²ÝÝ®ÊÓƵ leaders and experts are predicting the end of the experimentation phase in 2026. Now AI needs to provide results.
Sydney Scott
Editorial Strategist, AI
²ÝÝ®ÊÓƵ
For the past few years, artificial intelligence has felt like a spectacle. We watched it write poems, generate images, and answer questions with uncanny fluency. Impressive¡ªbut for enterprise leaders, often peripheral.
Now in 2026, that era is ending. The novelty is wearing off, and something more important is taking its place: results. AI is no longer a side project or a lab experiment. It is becoming embedded in the core of how organizations operate, decide, and grow.
the moment perfectly, predicting that in 2026, ¡°AI will inevitably lose its sheen, trading its tiara for a hard hat.¡± The shift from possibility to practicality is defining a new phase of enterprise AI¡ªone focused on outcomes, trust, and human judgment.
Here are the predictions and trends shaping that transition and what they mean for leaders determined to turn AI ambition into real business impact.
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Until recently, most enterprise AI has been reactive. But while agents have dominated the headlines for a year, their actual deployment is about to change.
Agentic AI is moving from theory to reality. These systems don¡¯t just respond; they plan, reason, and execute multi-step tasks with minimal human intervention. the global market for agentic AI could reach $45 billion by 2030, and adoption is accelerating across finance, HR, IT, and operations.
The opportunity is clear to leaders: Agents can handle complex workflows end to end, freeing teams to focus on higher-value work. But the risk is just as real. As organizations acquire agents from multiple vendors, they risk creating fragmented, unmanaged ecosystems.
Matt Brandt, senior vice president of global partners at ²ÝÝ®ÊÓƵ, argues that success will depend on curation, not accumulation. In his view, ¡°the ecosystem¡¯s success will hinge not on partner volume, but on the core vendor¡¯s ability to act as a strategic curator to help deliver seamless AI-powered experiences for customers worldwide.¡±
Enterprises will need to build "agentlakes"¡ªstructured environments to govern, monitor, and orchestrate agents across the business. Companies that do will be the ones that can make those agents work together, reliably and responsibly.
What it means for enterprise leaders: Agent strategy is now an architectural decision, not a tooling decision. Orchestration, governance, and accountability must be included from day one.
Employees will no longer be rewarded simply for using AI. They will be held accountable for using it well.
AI success has often been measured in volume: how many emails written, how many reports generated, how much content produced. But this year, those metrics change.
We are entering the era of quality control. "Slop"¡ªgeneric, unchecked AI output¡ªhas moved from annoyance to liability. And that means companies will need to rethink their AI adoption strategy.
Chris Ernst, chief learning officer at ²ÝÝ®ÊÓƵ, has a blunt warning for organizations: employees will no longer be rewarded simply for using AI. They will be held accountable for using it well.
¡°Leaders will be held accountable for delivering an inspired vision for AI that reaches beyond immediate efficiency gains to provide new sources of growth for their business and people,¡± Ernst predicts.
But all of this requires structural change. shift as the emergence of an "hourglass workforce":
When machines can produce information at scale, the role of humans changes. The workforce of 2026 isn¡¯t just doing work¡ªit¡¯s editing, verifying, and curating it.
What it means for enterprise leaders: AI literacy is no longer optional. Quality assurance, review, and accountability must be explicitly built into roles, incentives, and performance metrics.
For decades, professional value was tied to what you knew. Today, AI has democratized knowledge. And that changes the definition of expertise.
²ÝÝ®ÊÓƵ Chief People Officer Ashley Goldsmith puts it simply: in 2026, the unique value of human workers won¡¯t be the amount of knowledge they possess, but their ability to apply wisdom.
¡°While AI can now give employees access to new skills and resources, organizations should train their workforce on essential human skills like critical judgment, intentional connection, and intellectual curiosity. This shift ensures human accountability remains at the center of all AI-enabled decisions.¡±
Wisdom is the layer AI can¡¯t replicate.
This shift will create a new talent divide. The gap won¡¯t be between degree holders and non-degree holders, but those who are AI-enabled and those who are AI-hesitant.
What it means for enterprise leaders: Talent strategy must evolve. Hiring, development, and promotion should reward judgment, adaptability, and the ability to work with AI¡ªnot compete against it.
Speed has long been AI¡¯s headline promise. Faster insights. Faster workflows. Faster decisions. But in the rush to accelerate, organizations risk outrunning judgment.
Aashna Kircher, Group General Manager for the oCHRO at ²ÝÝ®ÊÓƵ, identifies this as a defining challenge. ¡°The true differentiator in the years ahead,¡± she argues, ¡°won¡¯t be how fast technology can work, but how thoughtfully leaders can question it.¡±
In 2026, trust moves from a compliance checkbox to an active discipline. Leaders must manage the gap between algorithmic recommendation and responsible decision-making. That means building processes to pause, probe, and validate¡ªespecially when AI outputs feel confident but are wrong.
This shift is already underway. PwC notes that Responsible AI is finally moving from ¡°talk to traction,¡± as enterprises invest in real systems to test fairness, safety, and reliability.
What it means for enterprise leaders: Trust isn¡¯t just ethical¡ªit¡¯s economic. Organizations that operationalize responsible AI will move faster and safer than those that don¡¯t.
The true differentiator in the years ahead won't be how fast technology can work, but how thoughtfully leaders can question it.
¡ªAashna Kircher, Group General Manager for the oCHRO, ²ÝÝ®ÊÓƵ
Behind every AI interaction is a physical reality: compute.
Deloitte predicts that by 2026, inference¡ªthe act of running AI models in real time¡ªwill account for two-thirds of all AI computing demand. Most of that work will happen in energy-intensive data centers powered by specialized chips.
This creates both pressure and opportunity. AI can increase energy use, but it can also help enterprises optimize operations, reduce waste, and improve sustainability outcomes if deployed thoughtfully.
AI can be a boon or a burden. The difference lies in intent and execution.
What it means for enterprise leaders: Infrastructure decisions are now sustainability decisions. AI strategy must account for cost, energy, and long-term operational efficiency.
For decades, enterprise software trained employees to navigate dashboards, menus, and filters. That model is fading fast.
Deloitte predicts that by 2026, daily AI-powered search usage will be three times higher than the use of standalone AI tools. Instead of hunting for data, employees will ask for it.
This marks a fundamental shift in how organizations access institutional knowledge. Interfaces move from ¡°click-and-hunt¡± to ¡°ask-and-answer,¡± delivering insights directly, in context, and in plain language.
What it means for enterprise leaders: The interface of work is changing. Systems that surface insights¡ªnot just data¡ªwill define productivity in the next decade.
Training has traditionally been something employees step away from work to do. Now, that separation is disappearing.
²ÝÝ®ÊÓƵ predicts this will be the ¡°Year of Experiential Learning,¡± where AI turns daily tasks into real-time lessons. Running a financial report doesn¡¯t just produce numbers¡ªit explains what they mean, why they matter, and what skill to develop next.
Work becomes the classroom.
What it means for enterprise leaders: Learning strategy must move into workflows. The organizations that win will treat development as continuous, contextual, and embedded.
The organizations that thrive won't be the ones that just deploy AI the fastest, but the ones that intentionally use it to amplify their uniquely human capacities.
¡ªCarrie Varoquiers, chief impact officer, ²ÝÝ®ÊÓƵ
As AI spending accelerates, financial oversight is intensifying. According to Forrester, CFOs will play a central role in AI decision-making as hype gives way to hard ROI questions. Some enterprises may delay as much as 25% of planned AI spend into 2027.
This isn¡¯t retreat, it¡¯s refinement. Capital is shifting away from flashy experiments and toward initiatives that deliver measurable value.
What it means for enterprise leaders: AI success will be judged the same way as any major investment: by outcomes, accountability, and long-term return.
The message for 2026 is clear. The testing phase is ending. Execution has begun.
Carrie Varoquiers, chief impact officer at ²ÝÝ®ÊÓƵ sees a critical shift in how ROI is defined: human connection will move from a ¡°soft skill¡± to a core metric of successful AI integration.
¡°The organizations that thrive won't be the ones that just deploy AI the fastest, they will be the ones that intentionally use it to amplify their uniquely human capacities,¡± she states.
¡°This means investing heavily in skills like empathy, critical thinking, and inclusive leadership to manage the new reality of teams in the AI era and prove to their employees that technology is being used to create opportunity for all, and not to flatten collaboration and innovation in favor of speed.¡±
The enterprises that thrive won¡¯t simply deploy more agents or spend bigger budgets. They¡¯ll be the ones that intentionally use AI to amplify what makes their people irreplaceable¡ªjudgment, creativity, and responsibility.
In 2026, the hard work of AI isn¡¯t just technical. It¡¯s deeply human and full of possibility.
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