In our professional practice, we have encountered two polarized opinions about AI and its impact on job roles and business models. One side is concerned about unemployment rates spiking and artificial intelligence taking over, while the other believes that AI won’t bring any significant changes and will end up being a bubble.
As 64% of CIOs place high hopes on using AI to elevate their business operations and evolve enterprises, understanding the strong capabilities and limitations of the technology becomes particularly important. Can artificial intelligence truly introduce brand-new business models, or are these expectations rooted in bias?
As always, the true answer lies somewhere in between.
Every technological revolution has been followed by the transformation of job roles and workplace routines. The evolution of AI promised to rapidly change workplaces and drive societal changes. As it turned out, AI didn’t impact society as expected, but society can and should impact AI.
The slowdown in LLM development and the continuous reports of AI hallucinations make it clear that the AI systems we know today are not just far from perfect — they don’t deliver what was expected, and the developers know it. It’s important to understand that the problem is not with artificial intelligence but the hype around it. Instead of slowing down and focusing on improving existing features, developers started aiming for the next goal. As a result, many potential problems remained underexplored and overlooked, causing numerous issues, such as Google experiencing a $100 billion share drop because its Bard AI made a factual error that nobody checked.
These results show that if AI needs control and monitoring to perform basic tasks, it’s too early to trust it with complicated tasks. Many job roles require deep insight, critical thinking, and flexibility that artificial intelligence lacks — and this won’t change any time soon.
As the former head of the AGI readiness group at OpenAI said, the real efficiency of AI is going to be the result of a robust dialogue between businesses, governments, industry voices, professionals, and citizens. Currently, this conversation has yet to get started, and it will require full participation from everyone concerned.
AI in business models: exploring the current value
While the era of AI-driven business models isn’t something we should expect in a year or two, there is no denying that artificial intelligence has significantly impacted the way companies operate and manage their workflows.
In general, it all boils down to three supporting pillars of any enterprise:
1. Data analytics
The more connected we are, the more data comes our way. This is particularly true for enterprises — each year of the business journey generates multitudes of data pools, documents, papers, and screencaps. Each of these bits offers immense value, but it has to be found first. For human experts, mining for and organizing all that data would take months, if not years. However, for artificial intelligence, it’s a matter of days, if not seconds. By diving deep into large volumes of data, sorting them out, and organizing them — including unstructured data — AI connects vital information with employees, decision-makers, and executives, erasing data bottlenecks and enabling sharper decision-making at every level. With AI, the history and entire view of the enterprise journey become much clearer, adding more certainty and helping business leaders realize what milestones they’re at and where they need to be in the future.
2. Customer interactions personalization
With customer experience quality in the US hitting an all-time low, reducing response time, enabling personalized interactions, and addressing client concerns as rapidly as possible have never been more important for enterprises. However, meeting these goals means taking in every single piece of customer data: demographics, purchase history, brand interaction frequency, and many other factors. A task of that scale is too much for a call center or support team to handle, but it is a routine activity for an AI assistant. By working in tandem, AI-powered platforms, and human employees can deliver superior customer service by instantly researching individual client histories and addressing their specific needs. Such an approach provides the levels of personalization and empathy customers look for in a brand, strengthening their relationship with the vendor and nurturing loyalty.
3. Risk management
Risk management is a constant and unchanging pain point for enterprises — and it will always stay that way. The more intense the business landscape, the more scenarios executives need to evaluate to properly assess financial and reputational risks. Some evaluations are based on critical thinking and experience, while others require tremendous amounts of historical data to reveal patterns. In the latter case, artificial intelligence offers immense help by handling anomaly detection, identifying patterns, and detecting suspicious behavior. These capabilities relieve pressure from managers, analysts, and executives, allowing them to identify threats before they emerge — and prepare accordingly.
The future of AI business models: stay tuned for more
One of the most important points to take into account is that the types of AI-powered business models will remain undefined until the full value of artificial intelligence is discovered. With business leaders still on the fence about calculating AI ROI, there is a need for exploration and research.
The adoption of artificial intelligence is no small change; it introduces a completely new workflow. Therefore, business leaders need to gain a good understanding of that workflow, identify its KPIs, and determine what makes it different from previous routines — and deduce transformational value based on their analysis.
For instance, in many cases, AI doesn’t just improve enterprise processes — it creates new ones that allow reaching desired outcomes. But to maximize the value of these outcomes and lay the foundation for brand-new models, any enterprise would need three integral components: the process, the technology, and the people using it.
