Dashing across AI’s highway to the danger zone

Reflecting on the growing number of industry conversations around artificial intelligence (AI), particularly in the software and service sector, I believe we are witnessing a significant shift. The accessibility of AI has made it easier than ever for companies to integrate AI capabilities into their software.

But I wouldn’t rely on ChatGPT to help me cross a busy road in 2023. Would you?

With a cloud-first and API-first approach becoming the norm, numerous organisations have embraced AI to enhance their offerings. The benefits are clear, as AI can save a tremendous amount of time and perform tasks far more efficiently than humans. 

Personally, I find myself utilising AI on a daily basis for various purposes, from generating speech texts to creating visuals for presentations. There is no doubt that AI is here to stay and can be a positive force for good, provided it’s handled with care.

The untapped potential of AI-enhanced SaaS services would be impossible to ignore as the financials involved are mind-bending sums of money when it comes to investment and revenue. The global SaaS market is valued at $197 billion this year and is estimated to reach $232 billion by 2024. Meanwhile, artificial intelligence as a sector already commands a global value of around $207 billion in 2023, and this is expected to race to almost $300 billion next year. 

And by the end of this decade, the global valuation of the AI sector is expected to be closing in on an incredible two trillion dollars. To give some context, that 2030 valuation of the AI industry compares with today’s market capitalisation of the entire global information services sector. Who knows what unexpected developments may occur in the next seven years that could make even those eye-watering figures look naive when we’re looking at them in the rear-view mirror?

False promises

However, there is a growing concern that comes hand in hand with the rise of generative AI, which is the issue of confabulation or hallucinations. These events involve the generation of content that is remarkably convincing, but completely false. As a result, we are faced with the challenge of redefining the concept of quality within the AI landscape. While AI undoubtedly brings immense value, we must remain cautious when relying on it for critical decision-making processes.

Like many others, I have come to realise that using AI for tasks like search or gathering factual information requires careful consideration due to the potential risks associated with confabulation and the rise of fake news.

And the risk is even greater as the impact of AI extends beyond just the software industry. It has the potential to revolutionise education, how we access information, and the way we work. As more individuals begin to appreciate the benefits of data-driven decision-making, there is an increasing need to address the issues of confabulation and hallucination in this process. 

This is where AI in analytics becomes crucial. While generative AI or large language models alone may not render data-driven decision-making obsolete, they do require additional layers of quality and explanation. Analytics plays a vital role in AI-generated decisions, providing metrics and context to substantiate their plausibility. By bringing AI-generated decisions out of the black box and incorporating traditional analytics, we can instill confidence and add a level of quality assurance to the decision-making process.

Data dashboards as guardrails

To address the risks associated with AI-generated content, dashboards and analytics serve as vital guardrails. Analytics offers valuable insights into the decision-making process of AI algorithms, enabling users to understand the rationale behind recommendations and the supporting metrics. By integrating analytics into AI-driven workflows, businesses can enhance the quality and reliability of generated content. This ensures decision-makers have a comprehensive understanding of the recommendations, enabling them to make informed choices based on accurate insights.

Imagine standing at the edge of a busy road, contemplating whether to cross. In the past, when you saw the traffic, you had absolute certainty that what you perceived was real. But now, imagine discovering that you occasionally hallucinate, seeing cars that aren’t actually there or missing ones that are. Would you still have the same confidence to step onto that road? I don’t think so.

This analogy perfectly illustrates the importance of analytics in the realm of AI. Analytics act as our guide, our way of ensuring that we have the most accurate view of the road ahead. Just as analytics can inform us when a car passes at a given moment, it boosts our confidence to trust analytics combined with generative AI to automate decisions. If we genuinely desire to automate processes, we must consider the chances of being run over by a car if we don’t incorporate analytics into our decision-making. It becomes a matter of establishing a similar level of trust and ensuring our safety.

The pace of change is relentless, much like the cars on the road. We can either stand by the side, overwhelmed by the speed at which things are moving, or we can choose to engage and keep up. However, we must acknowledge that there are times when we might be hallucinating, mistaking false realities for the truth. This is where analytics becomes our ally, helping us discern between what’s real and what’s not. It provides us with the most accurate view of the traffic, ensuring we don’t get blindsided by a truck we didn’t even know was there.

The dangers of speed

The acceleration of business is undeniable, and AI, including large language models, contributes to this increased speed. As it speeds up our work, it simultaneously accelerates the work of everyone else. To keep up with this rapid pace, automation becomes essential. However, we must be cautious not to automate blindly, ignoring the potential for hallucinations and false information. By combining automation with analytics, we mitigate risks and navigate the increased speed with greater confidence. 

Businesses must incorporate analytics as a safeguard against such risks. Analytics empowers decision-makers to question and verify AI-generated outputs, facilitating sound decision-making based on reliable insights.

The integration of AI into the SaaS industry presents both opportunities and challenges. While AI offers remarkable efficiency gains, it demands cautious implementation. By embracing data dashboards and analytics as essential components of AI-driven workflows, businesses can navigate the challenges posed by confabulated content and misleading outcomes. 

Ultimately, the synergy between human expertise, AI capabilities, and analytics leads to informed decision-making and propels businesses towards sustainable success in the era of AI.

But we must all remember to look both ways while we’re crossing the particularly busy road ahead.

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Karel Callens, CEO and Founder of Luzmo

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