AI adoption is creating a clear divide among employees. Some see AI as a tool to increase their impact, while others see it as a threat to their role. So, where does the truth lie between these two mindsets? Let’s explore.

AI doesn’t eliminate roles, it removes low-leverage work

Over the past two years, we’ve seen a wave of layoffs often attributed to “AI replacing humans.” Among widely discussed cases, IBM openly stated that over 7,000 back-office roles may no longer need to be hired because AI can absorb the work. Many other tech players, including Microsoft, Amazon, and HP, have cut between 6,000 and 200,000 employees as a shift toward AI-driven initiatives.

Meanwhile, beware: many might make misleading conclusions. They are often treated as straightforward examples of AI replacing human labor, when in reality they reflect a deeper restructuring of how value is created inside organizations.

Taking a closer look, it becomes evident that in many examples like these, people weren’t displaced because AI was smarter or outperformed people.

They were displaced because one (or more) of the following reasons:

• AI made it obvious that the output didn’t require so many layers of coordination.
• The work didn’t materially change outcomes.
• The role existed mainly because systems were manual and fragmented.

From this perspective, it becomes evident: AI isn’t coming for roles, it’s here to remove low-leverage work. Roles that exist mainly to handle volume or work around broken systems no longer make sense once those problems disappear.

This is already playing out across teams. For example:

• In operations, AI-powered tools (Celonis, UiPath Process Mining, ServiceNow AI Ops, Signavio, etc.) tackle which work reduces cycle time and cost.
• In marketing, AI-driven attribution tools (HubSpot, Adobe Sensei, Marketo, etc.) show which campaigns truly drive revenue.
• In finance, AI-enabled platforms (Anaplan, Workday Adaptive Planning, BlackLine, Planful) automate reporting and forecasting.
• In customer support, AI-powered tools (Zendesk, Intercom Fin, Gong, Ada) handle repetitive tickets and streamline customer communication workflows.

As AI reduces friction and automates workflows, organizations and employees are increasingly forced to ask these two questions: 1) where real value is created, 2) where human input is essential in areas AI cannot handle. Teams that explore and embrace this shift early will be better positioned to adapt as AI continues to compress the cost and effort of execution.

AI adoption beyond automation: the transparency tool reshaping roles and processes

An overlooked but highly efficient aspect many businesses miss: AI introduces a level of transparency most organizations have never had before. This visibility changes how organizations make decisions and how they manage business resources. In particular, AI-powered spend analysis is set to become one of the most transformative forces inside companies.

Here’s why:

• It makes spending explainable. Since AI connects costs to actual usage and outcomes, spending can be traced to specific tools, teams, or processes. This means spending decisions no longer have to be based on assumptions.
• Inefficiencies become hard to hide. Duplicate tools or bloated, effort-heavy processes surface quickly when cost and outcomes are analyzed together. As a result, businesses can efficiently eliminate redundant tools or processes and reallocate resources accordingly.
• It makes runway a competitive advantage. Clear spend visibility and AI-powered insights help companies move faster with less burn.

Ultimately, the level of business success is largely determined by the ability to turn spend data into an operational signal. When, instead of tracking costs in isolation, organizations clearly see how spend translates into productivity and revenue, that’s where real differentiation begins.

Smart spend management platforms like this already exist, take Spendbase, for example. In this case, AI can be the next layer that unlocks even deeper insights.

The real risk isn’t job loss. It’s being invisible to ROI

ROI and cost efficiency are at the very center of any business. Therefore, as AI increases transparency into multiple business areas (performance, time, spend, etc.), work that cannot be clearly tied to outcomes loses its footing. Hence, roles that fail to translate their work into clear outcomes risk being deprioritized.

As AI automates execution, the people and teams that remain essential are those who can clearly explain how their work impacts the business in ways AI cannot. Such as:

• Defining the right problems to solve and redesigning workflows accordingly;
• Interpreting data and weighing trade-offs, especially when there is no obvious answer;
• Validating AI outputs rather than simply executing tasks manually;
• Applying ethical judgment and accountability when data alone is insufficient.

Apart from this, another important area of impact lies in organizational and customer-facing work, where human-driven value is created from responsibilities focused on aligning teams, resolving complexity, building trust, and, particularly, innovating.

Imagine a product team competing in a crowded market or where competitors are continuously copying each other. In this case, AI can meticulously handle research and benchmarking: analyze competitor offerings, user behavior, reviews, and market gaps.

It can even generate predictions or simulate feature adoption. However, it takes human vision and accountability to challenge assumptions and find unpaved paths for growth.

When it comes to creating something inherently new, with no existing market equivalent, AI may fall short at innovation. Yet these are often the areas where the greatest business impact lies.

In most organizations, changes revolving AI adoption won’t happen overnight. Roles will gradually shrink, shift, and eventually be redefined. As a result, human contributions that can’t be tied to measurable outcomes quietly lose relevance.