2026 tech industry forecast: In the AI era, should mid-market firms look to the human?
It’s no bold prediction to say that AI will dominate tech in 2026. But most of the hundreds of billions of investment capital currently in play is being spent by or on a handful of giant firms like Nvidia and Microsoft.
So what’s the outlook for the tech industry middle market?
As talk of an AI bubble gets louder, even while the valuations for the largest firms climb well into the trillions, mid-market tech companies should take this moment to consider how to not just attract investors in a saturated market but also survive a correction. For the most creative companies, that might look like bucking the dominant industry trend towards ever more AI by asking how to put the human back in the loop.
Keep reading to learn more about what that means and where else the mid-market tech industry is headed in 2026.
What are the key tech trends that will shape 2026?
AI will continue to be the major tech story in 2026. But look for more downstream ripple effects, including not just continued investment in data centers but also job loss, governance and compliance issues and potential economic upheaval if the bubble theory does prove true.
Here are key trends or areas to be aware of:
- Impact on employment: People will lose jobs due to AI and automation. Other jobs are going to evolve. There is a huge amount of uncertainty here — no one knows exactly what is going to happen — but at a minimum, companies will need to upskill their teams to help everyone adapt to the ongoing wave of change.
- Data center construction: Even the most cutting-edge tech starts with putting shovels in the ground. Companies like Meta and OpenAI have already committed vast sums of money to build the data centers needed to power their AI systems, but that spending will need to continue in order to meet stated growth goals.
- Multi-agentic AI platforms: An AI agent allows you to automate a certain routine task. Agents have so far been fairly limited in what they can accomplish, but in 2026, look for tools that try to overcome those limits by linking multiple agents together to automate more complicated, multi-step work. For example, fintech companies might offer better trading automation via a series of agents that can assess and then react to changing marketing conditions.
- Faster, priority-based planning: Firms are moving away from annual planning cycles because change is happening so quickly. Look for more companies to adopt priority-based planning that gives decision-makers more flexibility to respond to shifts in the market.
- Governance and compliance issues: AI companies are gathering and using vast amounts of data, as are businesses that are further incorporating AI into their workflows. This raises major governance and compliance questions: Who owns this data? How is it secured and governed? And frankly, who is going to get sued when something goes wrong?
- Major benefits in medicine and healthtech: Digital health companies are genuinely transforming healthcare. For example, doctors using AI to offer second opinions on MRIs are seeing outcomes significantly more accurate than either all-human or AI-only analysis. With the healthcare industry facing massive Medicaid cuts, look for more providers to turn to AI to stretch limited resources, although with questions about how to define their own role in the loop.
- Cybersecurity challenges: Because firms have so much more data now and also face AI-powered cyberattacks, the cybersecurity stakes are higher than ever. Companies need to continue to evolve their cyber defenses via practices like ongoing training and penetration testing. But don’t think of cyber as a cost-suck: companies with more effective cybersecurity systems are often worth more to potential buyers or investors.
Is AI a bubble about to burst?
Today, Nvidia alone is worth roughly $5 trillion dollars, or over 8% of the S&P 500, while other tech and AI also have multitrillion-dollar valuations. Meanwhile, spending on data centers accounted for 92% of the U.S. GDP growth in the first half of 2025.
Is this sustainable? And what happens if it’s not?
Today, generative AI itself is wildly unprofitable. While much of the money being invested in the technology is coming from behemoths like Microsoft and Google, running the latest LLMs like GPT-5 demands hundreds of billions of dollars invested in infrastructure like data centers, energy production and GPUs (hence Nvidia’s ballooning market value).
Even the tech giants or top VC firms can’t keep spending that kind of money at a loss forever. But to recoup their costs, let alone turn a profit on generative AI, AI companies would have to charge users far more than they do now, which means that generative AI may simply not be viable as a business model, at least in its current form.
Generative AI is just one branch of AI technology, so a correction there wouldn’t mark the end of AI, just a shift in direction. But that shift would likely cause significant short-term pain.
Analysts have pointed to the dotcom bubble in the early 2000s, which erased $5 trillion in value from the stock market, as a potential analogy. That crash created a major disruption to both the tech industry and the larger economy, with many firms going out of business or merging with competitors to survive.
Given that AI growth has essentially kept the U.S. economy out of a recession in 2025, it’s not unreasonable to predict that a correction here would push the economy into negative territory.
However, dotcom crash only slowed, rather than halted, the tech industry’s momentum. Barring the truly unforeseen, it’s tough to see a tech crash today ending differently.
For now, firms should consider AI tech from a profitability perspective. What opportunities are there to harness AI in a way that can make money rather than lose it?
Why mid-market tech firms should look to the human to stand out
No one disputes that AI technology has revolutionized the business world. But what remains to be seen is what comes next.
Is artificial general intelligence a mirage — or an inevitability that could be here as soon as 2030? Will companies continue to integrate AI more deeply into their workflows, or will reports that only 5% of businesses actually see rapid revenue gains from AI slow enthusiasm?
There is much uncertainty right now. So smart middle-market firms are continuing to adopt AI where it makes sense, but moving cautiously before placing any big bets.
One bet that firms should consider is working to find the human in the loop. More jobs than ever involve AI, which means some jobs are being lost to automation entirely, and others are being transformed into something new.
What happens to all those people? How can companies develop new ways for people to ride this wave of technological change in a way that allows human beings to continue to contribute to the economy?
Often, the largest tech firms don’t seem to be asking these questions. So the middle market has an opportunity here to stand out from the pack and even attract investor dollars in a highly competitive market by refocusing on how to use the latest technology to best serve humans.
Mid-market firms are also poised to move faster if there is a cultural pushback to AI: more people wanting to talk to a human rather than engage with a chatbot or read a book rather than ask ChatGPT.
What it means to find the human in the loop will vary from company to company. A digital health company offering AI tools to doctors should think long and hard about what it would look like for doctors to thrive in an AI-heavy hospital environment. SaaS companies updating their products to leverage more AI analytics should consider what that means for end-users in analytics-heavy roles and how those users can continue to deliver value.
There is an opportunity here for firms to embrace the utility of AI in a way that benefits people rather than makes them redundant. Will companies take it?
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