What Most Enterprises Get Wrong About AI

When many people think of AI, they think of off-the-shelf large language models (LLMs) like ChatGPT that write marketing copy, engage customers, or generate images. But for enterprises, AI is likely to look very different, says Invisible Technologies CEO Benjamin Plummer.

“Individual use cases inherently have a ‘human-in-the-loop’ overseeing and guiding the LLM very closely,” he notes. “However, to make AI work at enterprise scale, you need a completely different architecture and approach.”

So, how does your business take AI from a side project to center stage? Here are four things all enterprise leaders need to know about adopting AI.

1. You Need to Act Fast

There is a lot of hype around large language models (LLMs) right now, Benjamin Plummer notes, and it’s easy for enterprise leaders to think that they should wait for this to die down before choosing which technology to back. However, he cautions those who do are likely to miss the boat.

“This is a completely different way of thinking that you can’t expect to develop overnight,” Plummer explains. “You really have to build AI muscles within your organization today so you’re not caught out.”

Plummer observes that AI is likely to reach well into every organization within the next few years, fundamentally reshaping both the way they operate and the markets they operate in. In the process, it should significantly reduce the barriers to entry into many fields, creating real opportunities for ‘AI-first’ businesses, many of whom will be newcomers to a field.

This is supported by PWC Global research, which found that healthcare, automotive, and financial services were the three sectors most likely to face severe disruption.

Meanwhile, Invisible Technologies CFO, Joseph Chittenden-Veal, says that to survive in this post-AI world, enterprises in these sectors and others need to quickly work out how they’re sufficiently differentiated. Otherwise, they are likely to find themselves at the mercy of lower-cost competitors who harness AI more effectively.

“In this environment, those businesses that thrive will be the ones who work out how they add value and focus only on these core competencies,” he explains. “They can then use AI to carry out the rest of what they do.”

2. AI Is Not a Silver Bullet

That said, Benjamin Plummer argues that executives should refrain from seeing AI as the savior to all their business problems.

“Even those people who have started using generative AI for more complex tasks know that you can’t just input text at one end and expect magic to happen,” he explains. “Process still matters.”

Plummer says enterprises should see AI as part of a broader ecosystem that still involves humans. However, he cautions that enterprises are most likely to need several AI models performing different tasks within their business.

“Like humans, these models will have different strengths and weaknesses,” he says. “It will be important to use the right one for the right job.”

Plummer also says that thinking through the orchestration of these AI models and the way they interact with employees, customers, and clients will be just as important as the models themselves.

“The challenge is combining these models, which are great at particular types of tasks, with humans who are great at other types of tasks.”

“Enterprises will have to map out every challenge and every process to determine who can perform it best - human or machine. And, this is likely to be an ongoing process."

3. AI is More than Just ChatBots

The individual takeup rate of Generative AI has been nothing short of extraordinary. By September 2022, 49% of people in the United States, India, Australia, and the United Kingdom reported using generative AI.

However, Joseph Chittenden-Veal believes the popularity of large language models (LLMs) such as OpenAI’s ChatGPT, Google Bard, and Meta’s Llama 2, may have already conditioned people to see AI only as a ‘text in/text out’ technology.

“While text in/text out processes may be the ‘low hanging fruit’, really AI as a technology can be built around anything in and anything out,” he explains.

“For instance, in the context of manufacturing, the input could be a slight surge in demand. The output could be a series of actions behind the scenes that involve a multitude of applications and datasets that ultimately produce more goods.”

“When you understand AI this way, it can really be applied to anything,” he says. “Industries such as retail, healthcare, financial services, logistics, agriculture, and manufacturing could be revolutionized.”

“As we roll out more and more generalized AI and build more AI applications and use cases in different verticals, the realm of things that can be automated and enhanced by technology is going to radically increase.”

4. AI Can Be Implemented in Small Steps

Despite AI’s transformative power, Joseph Chittenden-Veal says that another common misconception among enterprises is that they need to go ‘all in’ on AI right from the start, making it a costly and time-consuming process.

The truth is that, unlike most other technologies, enterprises can start incrementally - implementing it in a contained way before expanding.

“The great thing with AI is that adopting it doesn’t require an overhaul of your systems,” Chittenden-Veal explains. “Consider diagnostic imaging. Initially, AI helped enhance the resolution of images as a simple step in the pre-processing. Soon after, it began being used as a backup to catch missed anomalies after radiologists had their turn, adding another step after the existing workflow. As its precision improved, it has started leading on early diagnoses and even treatment plans, becoming a larger piece of the process. This expansion of impact is characteristic of great AI applications.”

“It will be the same for enterprises,” he says. “Implementing AI will often be a matter of building on what’s already there. You can pick and choose where you want to implement it first.”

“With the right partner, you can have very light touch integration points and get value in just weeks. This is especially true if you already have an existing bank of good data.”

In fact, a 2021 Harvard Business Review article reported that companies in specialist fields such as manufacturing, healthcare, and agriculture would be better served by implementing more streamlined solutions underpinned by small datasets focused on discrete tasks. Many of these projects cost in the range of just $1 million to $5 million.

The flip side, though is that, unlike software products, AI requires a high level of oversight and maintenance.

“Enterprises will need to make sure they have the right machine learning platform and resources in place to keep watching and refining what the AI is doing, whether that’s in-house or outsourced,” Chittenden-Veal says.

“My advice is to go fast and think big - don’t constrain your thinking around AI.”

“But you can start in bite-sized chunks.”

What Every Enterprise Should be Doing Right Now

In the face of sweeping changes across industries, all businesses should assess their current processes and make sure they’re AI-ready. Don't wait for disruption; prepare for it proactively.

If you need guidance on how to align your business with AI, get in touch.