We’ve been hearing the hype and hysteria for several years now: AI will be taking over everything, while eating entire classes of jobs. Needless to say, taking on initiatives at such a scale means fierce headwinds – from management, employees, and society at large. AI proponents have received their share of slaps in the process. Now, cooler heads are prevailing, it looks like smaller, nimbler, and less threatening AI may be the better way to go.
A new analysis out of Unisys Corporation offers compelling observations that should help shape the perspectives of business leaders, practitioners, and artificial advocates for the coming year. These include a notable shift away from the larger, grandiose promises of AI toward smaller, more practical efforts. Second, while this year’s economy may see its share of layoffs, don’t blame AI – there’s less talk – or evidence – of automating people out of jobs.
With AI projects this year, there will be less of a push to boil the ocean, and instead more of a laser-like focus on smaller, more manageable projects. “The majority of AI deployments will not be large-scale initiatives,” the Unisys report authors conclude. “They will be smaller, task-based integrations that fold into existing processes. These developments will use smaller data sets that are easier to clean, require lower investment thresholds, enable smoother change management, and deliver quicker results.”
In particular, three types of applications will be commonplace as repeatable, high-ROI deployments. These consist of chatbots for employees and clients, AI coding agents, and AI-driven service assistants. Such apps are “packaged, measurable, and quick to deploy, the report states. “Instead of pursuing large-scale transformations, teams are finding success with targeted deployments that fit into existing workflows. The wins will come from projects that assist rather than replace.”
What’s not to like? Consider the advantages of smaller, sleeker AI:
- Less begging boards and C-suites for more funding: Lower initial investment with fewer AI specialists needed and less data preparation required.
- Faster deployment timelines
- Smoother change management
- Simpler execution
- Quicker learning cycles
- Higher success rates
On the job front, fear about AI sweeping away roles and professions seems to be abating. While the state of the economy is often an uncertain thing, so it’s difficult to predict which way workforces will grow. If layoffs do occur over the course of 2026, don’t blame AI.
For the most part, businesses are realizing that layoffs will diminish any advantages AI may provide, the Unisys report’s authors state. “Despite the rise of automation, widespread AI-driven layoffs are not expected to materialize in 2026. Organizations know that blunt headcount cuts undermine transformation. Rather, leaders will redirect productivity gains to backlog reduction, customer experience, and modernization.”
There is one caveat, however: junior coders, however, may see diminished opportunities, as “AI agents will automate routine coding,” they said.
If anything, AI-driven productivity gains are fueling job growth, “consistent with historical patterns rather than mass displacement,” the report states. Tellingly, “companies that initially planned AI-related headcount cuts are reversing course, finding those reductions slow implementation and limit returns.”
Ai is also fueling job growth, with demand rising for security engineers, data engineers, and platform teams required to scale AI responsibly. In addition, roles will evolve: “analysts will become insight curators; support agents will become case managers; engineers will become system owners assisted by agents.
As noted in my previous post on data center energy consumption, a building boom is underway – meaning a lot of associated jobs to go with it.
“Be wary of business cases built on large-scale headcount replacement,” the authors warn. “They are hard to realize and may carry invisible costs such as degradation in
the customer experience.” Employees have “valuable institutional knowledge that may be key to enabling processes to work fluidly and with high-quality outcomes.”
