AI in 2026: from experimentation to enterprise expectation
From invisible infrastructure and enterprise deployment to responsible AI and workforce impact, these perspectives outline how AI will fundamentally shape how organizations operate, build trust, and deliver value in the year ahead.
AI becomes the invisible infrastructure
Rachel Enstrom, our Sr. Director of Product, Marketing & Tech, notes that by 2026, AI will no longer act as a “sidekick” to staffing and talent operations, it will become the invisible infrastructure behind day-to-day execution.
AI agents will increasingly:
- Pre-qualify candidates
- Validate skills and identity
- Predict placement success and attrition
- Automate onboarding, compliance, payroll exceptions, and redeployment
In this next phase, the differentiator won’t be who uses AI, it will be who governs it well. Ethical use, fraud prevention, explainable decisions, and human-in-the-loop design will matter more than raw capability.
From pilots to enterprise scale
According to Pallayya Batchu, our Vice President of Global Technology Services, 2026 marks a decisive shift from experimentation to execution.
Enterprises will move beyond isolated AI pilots and proofs of concept into production-grade, scaled deployments. The focus will shift from “Can we build it?” to “Can we operationalize it reliably, securely, and at scale?”
Success will increasingly depend on:
- Data readiness
- Integration into core business processes
- Clear ownership across IT and business teams
AI will no longer be treated as a side initiative. It will become a foundational enterprise capability embedded across functions.
From AI tool proliferation to AI portfolio governance
Pallayya also highlights that as AI tools, platforms, copilots, and point solutions proliferate, many organizations are already experiencing fragmentation, redundancy, and rising risk.
By 2026, enterprises will recognize that unchecked AI sprawl:
- Erodes value
- Increases security and compliance risk
- Complicates data integrity and oversight
In response, organizations will establish AI portfolio governance, rationalizing tools, standardizing platforms, and defining clear guardrails. The challenge will be enabling governance without stifling innovation, striking a balance between centralized control and decentralized experimentation.
Responsible AI becomes a business imperative
Shannon Drost, our VP of Technology Solutions, emphasizes that AI governance and responsible AI will become critical priorities in 2026, not optional considerations.
Why AI governance matters:
- Risk mitigation: prevents biased outcomes, data leakage, and opaque decisions
- Regulatory compliance: supports transparency and accountability amid evolving regulations
- Trust and transparency: builds confidence with customers and stakeholders
- Operational integrity: limits the impact of AI errors that can scale quickly
Responsible AI goes further, ensuring systems align with ethical standards and societal values.
Why responsible AI matters:
- Fairness and bias mitigation through audits and checks
- Privacy and security via privacy-first architectures
- Transparency and explainability to support compliance and trust
- Brand reputation, as ethical technology becomes a competitive differentiator
AI redefines work and operating models
Pallayya shares that as AI matures, enterprises will rethink work design itself. The mindset will shift from “How can AI help people?” to “How should people operate in systems where AI does the heavy lifting?”
This evolution will lead to:
- Redesigned roles
- Leaner teams
- New skill expectations focused on judgment, orchestration, and exception handling
AI will increasingly act as the primary executor, while humans evolve into supervisors, strategists, and decision-makers.
AI accelerates developer productivity
Shannon explains that AI is dramatically reshaping the software development lifecycle.
By enabling rapid prototyping, code generation, testing, and refactoring, AI is accelerating delivery timelines at unprecedented speed. What once took weeks can now take days or hours.
As a result, organizations will need to adapt:
- Engineering governance
- Quality controls
- Security practices
Those that fail to evolve alongside AI-driven productivity gains will struggle to scale and compete.
Experience, balance, and intentional design
Rachel notes that as AI reshapes operations, expectations around experience will rise. Consultants increasingly expect consumer-grade experiences at enterprise scale, comparable to Amazon’s speed, Spotify’s personalization, and Apple’s simplicity and trust.
This means:
- Frictionless onboarding
- Proactive communication
- Transparent pay, timing, and expectations
- Clear paths to redeployment and growth
At the same time, work-life balance will become an operational metric, not a perk. Burnout, flexibility, and mental load will directly impact fill rates, assignment completion, client satisfaction, and employer brand.
In 2026, the staffing firms that grow the fastest won’t look the most innovative, they’ll look the most intentional.
Leaders will focus on:
- Where automation helps (and where it shouldn’t)
- How talent is treated between placements
- How technology truly changes day-to-day work
- How trust is built at scale
In 2026, AI will be expected, not impressive. The organizations that succeed won’t be those chasing the newest tools, but those applying AI thoughtfully, responsibly, and at scale.
From enterprise deployment and governance to experience design and workforce impact, AI’s next chapter is about intention. Leaders who balance innovation with trust, automation with humanity, and speed with responsibility will be best positioned to unlock lasting value in the year ahead.
Our teams partner with organizations to design, govern, and scale AI solutions that are secure, responsible, and built for long-term value. Let’s talk about what intentional AI looks like for your organization.