In the early hours on April 10th, an anti-AI protestor made an attempted arson attack on the home of OpenAI's CEO, Sam Altman. 

This was an alarming development in the rising tide of anti-AI sentiment, as job cuts in tech firms continue to rise. In the immediate days after, Altman addressed this uncomfortable reality head-on and called for a de-escalation of tensions that presently surround AI. 

However, industry insiders may have a different concern.

While anti-AI protestors believe the technology is too powerful and will lead to spiraling unemployment and social unrest, organizations that are at the leading edge of AI deployment have a different perspective. 

Up until now, the promise of the game-changing results is responsible for the billions of dollars propping up foundational AI companies, who raised over $80 billion in 2025. In the same year, U.S. companies spent an estimated $37 billion on generative AI alone. 

Yet in enterprises and multinationals, there is a different kind of disquiet brewing among executives that AI isn't delivering on the promise they were sold on. 

A new report from AI engineering firm Solvd surveyed 500 U.S.-based CIOs and CTOs at organizations generating more than $500 million in annual revenue to get a clearer picture of how AI deployments are progressing within large enterprise organizations. 

The finding painted a nuanced picture. On one hand, board members and leaders are growing tired of long development cycles and experimentation phases. With 69% of those surveyed noting that  AI is not fully embedded across key business decisions and 72% believe it's likely AI projects will be shut down in the next year for failing to meet KPIs. 

Even so, the same report found 90% expect investment in experimental AI initiatives to increase in 2026. 

With such huge sums at play, it’s understandable that expectations are high. However, if AI doesn't perform as expected, it isn't a reason to disregard the technology and shut down initiatives. In many cases, companies have focused on "AI theatre" that has focused on style over substance or a lack of alignment between strategy and business objectives. 

This backlash against AI is a fundamental misdiagnosis in the way that the technology has actually been built to function and the strategies needed to produce an ROI on the billions of dollars already ploughed into the technology. 

Both the AI fanatics who predicted we would have super general intelligence in a few years to advance humanity and the naysayers who expect AI to wipe out entire industries are two extreme ends of the spectrum. Unfortunately, the hype on both sides has clouded the reality and led to unrealistic expectations from stakeholders with skin in the game. 

This skepticism shouldn't lead us to the conclusion that investment opportunities with AI have already been exhausted. However, returns might not happen overnight or be found in the most obvious place. 

Let's take a closer look at three AI-related opportunities investors should keep an eye on this year. 

AI capabilities improving exponentially are creating investment opportunities

As skepticism around AI seems to be reaching a fever pitch in 2026, it’s causing people to worry about the investment potential of the technology and the capital that's already been allocated. 

However, this is as much about misaligned expectations as anything else. 

Enterprise leaders are within their rights to expect to see an uptick in productivity or revenue, but looking at things linearly is a mistake. AI is not only amplifying success if you do it right, but it's also amplifying failures. Technology is not the problem, but rather how it is deployed.  

If we actually look at the technology, its capabilities continue to improve at an incredible rate. We only need to look at how different the latest LLM models from Anthropic and OpenAI are from the original releases, which pale in comparison to today's AI.

The pace of development is expected to gain steam as the foundational models and adjacent infrastructure mature. 

Several big tech leaders follow this sentiment. Amazon (NASDAQ:AMZN) CEO Andy Jassy recently wrote that "AI is a once-in-a-lifetime opportunity where the current growth is unprecedented and the future growth even bigger,” in a letter sent to shareholders defending $200 billion in planned spending. 

Meanwhile, over in the Anthropic camp, Claude Mythos, its latest AI model, hasn't been released to the public because it’s too powerful. However, researchers at Aisle have found that smaller, cheaper models deployed can replicate similar feats. This suggests we're at a new breakthrough in the field that will see progress leapfrog, rather than move in small increments. 

Companies on this exponential curve continue to offer investment potential, as seen by Bay Area investor offering his millionaire dollar home in exchange for shares in Anthropic. 

Enterprises turn to external partners to drive AI implementation with speed and precision 

The competitive edge is no longer access to AI, as Anthropic and OpenAI have made it accessible to every business. Nor is it about chasing "cool AI" that sounds impressive on paper but isn't realistic in practice. 

Seventy-one percent of global chief information officers in a recent survey said that their AI budgets would be frozen or cut if value from AI couldn't be demonstrated within two years. 

Many AI projects remain in experimental phases, creating applause but limited scalable growth or efficiency. Success depends on strategy-first deployment, not just technology

Although the experimentation phase is important for enterprises, learning how to scale at speed is going to be a key focus. To do this, organizations will need to break down common execution failures that include over-engineering solutions, a lack of clear ownership and poor integration with existing workflows and legacy software applications. 

CIOs and CTOs are increasingly choosing to collaborate with external partners and consultants to meet KPIs and deliver the results needed to avoid having their funding cut and projects shut down.

These challenges are also creating investment opportunities. 

In particular, this means AI adjacent software engineering companies could see their revenue increase notably if demand for their services grows, thanks to this trend, presenting another investment opportunity.  Leveraging AI through legacy software tools means that enterprises can unlock ROI faster and don't need to reinvent the wheel to get there, and specialist engineering companies are helping to build realistic solutions that focus precise implementation as a lever to speed. 

Beyond engineering partners, enterprises are also turning to specialized AI platforms that embed intelligence directly into their core workflows.

Aside from partners of apps like Snowflake and Salesforce, we can expect to see companies in the cybersecurity space present investment potential. In response to Claude Mythos, Anthropic has launched Project Glasswing and has named 40 organizations as partners who will help to patch vulnerabilities that AI could expose. 

Software consultancy firms are also expected to play a growing role in the AI industry, helping enterprise organizations solve teething problems and understand how to refocus their efforts to support tangible business goals. 

By combining quick wins with longer-term experimentation, companies can keep up support of AI initiatives by meeting immediate KPIs that maintain investments for more ambitious projects that take longer to build. 

How To uncover AI startups with real investment potential?

With so many new AI startups it can be overwhelming to uncover which of those have real investment potential. For example some companies don't actually hold any property AI solutions. Instead they are essentially repackaging foundational LLMs like Claude or ChatGPT with a new wrapper and UI.  

Other startups are competing in a highly crowded market, while others may not have real translation pathways to scale into. 

Yet early-stage companies remain one of the best ways to generate the best returns, by getting in on the ground with the next big thing. To access these opportunities without getting overwhelmed by the sheer volume of early-stage ventures, independent investors can turn to VC firms who have active AI portfolios. 

These firms will have done extensive due diligence and research to find startups with the most potential. 

In many cases, they are going above and beyond to increase the likelihood of success by creating inroads that help new ventures cross the "valley of death" and scale revenue.

The key here is to look for LP investment opportunities with VC firms who are actively supporting the evolution of the AI industry and to avoid those taking a hands-off approach. 

Seeing beyond AI skepticism 

Although skepticism around the technology may be growing, this is largely thanks to the amount of hype that has accompanied the tech since the first GenAI models went public and the slew of unrealistic predictions that followed.

AI is not underperforming. If anything, the technology is more powerful than ever. However, to extract value, companies need to avoid AI vanity projects and focus on deploying the technology in a strategic manner that unlocks low-hanging fruit along with more ambitious goals. 

These three examples show that AI continues to present investment opportunities. 

Benzinga Disclaimer: This article is from an unpaid external contributor. It does not represent Benzinga’s reporting and has not been edited for content or accuracy.