Keith is a thought leader in Digital Journal’s Insight Forum (become a member).
Can anyone remember the world before AI tools? Since the fateful launch of ChatGPT in November of 2022, the world has forever changed. As someone in technology, the AI conversation has been phenomenal — it’s raised my popularity at BBQs (even more so than the barrage of cyber-security and tech support questions) and has created another moment that has created a burst forward for the conversation of transformative digital projects as a means to drive profitability, competitiveness and go-to-market tactics.
However, with all of the optimism and excitement, there is a nuanced conversation that is also taking place simultaneously around expectations, risk, and bubbles. I’ll try to break down my perspective on each of those three through the lens of how that will impact business leaders looking to find the right balance of innovation and healthy skepticism.
Expectations
First, I want to clarify that I am by no means the domain expert on AI, but rather have the privilege to have had conversations with many people who are. In these conversations, there are two connecting factors that all of these experts seem to have in common. The first is an admission of how much is still unknown, and although we have made great progress, there is still a metric ton to figure out and the second is that there is still a big lift before AI truly makes the splash that some executives hope it will.
One of the things that will continue to hold companies back is still the foundation — while many organizations are very eager to implement digital strategies that have massive output potential, they get less excited about the tedious work in the back-end that must be done in preparation (at least with today’s technology) to make many of the models generate correct and useful outputs. Without doing this work and willing to wade through the data cleanup, data hierarchy and workflows, you will be woefully disappointed with anything outside of the general models already available. Aligning longer-term goals rather than bursts of value will help to align and justify the investments needed to produce AI models that produce reliable insights or predictions.
Risk
There is a balance of risk that most leaders consider with all decisions. There is a balance that is being weighed between what if we do vs. what if we don’t. To start with the latter, there has never been as much of a technical FOMO as with AI — boards are pushing leaders, leaders are pushing their teams and so on. There is a real perceived risk of not moving fast with AI for many companies and leaders much more likely to survive a failed AI experiment than no experiment at all.
On the flip side, the risk of implementing tools that circumvent traditional adoption curves leads to a host of problems. The speed at which OpenAI reached 100 million users was within two months of its public release (as a comparison, Facebook took eight years). Everything from not understanding security or privacy, data sovereignty, or data permissions has led to some embarrassing stories ranging from Samsung uploading confidential IP and losing domain or even dating further back with Clearview’s misuse of a facial recognition tool leading to fears of mass surveillance. Even more than ever, employing and having a position around ethical AI policy, implementing strong data management policy and framework along with a defence-in-depth strategy are ‘must dos’ when considering AI integration.
Bubbles
The fear leaders have in investing in technology or companies that won’t be around in a few years, especially in such formative times, is high. Similar to the .com bubble of the 2000s, AI is currently accounting for 50% of venture funding, and the funding is provided at significantly higher multiples than other tech peers. Another issue with the bubble is companies ‘AI Washing’ their offerings and simply integrating a GPT to an otherwise static platform to increase market presence or valuation.
To put it simply, as much as there are many players in this market, the control by the big players is increasing the barriers to entry to a point where market consolidation will most likely start to happen. My advice to leaders is to first truly understand the business problem and use case before venturing into some of these partnerships, and a high degree of diligence to evaluate the depth of AI integration and roadmap will be critical for long-term success.
The possibilities of what technology teams can deliver back to their organizations by using the growing suite of powerful AI platforms is incredibly energizing, but like many decisions, having a strong balance between innovation and cautious optimism can help to navigate what is still a very early part of the AI story.
