AI: From Hype to ROI – Real Business Applications in 2026

The initial wave of fascination with Artificial Intelligence (AI) has passed. Following a period of experimentation, companies are now selectively adopting only what proves to be profitable.

The Bubble That Didn’t Burst

Entrepreneurs and investors have already lived through major tech bubbles, such as the dot-com explosion of 1995–2001. Back then, simply announcing that a company was involved in the World Wide Web was enough to send its stock prices soaring to astronomical heights. Later, when these claims proved to be little more than marketing gimmicks, many such enterprises went bankrupt.

A similar trend is emerging now, as nearly every major brand announces AI investments. Employees are the most concerned, fearing their roles will be replaced by algorithms. This fear is not baseless, as AI indeed enables mass automation. However, this anxiety has accompanied us through every major shift—from the Industrial Revolution when machines entered factories, to the era of robotics when entire production lines began operating without human intervention. It is here once again. Yet, in reality, workers are rarely "thrown overboard"; instead, they are redeployed to more complex, sophisticated tasks.

The previous years were a time of experimentation and mass-scale AI implementation. Currently, nearly 88% of companies are already utilizing AI in their business processes. Interacting with AI has become a part of daily life. While a year or two ago these were merely trials—some more successful than others—today, automation is a daily reality in customer service and CRM/ERP systems.

Companies have had to answer a fundamental question: where is the application of new solutions most profitable, and where will it remain a risky experiment for years to come? It turns out that the highest return on investment (ROI) occurs in the IT and software engineering sectors. Using coding assistants can shorten the delivery of large development projects by as much as two years.

The results are similar in customer service and sales personalization. Voicebots and chatbots reduce service time by up to 60%, resolving basic issues during the first contact in as many as 95% of cases. In sales, they improve conversion rates for new customer acquisition by 25–40%. There is clearly much to be gained.

 

Who is Artificial Intelligence For?

Common sense suggests that large corporations stand to benefit most from AI. We are already hearing about mass layoffs in various parts of the world and the replacement of entire teams with generative AI solutions. Implementing machine learning algorithms in maintenance systems allows for a reduction in unplanned downtime by up to 30%. For the industrial sector, these savings are measured in hundreds of millions of euros.

In finance and accounting, using AI to analyze and automate routine, repetitive tasks triggers productivity growth of up to 40%. Even more impressive is the cost reduction index, which in many cases exceeds 70%.

Large companies are taking the AI era deadly seriously, using this new reality for a radical transformation of their entire business model. Corporate strategists are willing to fundamentally revolutionize company processes. They allocate up to 20% of their total IT budget to this transition, achieving an increase in operating profit of at least 5%—which, given the scale of their operations, generates massive figures.

On the other hand, innovative companies often bet everything on a single card. Various startups increasingly base their entire business model on AI. Often, if they hit a niche, they become "Unicorns" (reaching a billion-dollar capitalization in a short time) or simply vanish from the market—either going bankrupt or being acquired by stronger competitors.

Meanwhile, the most numerous group—small and medium-sized enterprises (SMEs)—acts selectively. On one hand, they are cautious, choosing only the best tools due to limited resources. On the other hand, statistically, they are more likely to reach for AI than large firms.

Why do many AI implementations fail? There are several reasons:

• The pursuit of novelty: Many companies invested in AI on a wave of hype, spending millions on innovation without fixing real underlying problems in management or logistics.
• Automating "broken" processes: Sometimes, bad processes were automated without being repaired first.
• The Talent Gap: Frequently, implementations were entrusted to employees who, tempted by other firms, took their knowledge with them, leaving tasks unfinished.
• Underestimated costs: Companies invested in infrastructure and entire departments that proved extremely expensive to maintain.

Success Follows Success

Many AI implementations are spectacular. The World Health Organization launched an intelligent health surveillance platform that detected outbreaks of a new flu strain several weeks earlier than traditional methods. The electronics manufacturing leader Foxconn Technology Group & BCG created an ecosystem of AI agents to automate decision-making processes in factories; they succeeded in 80% of cases, unlocking resources worth approximately $800 million.

In Kenya, the mobile platform AgriAI enabled 0.5 million farmers to increase yields by 30% and reduce pesticide use by a quarter. The European giant Schneider Electric used AI to optimize energy consumption in its factories and office buildings, saving 5–15% in expenses within the first two weeks.

Tomasz Teluk

PR & Communication Manager 

MindPal.co