Artificial Intelligence: Between Real Impact and Market Hype

To cut through this noise, it helps to focus on where AI already delivers tangible value today. Below are three specific enterprise process areas where AI is no longer just experimental, but increasingly operational.
 

Three Areas Where AI Delivers Real Impact Today
 

• Multilingual content and translation workflows

AI supported translation and localisation tools have reached a level where they can accelerate first pass translation, improve terminology consistency, and reduce turnaround time across multilingual content operations. Human experts still remain essential for nuance, brand tone, legal accuracy, and final quality control. The shift is therefore less about replacing expertise and more about redesigning the workflow around faster machine support and more targeted human review.
 

• Document intensive finance operations

Finance teams are increasingly applying AI in document heavy processes such as invoice handling, reconciliations, and exception management. Recent McKinsey research shows that finance organisations are expanding generative AI use across multiple finance use cases, with growing investment and operational uptake. For document intensive finance work, this shifts daily effort away from repetitive extraction and routing tasks toward exception handling, oversight, and control quality.
 

• Marketing content localisation and adaptation

AI is also increasingly used to adapt existing content for different languages, markets, audiences, and channels. Rather than replacing creative direction, it helps accelerate rewriting, localisation, and consistency checks across larger content volumes. This can shorten production cycles while keeping human editors in control of tone, accuracy, and cultural fit.

And there are many more such areas.
 

Why AI Is So Often Oversold
 

If AI already delivers real value, why does the public discussion still feel so disconnected from reality? The answer lies less in bad intent and more in structural incentives. Several actors benefit, directly or indirectly, from optimistic narratives.
 

• Startups that need to raise venture capital

Venture funding rewards growth potential and future scale. In that environment, startups are incentivised to present early solutions in the strongest possible light, sometimes describing prototypes, roadmaps, or narrow use cases in terms that sound broader than their current operational maturity.
 

• Advisory narratives that move faster than implementation reality

Another driver of overselling comes from discussions that stay at the level of ambition, strategy, or boardroom narrative for too long. Once data quality issues, integration effort, governance, and operational constraints become visible, progress often looks more gradual than the original story suggested. Broader market research reflects this same gap between adoption and scaled value creation.
 

• Software vendors relabelling existing features as AI

A more subtle but widespread driver of hype is the relabelling of existing automation, analytics, or assistant capabilities as AI led innovation. Regulators have already started to scrutinise deceptive or weakly supported AI claims more closely, which shows that the credibility risk is not just theoretical.
 

Separating Signal from Noise
 

AI is already delivering real value, but mostly in specific processes, specific tasks, and specific operating contexts. Overselling persists because different market actors are often rewarded for visibility, momentum, and optimism more than for operational precision.

Understanding these incentives helps decision makers ask better questions:

• What exact process or task is being improved?
• Where is the measurable business benefit?
• How mature is the solution in real enterprise use?
• What would successful adoption realistically require?
   

A Practical Way Forward
 

AI is neither magic nor empty marketing. It is a powerful tool whose value becomes visible when applied with precision and realism. The most useful executive conversation is therefore not whether AI matters in principle, but where it can improve speed, quality, control, or relevance in a concrete part of the business. Organisations that separate signal from noise early are better positioned to turn selected use cases into disciplined execution. Broader market research suggests that this step from experimentation to scaled impact remains a challenge for many, which makes focused prioritisation even more important.