AI is quickly developing, and it’s changing the nature of work at the same speed. “Keeping up” with AI has become less about adding AI to existing jobs and more focused on redesigning how work is completed end‑to‑end.
The Human Edge, ManpowerGroup’s 2026 Global Trends Report, shows that AI is pushing companies toward hybrid super teams. In such environments, work is delivered by blended groups of humans, AI and flexible talent organized around outcomes instead of traditional hierarchies. This shift is already measurable at the enterprise level. At Experis, we see this shift firsthand as clients begin moving from AI experimentation toward enterprise‑wide execution across analytics, product and operations functions.
AI and information processing is projected to transform 86% of businesses by 2030. Leaders responsible for workforce strategy as well as executives accountable for scaling AI shouldn’t treat it as a simple operational upgrade. From our work with clients, it’s clear that an active workforce transformation plan that reshapes roles and accountability is what determines whether AI delivers durable value for an organization.
Workforce Transformation That Leads to an AI-Enabled Enterprise
Workforce transformation for AI begins by recognizing that AI changes workflows first and job titles second. The Human Edge describes a move from informal AI usage, such as ad hoc prompting and isolated copilots, to targeted, workflow‑specific AI that’s directly embedded into redesigned roles.
At Experis, this insight shapes how we approach AI transformation with clients. We consistently see that AI creates value when it’s intentionally worked into workflows and supported by human governance and enablement.
This is also why governed enablement matters early. To help organizations move beyond pilots, Experis brings Sophie AI into engagements as a foundation for responsible AI adoption. Sophie AI is ManpowerGroup’s enterprise platform for AI that’s designed to consistently scale through reusable components and centralized governance. Together, Experis services and Sophie AI create optimal conditions for moving from experimentation to execution.
The practical implications of workflow redesign
At a practical level, this shift shows up in how everyday work is restructured:
- Finance teams move from analysts manually reconciling data to AI performing first‑pass variance analysis. Humans focus on scenario evaluation, risk implications and executive recommendations.
- Customer operations embed AI into service routing and case summarization to allow agents to concentrate on escalations and empathy‑driven interactions.
- Product and analytics teams use AI to draft model documentation and test cases, while humans validate assumptions, assess business impact and decide what actions to take.
This evolution requires leaders to shift framing from “Where can AI save time?” to:
- Where is value created in this workflow?
- Which steps can AI safely handle, and which require human judgment?
- What new routines reinforce quality and accountability?
Breaking roles into high‑impact steps, assigning tasks to human and AI partners, then regrouping them into new roles and job families creates focal points for workforce transformation to become practical. A useful way to make this tangible in data and analytics teams, for example, is to redesign around the data‑to‑decision chain:
- Data acquisition and quality: what enters the system
- Modeling and analytics: how insights are produced
- Interpretation and decisioning: how insights change actions
- Monitoring and improvement: how performance remains reliable over time
Leaders often expect AI to immediately increase productivity. Realistically, though, early phases can temporarily reduce productivity as teams retrofit new tools into outdated processes.
The skills market reflects this tension. One of every 10 job postings in advanced economies now requires at least one new skill, with demand concentrated in professional and technical roles. When roles are redesigned around workflows instead of tasks, AI becomes an integrated contributor to work that compounds value rather than creating friction.
The actionable implications of workforce transformation
Translating workflow redesign into execution requires deliberate choices about where to start and how to sequence change. Workforce transformation becomes actionable when you move from conceptual models to concrete decisions about ownership and outcomes.
A way to start this work is by mapping your top five analytics or AI workflows (for example, forecasting, churn models, demand planning, fraud detection, service routing). Identify the decision moments in each workflow and document what evidence and approvals are required. From there, you can restructure roles around owning outcomes (speed, risk, adoption), rather than solely producing artifacts like dashboards or reports.
Where AI and Humans Connect: Building Cohesive Teams That Deliver Outcomes
As AI capabilities have expanded, a persistent mental model has framed adoption as “humans versus AI.” While displacement concerns are real, the workforce model most likely to succeed isn’t competitive but collaborative.
The Human Edge describes the rise of hybrid super teams: coordinated systems of people, AI agents and flexible talent that are assembled and reconfigured based on what the work requires. In Experis engagements, we see this collaboration improve most when teams intentionally redesign how people and AI interact inside workflows rather than treating AI as a separate overlay.
The following dimensions show how AI participation influences role functions and delivered outcomes.
1. How teams collaborate
AI affects collaboration by changing how work is handed off. When AI agents can produce first‑pass analyses or predictions, human collaboration shifts upstream. Conversations move toward problem framing, assumptions, risk checks and defining what success looks like. Meetings become less about reporting progress and more about approving tradeoffs and decisions.
Analysts test and refine prompts, engineers validate data lineage and domain experts assess whether outputs are reasonable in context. AI accelerates the work, but the emphasis Experis places on verification and decision ownership reinforces accountability that’s human‑owned as AI participation expands.
2. Which skills are essential and difficult to automate
The ManpowerGroup report found that one‑third of employers say skills such as ethical judgment, customer service and team management are the hardest to automate. This finding aligns with operational reality. AI can generate confident output even when inaccurate or misleading, which makes human capability in verification, judgment and accountability essential as AI takes on larger portions of workflows.
3. How roles mature in hybrid teams
In hybrid environments, roles increasingly evolve toward:
- Orchestration: managing AI contributions and routing work
- Verification: validation, testing and auditability
- Translation: converting insights into actions the business will adopt
This evolution is a direct response to AI’s growing participation in workflows. Value is limited when AI is narrowly introduced. Experis’ workforce‑led approach is designed to support this shift so that AI agents will fully participate in every workflow and operate alongside people and other agents.
A consideration for leaders: if analysts spend half their day cleaning data and formatting decks, and AI is introduced only as a writing assistant, value will be limited. When AI participates directly in the workflow by classifying data issues, summarizing anomalies and proposing next steps, people move toward higher‑value judgment and decision work.
This is how your business makes the transition from AI as a helper to AI and humans as a coordinated system.
AI Literacy: A Practical Foundation for Data Analytics and AI Teams
Today, AI literacy is often treated as a general awareness concept. In practice, it’s becoming a set of concrete, job‑relevant capabilities.
This matters because the workforce is unevenly prepared. The Human Edge notes that less than half of global workers received skills training in the past six months. At the same time, AI literacy is a democratizing skill. Training is widely accessible and does not require advanced degrees. Recognizing this opportunity, Experis has developed a solution for this skills divide by building role‑based learning pathways.
A measurable standard for AI literacy
Across data analytics and AI functions, AI literacy should include:
- Prompting and iteration that involves refining and constraining outputs
- Verification habits such as cross‑checking sources, testing for hallucinations and validating numbers
- Data‑handling discipline to learn data permissions and leakage risk
- Bias and risk awareness to recognize when outputs may discriminate or mislead
- Workflow integration that uses AI inside defined processes, not as ad hoc shortcuts
AI Implementation Planning: Scaling Safely With Guardrails
As organizations move from literacy to execution, many stall at pilot stages because they skip AI implementation planning. This discipline involves turning experiments into consistent, governed operations.
One reason scaling is difficult is that leaders cite growing concerns around data privacy, workforce impact, skills gaps and cost. That is why scaling AI requires guardrails that function operationally, not just policy statements.
Guardrails that enable sustainable adoption
A successful approach typically includes clearly defined governance structures and ongoing oversight mechanisms. Without these, AI adoption becomes fragmented and risky. Key elements comprise:
- Governance: clear ownership, approved use cases and escalation paths
- Risk management: testing, monitoring, bias checks and incident response
- Data controls: privacy, access management, retention and audit trails
- Change management: training, adoption support and feedback loops
Supporting AI at scale with Sophie AI
Establishing guardrails is only effective if they’re consistently applied as AI use expands. This consistency is difficult to maintain without a shared platform foundation. It’s why the Sophie AI ecosystem is specifically positioned to support AI adoption with governance rather than tool sprawl.
Sophie AI Enterprise provides a secure foundation with reusable components and built‑in oversight. It combines workforce data, machine learning and expert insight to support planning and productivity. Sophie AI uses a multi‑agent approach across planning, querying, validation and composition. This platform offers pre‑vetted workflows, model oversight routines and reusable governance components. This way, teams can move from isolated experiments to consistent, responsible AI use.
Scaled AI implementation planning and execution require a governed process where experimentation can mature into production without sacrificing control. To start with a focused approach and maximize the benefits of Sophie AI’s capabilities:
- Choose three to five workflows to scale (not dozens of disconnected use cases).
- Define guardrails using NIST‑style functions (govern, map, measure, manage).
- Create routines with required validation steps, sign‑offs and monitoring metrics. Experis uses these frameworks to help clients build responsible AI operating models.
- Establish enablement through training, prompt libraries and internal champions.
- Use a platform approach (like Sophie) so each new workflow inherits governance by design.
Workforce Transformation That's a Lasting Advantage: What Can You Do Now?
Winning workforce transformation is about building roles and routines together, not just deploying more tools. Organizations that succeed redesign work around workflows, clarify accountability and create guardrails that reinforce trust. They use AI to elevate skills and reshape collaboration.
The most effective next step is practical: identify where value is created, redesign how work flows and hold AI and humans accountable together. That’s how AI moves from experimentation to enterprise advantage.
For leaders navigating this shift, partnering with an organization that understands technology and workforce design can accelerate progress. Experis helps organizations translate AI ambition into workforce strategies that are actionable, governed and aligned to real business outcomes.
Experis brings together data, analytics and AI engineering expertise with workforce transformation specialists to help organizations implement governed AI and scale safely. It’s the direct, hands‑on support you need to redesign workflows and roles for the next phase of AI adoption. See your next steps for productive AI adoption, and get the implementation support and guidance you need from Experis.
