Editorial
The Gist
- Knowledge mismatch. Majority overestimate their AI understanding, risking strategic errors.
- Misaligned investments. Over 85% risk budgets on unfit AI technologies, needing precision.
- Education as a solution. More AI education can bridge knowledge gaps, enhancing strategic fits.
We are ostensibly at the peak of AI hype, driven heavily by the rapid adoption of generative AI in both the consumer and business space. But hype does not necessarily equate to understanding, as a study of more than 500 business decision-makers across enterprises worldwide, conducted by research firm Savanta for Pega shows.
The majority (82%) of those surveyed expect that AI adoption and usage will be responsible for up to half of their increased profits over the next three years. Building on this, an even greater majority (93%) claim to have a good understanding of what AI is and how it works, yet only 35% could provide an accurate definition of generative AI. The risk of this knowledge gap between the reality of AI and potential AI applications and benefits only stands to hurt companies that stand to gain by its strategic adoption.
Let’s explore a few areas where the delta between AI knowledge and its potential application can affect the enterprise. We'll examine some AI challenges caused by this knowledge gap and some missed opportunities.
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AI Challenges: 1. Overconfidence in Understanding Solutions
One thing that this gap between assumed knowledge and actual understanding can bring is overconfidence in potential results and in a “silver bullet” fix to longstanding issues. We’ve been down this path before with other technologies (remember the metaverse, anyone?), so it is important for those responsible for long-term strategies to understand that there are potential AI applications, and it is important to understand the different types of artificial intelligence that can be leveraged in the enterprise, such as predictive analytics, automation and robotic process automation (RPA), and yes, generative AI.
This AI challenge overconfidence may lead to missed expectations, per the profit expectations from the study. I’ve said many times already that AI isn’t a goal, nor is it a strategy. Understanding where AI plays a role and where it may be the wrong fit is critical. With 85% of respondents to the study saying they spend up to half of their annual IT budget on AI solutions, enterprises can’t afford the time (or the money) to make wrong decisions and invest in wrong fits.
Related Article: Can AI Transform Your Digital Content Strategy?
AI Challenges: 2. Assumption of Best Solutions
Another of the AI challenges that the AI knowledge gap poses is the incorrect scoping of a solution to an AI challenge that marketers face in the months to come. For instance, with the lack of ability to properly define generative AI, how can the best directional guidance be provided on what the best solution to an AI challenge may be.
It would appear that many in the enterprise know this on some level, but perhaps the AI hype is just too strong. Over three-quarters (77%) of respondents admit to at least some level of waste in their budgets (of which nearly a half is spent on AI) due to a lack of a proper strategy.
The writing is on the wall here. The knowledge gap translates to a strategy gap, and greater education about AI is needed so that the right fit solutions can be matched to the right AI challenges or AI opportunities.
AI Challenges: 3. Missed Opportunities
Yet another AI challenge that this knowledge gap brings is being distracted by the hype and overlooking tangible ways that AI can improve the way that work is done by marketing teams in the enterprise.
For instance, generative AI is often thought of as purely a way to create text- and image-based content (and increasingly video), but one of the overlooked areas where it can help marketers and many others within the enterprise is in the operations of work being done. Marketing operations can utilize generative AI to streamline steps within the process of creating content and campaigns in a way that goes beyond sending a single prompt to get help writing a blog post.
Additionally, mixing the different types of AI together can yield transformative results. For instance, using predictive analytics with generative AI means that propensity models can be married with generative AI to create content and experiences targeting your best and most valuable customers with content that is directly relevant to them.
Mixing different types of AI solutions also requires a more nuanced understanding of artificial intelligence so that these applications can be utilized to truly find the best tool for the task at hand. Plainly put, executives and leaders that assume plugging in AI to solve a singular problem are missing opportunities to make larger and more meaningful improvements.
So what is the solution? There are many potential solutions to this knowledge gap, but the obvious one is greater education about not only where potential hype may be setting unrealistic expectations, but also where true potential for gains may be obscured by shiny objects that make good headlines but don’t translate into real results.
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AI is definitely not all hype. But it is also easy to be distracted by the hype and lose sight of some real AI opportunities that businesses can leverage to transform the way they work. With greater education about the breadth of ways that AI can help marketers in the enterprise, we can look beyond some shiny objects to realize more tangible results.
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