From investment to implementation: how to get your company past its AI hesitancy

You might feel that AI could be a double-edged sword for your business. Why? Well, you're excited about what it can do. But also worried about how much it could cost, or the risks to security, such as data breaches, associated with machine learning. 

We investigated how businesses feel about AI in our recent whitepaper, and found evidence that suggests many are apprehensive.  There are often valid reasons for hesitating; such as concerns around privacy, lack of an actionable plan or not having found the right AI vendor. But there’s plenty of interesting ways to address these concerns, starting with better training or more creative ways to trial machine learning projects.

Is AI holding you back? Let's find out.

Right reasons, wrong way: why AI is bucking the trend

Lots of businesses are moving forward with AI quickly because they have FOMO; this is understandable given the vast changes to business landscapes that may occur as a result of AI, and the need to get on top of it. But frequently the same businesses do not have an actionable plan. If you're investing in AI, you need to do so for the right reasons. 

AI and ML's potential doesn't mean you should be rushing in without consideration, however. The economic landscape has been turbulent in recent years, and concurrent with that turbulence has been a rush to adopt AI, sometimes unthinkingly. 

• 97% of organisations in the retail, distribution and transport sector reported adoption of AI, but only 70% strongly agree their company has a vision for AI.
• This drops to 91% in financial services, where only 58% strongly agree.

It is preferable for businesses to have an awareness of expected ROI for new technology. This might involve going back to basics and planning with the capabilities of the technology and business strategy in mind or it might involve the collection of experience and data through research and small scale proofs of concept. 

If you don't want to be left in the dirt, investing in AI makes sense, but if you don't have a plan to use it, you might be left with a big investment with brand new tech, and no concrete way of moving forward. 

How to move past hesitancy in adopting AI

As with any new tech, trepidation around adopting it isn't just for one single reason. The concerns around AI vary from issues of privacy and ethics, to security, the difficulty of finding a trusted vendor, legacy tech, and even skill levels within the business. 

This means that while 98% of businesses have said they do have a vision for their future utility of AI, only 48% of businesses could see that vision becoming a reality.

“Our data reveals a growing disconnect between interest in AI and developing a strategic roadmap. ... Without a refined strategy, businesses cannot expect to unlock the full suite of benefits delivered by AI.” - Ian Ray, Head of Data, AI, ML

If the reasons behind the hesitancy are multi-pronged, a multi-pronged approach must be used in response. Here’s how:

1. Business benefits

One way to get the stakeholders in your business to be interested in AI is to give them a reason to be. If enthusiasm for new tech typically comes from having strong ROI awareness and a proof of concept, then you don't have to settle for anything less with AI. Show your stakeholders specific use cases and ways that AI can benefit the business.

2. Training

AI can be intimidating. It's a tool that can help employees improve efficiency, but 'machines taking all our jobs' is an idea that's in the zeitgeist, for good reason. Your workers might have apprehensions; if you give them training to understand what AI really is, and how to use it, they may want to give it a go. 

Training, done right, can help employees to use AI effectively, and aligned with operational needs. Appropriate use policies can produce certainty and trust while embedding expert knowledge  about the realities of the technology into business processes.

Last year a lawyer famously read an article on how ChatGPT “could make legal research obsolete.” He went on to use it in a lawsuit where ChatGPT created fake court cases and data, as he didn’t understand that ChatGPT was not a search engine.

• 24% of decision makers do not think employees have the skill level to comprehensively use AI in their business. Training and documented policies can show how.

3. Proof of concept

There are many good reasons to do a proof of concept for any new project. A small-scale or low risk trial of AI, in an environment you can control, allows you to test and future-proof the use of any new technology. 

Doing a trial run also enables you to verify any questions you have, and allows you to build capacity without breaking the bank. POCs allow targeted testing, even if your budget only allows for cerebral planning - this still involves key decision-makers. 

At Daemon, we really enjoy watching clients turn their great ideas into something more concrete, and a proof of concept is a fantastic way to test this.