Has AI taken your job yet?
Back in 2023, when GenAI first gained traction, countless analysts claimed that AI would replace multiple jobs within two or three years. We’re in 2026, and… not much has changed.
Often, these predictions rely on the desire to generate viral posts or come from the so-called “experts” who simply don’t understand the underlying technology.
That is why in this article we offer a different perspective: grounded in real, delivered AI automation projects for our customers, we share our outlook for the closest future.
Predictions: Intelligent Automation trends
Thesis 1: the SMB vs. enterprise split (single agents vs. multi-agent systems)
Prediction: Small and Medium Businesses (SMBs) will focus on deploying single, specific AI agents to solve individual problems, utilizing agile SaaS tools like n8n. Enterprise clients will move towards complex Multi-Agent Systems (MAS) orchestrated by platforms like UiPath.
Why this will happen: SMBs need speed and low overhead. They cannot afford the infrastructure required to manage a swarm of agents. Tools like n8n allow them to stitch together a “Customer Service Agent” or a “Lead Gen Agent” quickly and cheaply.
Enterprises have hit a ceiling with simple bots and agents. To handle complex processes like “order-to-cash,” they need agents that talk to each other. For example, one to read the invoice, one to check the ERP, and one to validate fraud. This requires an orchestration layer that ensures governance and security, playing directly into the hands of established vendors like UiPath who are pivoting to “agentic” orchestration.
Thesis 2: human-in-the-loop (HITL) will be mandatory
Prediction: we will see a sharp increase in AI deployments designed specifically with human-in-the-loop (HITL) architectures. Purely autonomous processes will remain rare in critical business operations.
Why this will happen: trust is low. After the pilot failures of 2024-2025, companies have realized they cannot let AI run business-critical processes without supervision. The hallucination rate is still too high for finance or legal compliance. Platforms that prioritize easy human intervention – where the AI does the work but “asks” for sign-off before executing a transaction – will win. This is a defensive move: companies want the speed of AI but are terrified of the liability of an autonomous agent sending a million dollars to the wrong IBAN.
Thesis 3: rise of AI assistants, stagnation of autonomous agents
Prediction: 2026 will be the year of the AI assistants (GenAI grounded in enterprise data), not the fully autonomous agents. While tech X and LinkedIn talk about agents that run entire companies, in reality, agents will still be crawling.
Why this will happen: true autonomy requires reasoning capabilities that are still expensive and unreliable at scale. Most “agents” today get stuck in loops or make bad decisions when variables change. In contrast, “assistants” – tools that search internal wikis, summarize meetings, and draft emails for a human to review – are mature and reliable. We will see massive adoption of RAG (Retrieval-Augmented Generation) systems that help employees do their jobs faster, rather than robots replacing the employees entirely.
Thesis 4: high vendor churn due to pilot failures
Prediction: we might see a wave of contract cancellations and vendor switching. Companies that bought into the hype in 2024-2025 and saw 95% of their vertical AI projects fail will fire their current providers.
Why this will happen: MIT research indicates that nearly 95% of GenAI pilots failed to deliver measurable P&L impact. CIOs and CFOs are done paying for “innovation theater.” They are cutting losses on tools that didn’t deliver and looking for new partners who can promise specific, boring, measurable outcomes. This creates a volatile market for service providers – beneficiaries are unsafe, and new challengers with “fix-it” attitudes have an opening.
Thesis 5: low real impact on P&L (but high “AI washing”)
Prediction: despite the noise, AI will have a negligible impact on the actual profit & loss statements of most non-tech companies in 2026. However, CEOs will continue to claim huge efficiency gains to boost stock prices.
Why this will happen: integrating AI into legacy systems takes years, not months. The productivity gains are often eaten up by the cost of the AI itself (licenses, compute, cleaning data). Yet, executive compensation is tied to stock performance, which is currently tied to having an “AI strategy.” We will see “AI washing” where standard layoffs or cost-cutting measures are rebranded as “AI-driven optimization” to satisfy the stock market, even if the AI had nothing to do with it.
Black swans: low probability, high impact events
Thesis 1: the “AI security pearl harbor” happens
Possible scenario: a massive AI security compromise occurs. A major enterprise or government institution collapses or suffers a data leak on an unprecedented scale due to an autonomous agent being hijacked or a model being poisoned.
Impact: this would trigger an immediate “AI winter” of adoption. Governments would push the brakes on approvals. CIOs would pull the plug on cloud AI projects overnight. We would see a panic-driven return to on-premise hardware and “dumb” software, setting the industry back by years. Data centers built for AI would sit empty as risk appetite vanishes.
Thesis 2: The “market-killer” model
Possible scenario: a new player (or a rogue release from a major lab) releases a model that is exponentially better and cheaper than anything else – effectively free and much more intelligent than any other LLM.
Impact: this would pop the AI stock bubble. If intelligence becomes free, the business models of OpenAI, Google, and Microsoft (based on selling compute and API access) collapse. The trillion-dollar valuations evaporate. This deflationary shock would wreck the tech sector’s economy but potentially unleash a chaotic wave of consumer innovation, as high-end intelligence becomes a commodity like electricity.
But let’s take a deep breath…
These are just predictions based on our current market analysis and project experience, not guarantees of the future.
We take no responsibility for how the market ultimately evolves.
“Black swan” scenarios carry a low (or even very low) probability, but their potential impact is systemic and severe. We strongly believe that a robust enterprise strategy must include contingencies for such edge cases, as being prepared for the unlikely is often what ensures survival.
Good luck with all your AI & Automation projects in 2026!
