AI has rapidly reshaped nearly every industry, promising greater efficiency and a sharper competitive edge. But somewhere along the way, “AI” became a catch-all term used for everything from simple automation to complex generative models. The result? A glossy façade of innovation with little substance underneath.
This point is made by Michael I. Jordan, a professor in the department of electrical engineering and computer science, and department of statistics, at the University of California: “People are getting confused about the meaning of AI in discussions of technology trends – that there is some kind of intelligent thought in computers that is responsible for the progress and which is competing with humans…We don’t have that, but people are talking as if we do.”
Too many organizations are rushing to adopt AI without truly understanding what it is or how it works. That disconnect is not just superficial it’s a serious risk.
This is something that businesses and the technology sector need to reflect on now that AI Appreciation Day is here.
Artificial Intelligence Appreciation Day is celebrated on July 16 each year. With discoveries in science, tech, and healthcare, A.I. offers the possibility of a more evolved future. A.I. tools are dominating parts of the market, sometimes adding value and making human life easier.
To take stock of what AI actually means for business, Digital Journal canvassed the views of two industry experts.
Not every solution is right for your needs
The first is Mayank Kumar, Founding AI Engineer, DeepTempo. Kumar heralds the potential of AI but also notes its weaknesses: “AI is the most consequential innovation in modern technology and cybersecurity. When applied thoughtfully, it can dramatically improve how organizations detect threats, adapt to changing environments and scale decision making. Outcomes are not guaranteed as not all AI is created equal and not every solution is right for every environment. Blind adoption without foundational understanding is putting you at significant risk, especially when teams lack the expertise to evaluate the underlying technology itself.”
To illustrate such concerns, Kumar cites: “Take the difference between traditional machine learning and deep learning. Legacy models often require weeks of labelled data and retraining just to perform in a new context. Deep learning-based approaches, by contrast, can leverage pre-trained models built on large-scale, unlabelled, real-world network telemetry like NetFlow and VPC flow data to deliver immediate insights into traffic patterns and behaviours. The bottom line: AI can be transformative, but the benefits come when you dig deeper, ask the hard questions, and align the technology with your organization’s unique needs.”
Glossy on the outside, but often empty in the middle
The second person looking at the frailties of AI is Evan Powell, CEO, DeepTempo, who states: “AI Appreciation Day is often filled with praise. Don’t get me wrong, some of it is deserved, but this feels like the right moment to double click a bit.”
Powell uses cybersecurity to make his point: “Take security, for example, if you simply chase the shiny thing – it might be easiest to bolt some AI onto your old security workflows. Detections bad? False positives high? No problem, we pick from one of a few dozen AI SOC solutions and – POW – false positives be gone, right? Yes, however – not so much the false negatives. The same detections that flood your SOC with false positives ALSO are unable to see AI powered attacks. So here we have it. Attackers are using AI to avoid our pre AI detections and our answer is to put AI lipstick on our detections and call it good, hiding ever increasing vulnerabilities.. What about your use case, do you also see AI being used to paper over deeper challenges? And do you see competitors – in this case cyber attackers – using AI in ways that put all you do at risk?”
So, as we actually dealing with ‘artificial intelligence’? Here Powell ponders: “Somewhere along the way, “AI” turned into a vague label used for everything from machine learning to foundation models to generative tools. It’s become a donut of innovation, glossy on the outside, but often empty in the middle. Many are adopting these technologies without knowing what they are or how they work. That’s a risky place to be.”
Powell concludes with: “So by all means, celebrate AI! But take the time to understand it deeply. When you go beyond the buzz and engage with how it really works, you unlock smarter decisions, more powerful results, and a far more promising future for AI in your organization.”
