Digital workers: The CIO's new IT multiplier
Budgets are under pressure. Output expectations keep climbing. CIOs need a way to increase reliability and pace without burning out teams or ballooning headcount.
Agentic AI makes that possible. Think "digital workers": AI-powered software that can take goals, understand context, make decisions, and execute multi-step workflows end to end-without human micromanagement. That's different from AI assistants, which are great at on-demand tasks like scheduling or drafting reports but aren't built to run autonomous processes. Executives see the shift coming-88% plan to increase AI budgets in the next 12 months due to agentic AI, and 66% say AI agents are already lifting productivity and reducing costs, according to a PwC survey.
What makes a digital worker different?
Digital workers don't wait for a single instruction. They decompose projects into tasks, orchestrate the steps, and complete the work. As Brady Lewis, senior director of AI innovation at Marketri, put it: "The true advantage is that it does not replace the resources and staff within IT organizations, but allows for the automation of predictable, rule-driven tasks… giving IT leaders more time to think."
Like human hires, digital workers need structure and guardrails:
- Unique identity for access and audit.
- Least-privilege permissions mapped to their scope.
- SLAs for speed, accuracy, and quality.
- Continuous monitoring and regular check-ins.
- Defined workflows and clear boundaries for handoffs.
Basic tools complete one-off actions. Digital workers handle entire workflows-context gathering, decisions, execution, and verification-start to finish.
Where digital workers multiply IT output
IT is full of repetitive, data-heavy work. That's why more than half of function-specific agentic AI deployments are in IT-especially DevOps, cybersecurity, and infrastructure-according to an ISG report. "The benefit is not cost-cutting," said Shawn Jahromi of Alpharay Consulting. "The real benefit is operational reliability… consistent execution across shifts, time zones and peak periods."
- IT service desk automation (ticket triage, knowledge retrieval, resolution).
- Software provisioning and access requests.
- Cloud resource management and optimization.
- Security alert triage and remediation.
- Finance and procurement workflows tied to IT operations.
As Roman Rylko, CTO at Pynest, put it: "The increase in IT output is not so much because 'the agent works 24/7,' but rather because bottlenecks are eliminated." His example: recruiters receive ready-made engineer profiles instead of sifting raw resumes-hours saved, less drudgery, and fewer queues.
How to make digital workers effective
Don't treat them like scripts. Treat them like hires. "Companies that successfully leverage autonomous workers will manage them through the same operational processes used when hiring a new member of an IT organization," said Lewis. Used poorly, they slow things down and create rework. Used well, they push throughput and consistency.
- Shift operating models. Build hybrid human-AI teams with clear collaboration patterns and ownership.
- Define workflows and handoffs. Specify where digital workers start, stop, and escalate. "A digital worker can prepare an access decision, but a human approves edge cases," said Jahromi.
- Prioritize AI management skills. Upskill people in AI supervision, automation engineering, and exception handling. Consider focused programs like AI automation certifications to speed readiness.
- Track the right KPIs. Task completion rate, intervention frequency, autonomy level, error rate, time-to-resolution, and customer satisfaction.
Governance, risk and responsibility
Digital workers aren't a fire-and-forget initiative. They require ongoing oversight. "We treat them as tools with a clearly formalized area of responsibility… a list of permissible actions… a log of all operations… and a clear 'red button,'" said Rylko. "We do not transfer to AI agents any authority for which we ourselves are not prepared to be accountable."
Eddy Abou-Nehme of RevNet Ottawa is blunt: "Governance should answer who owns the digital worker, what it is allowed to do, what access it has, how actions are logged, and who is accountable when something goes wrong." A named leader should sign off on scope, monitor performance, and maintain a human escalation path.
- AI oversight and auditability. Keep decision logs and outcomes for post-incident reviews and tuning.
- Security and access controls. Provision like any employee, especially around sensitive data and privileged actions.
- Accuracy, predictability and explainability. Guardrails, validation, and rollback plans reduce surprises.
- Vendor management. Demand transparency on data handling, model behavior, and security posture.
Future outlook
As AI automation and analytics mature, AIOps will become a core layer in IT. Systems will shift from reactive fixes to predictive, self-healing patterns-flagging issues early, triggering mitigations, and reducing downtime.
Org charts will adjust. Some admin-heavy roles will shrink. New roles-AI supervisors, automation architects, prompt and policy engineers-will grow. Leaders will spend more time on architecture, resilience, and innovation, with digital workers carrying the repetitive load.
How to get started
Reduce risk and build trust step by step. "Start by running the digital worker in assist mode, where employees approve actions, and gradually expand autonomy once performance is stable," said Abou-Nehme. Give it an owner, documentation, runbooks, and a clear plan for uncertainty and failure.
- Pick the right first use case. High-volume, rules-based tasks like system management or incident triage.
- Assess integration readiness. Validate APIs, event streams, identity, and logging before rollout.
- Run low-risk pilots. Measure results, refine workflows, then scale.
- Build a digital workforce roadmap. Cover all IT domains and plan capacity, skills, governance, and budgets.
- Upskill early. Train managers and operators to supervise AI effectively-see role-based paths at Complete AI Training.
The goal isn't a handful of clever bots. It's a dependable, accountable digital workforce that removes bottlenecks, improves service levels, and frees your people to solve higher-order problems. Start small, measure hard, expand with confidence.
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