasks carried out in the United States and Europe. For some occupations, such as administrative (46%) and legal (44%), the number of processes it could potentially replace was higher still.

However, Plummer says that rather than being a threat to employment, AI will enable and empower people, removing the mundane and monotonous and lifting people up to do more creative and high-impact work.

“AI is better at some things, but humans will always be better at others. This will be the catalyst for many people to be able to do more interesting and fulfilling work.”

Plummer also says that humans will still be needed to oversee AI, both from a practical and ethical point of view.

“The right solution always ends up being some blend of human judgment and intent with an AI system augmenting them.”

By blending AI's strengths with human ingenuity, Plummer argues businesses can stay ahead in the tech race.

4. Avoid Getting Tied Into One Set Path 

AI is evolving so rapidly and developing so many new capabilities that Downes believes it is a mistake for enterprises to tie themselves to just one solution or platform.

“Those enterprises that chose to go it alone or who put all their eggs in one basket will be taking a huge risk,” he says.

“The right approach is to be both flexible and ‘solution agnostic’. What’s important is that there is a layer of orchestration to seamlessly integrate humans and tech.”

Downes says that properly integrating AI means analyzing and documenting every single action currently performed across an entire process, department or organization. Only then is it possible to determine whether each step is best performed by a human or AI.

Beyond this, enterprises need to either build or engage a dedicated and experienced machine learning team that can process data and train their AI model. They also need to constantly monitor and refine AI’s outputs to make sure they continue to match its needs.

“Rather than spending the millions of dollars required to build these capabilities in-house, most enterprises are far better off outsourcing to someone who specializes in the space,” Downes says.

“By outsourcing, you can also get someone who understands and is experienced in the shortcomings of AI, such as data and privacy issues and model hallucinations, and knows how to train your model to potentially overcome them.”

5. Think Bigger

Finally, Plummer observes that most organizations have “blinkers on” when it comes to assessing the extent of what’s possible with AI. That’s because, even when contemplating the future of tech, they still tend to think in terms of what has already been achieved rather than what’s possible.

Plummer encourages organizations to challenge their preconceived notions and think beyond the boundaries of what has traditionally been automated. By doing so, they can uncover new ways to leverage AI technology to enhance productivity, efficiency, and innovation across a broader spectrum of their operations.

“Those that do will find they have a huge competitive advantage and their tech will be propelling them forward rather than holding them back.”