Agentic artificial intelligence (AI) is more than a buzzword—it is swiftly becoming a transformative force in today’s workplace, redefining productivity, enabling innovation, and reshaping organizational culture. As companies grapple with information overload and evolving pressure to innovate, agentic AI, in the form of decision-making AI agents, is turning theoretical promise into practical progress. Microsoft’s “Agents of Change” report, echoed by the latest findings of the Work Trend Index, underscores a mounting consensus: AI isn’t just another tool—it’s a new foundation for the way work gets done. This feature explores four critical ways agentic AI is changing work as we know it, highlighting real-world examples, cross-industry analysis, and both the rewards and risks of this emerging technology.
The convergence of digital transformation and AI maturation has paved the way for agents—AI systems with the autonomy to act on users’ behalf within set parameters. Data from the UK highlights this urgency: over 60% of business leaders and more than half of employees feel their workload exceeds a single person’s capacity. Productivity stagnation, overburdened staff, and fragmented workstreams have become persistent challenges, leaving organizations searching for relief valves and new value streams.
AI agents are not simply automating tasks—they are actively reshaping business processes, making workflows smarter, more efficient, and, critically, more adaptable. Unlike traditional AI solutions, agentic AI integrates into daily activities, learns from user preferences, manages multistep tasks, and adapts contextually. A new class of organizations—termed “Frontier Firms”—are rebuilding their operations around AI, seeing step-changes in productivity and innovation.
Kantar offers a prime example: the company is leveraging agentic AI to break down information barriers and bring three decades of proprietary assets right into employees’ workflows. Staff can pull relevant data directly into applications like PowerPoint, eliminating time wasted on document hunts. What’s notable about this deployment is its informality and speed—Kantar’s business development agent was built collaboratively in Teams chat, rolled out without prolonged planning, and is now actively used by hundreds.
The technical underpinning here is significant. Unlike static bots, agentic AI agents can search, retrieve, summarize, and integrate diverse data sets within context, then translate insights into immediate, actionable next steps. This capability is accelerating decision cycles across industries, allowing knowledge workers to focus on value-adding tasks instead of administrative “data wrangling.” Not only does this increase productivity, but it also empowers more people to leverage the organization’s intellectual property directly and efficiently.
Kantar’s approach provides a masterclass in this new paradigm. Every employee, from entry-level staff to the CEO, is encouraged to use and improve AI agents. The company boasts over 15,000 Copilot users, with a remarkable 81% engagement rate—a figure that stands out even when compared industry-wide. Rather than piloting tools with small, siloed test groups, Kantar builds, tests, and scales iteratively, with users involved from the outset.
This grassroots experimentation flips the traditional IT script. Instead of central teams specifying requirements, every employee becomes a potential innovator, able to build and refine agents suited to their daily workflows. This approach not only accelerates adoption but also surfaces unanticipated use cases, expanding the strategic value of AI across the entire organization.
Agentic AI offers an antidote. At Arup, engineers have started using agents to strip away routine administrative burdens, freeing valuable time for creative and client-focused efforts. This isn’t just about cutting busy work; it’s a strategic move to allow staff—especially highly-trained professionals—to operate at the peak of their expertise. “We see agents as a way of reducing repetitive everyday tasks so our people can best fulfil their potential,” says Tom Heath, Arup’s Global Director of Data Science and AI.
The potential is especially pronounced among new “AI-native” graduates, who enter the workforce expecting intelligent, supportive digital tools. AI agents help bridge the expectation gap, aligning daily tasks with their skills and ambitions. But the benefits are not restricted to the young: workers of all ages report greater job satisfaction and purpose when freed from low-value drudgery.
Accessibility is another area where agentic AI shines. At Arup, Copilot Agents have been highlighted for supporting neurodivergent workers, helping them navigate dense information landscapes and prioritize focus areas. By enabling information to surface contextually, agentic AI reduces cognitive overhead, making work more inclusive.
Arup, for instance, is developing agents capable of handling sophisticated engineering design workflows, bridging domain expertise with the power of automation. The potential upside is immense, pointing to fundamentally new ways of organizing expertise, knowledge management, and project delivery. In construction, healthcare, legal, and other specialized fields, autonomous agents could enable domain experts to solve previously intractable problems or design innovative solutions at unprecedented speed.
However, as agentic AI grows in power, so do the risks. Security and compliance must evolve in lockstep. Arup has responded by creating “centre line guidance”—principles for navigating ethical, secure AI usage, distinct from traditional policy documents. This empowers staff to explore and innovate but always within clear, safe boundaries. The value of such institutional guidance cannot be overstated; as AI systems act with more autonomy, the human oversight layer must become more sophisticated, too.
Key capabilities include:
But the risks—security, compliance, fragmented user experience, and skills atrophy—loom large. Organizations that ignore these vulnerabilities risk immediate setbacks and long-term trust erosion. Successful leaders will strike a dynamic balance: nurturing a culture that celebrates experimentation, while wrapping AI initiatives in strong, evolving frameworks for ethics, security, and inclusion.
Practical recommendations for organizations accelerating their agentic AI journey:
As agentic AI becomes mainstream, its impact will be determined by the alliances between humans and their digital co-workers. Far from replacing the workforce, the most effective agents will empower people—making work more impactful, meaningful, and inclusive for all.
Source: The Guardian Four ways agentic AI is transforming work today
The Rise of Agentic AI: Why Now?
The convergence of digital transformation and AI maturation has paved the way for agents—AI systems with the autonomy to act on users’ behalf within set parameters. Data from the UK highlights this urgency: over 60% of business leaders and more than half of employees feel their workload exceeds a single person’s capacity. Productivity stagnation, overburdened staff, and fragmented workstreams have become persistent challenges, leaving organizations searching for relief valves and new value streams.AI agents are not simply automating tasks—they are actively reshaping business processes, making workflows smarter, more efficient, and, critically, more adaptable. Unlike traditional AI solutions, agentic AI integrates into daily activities, learns from user preferences, manages multistep tasks, and adapts contextually. A new class of organizations—termed “Frontier Firms”—are rebuilding their operations around AI, seeing step-changes in productivity and innovation.
1. Instant Data Access: Moving from Data Deluge to Insight
Modern enterprises are drowning in data, but the vast volume often becomes more of a hindrance than an advantage. The promise of digital transformation hinged on harnessing data for better decisions, but siloed, unstructured repositories slow progress. With agentic AI, especially as implemented by pioneers like Kantar and Arup, this picture is changing rapidly.Kantar offers a prime example: the company is leveraging agentic AI to break down information barriers and bring three decades of proprietary assets right into employees’ workflows. Staff can pull relevant data directly into applications like PowerPoint, eliminating time wasted on document hunts. What’s notable about this deployment is its informality and speed—Kantar’s business development agent was built collaboratively in Teams chat, rolled out without prolonged planning, and is now actively used by hundreds.
The technical underpinning here is significant. Unlike static bots, agentic AI agents can search, retrieve, summarize, and integrate diverse data sets within context, then translate insights into immediate, actionable next steps. This capability is accelerating decision cycles across industries, allowing knowledge workers to focus on value-adding tasks instead of administrative “data wrangling.” Not only does this increase productivity, but it also empowers more people to leverage the organization’s intellectual property directly and efficiently.
Critical Perspective: Strengths and Caveats
This mode of deployment demonstrates the democratizing potential of agentic AI: any team can construct and iterate their agents, tailoring solutions to unique needs. However, organizations must be mindful about data governance and quality. Quick, collaborative builds are powerful, but the risk of shadow IT—systems created without formal oversight—can spiral, potentially opening doors for compliance issues or data leaks if not managed carefully. While early results are promising, mature oversight processes are essential to safe scaling.2. Empowerment Through Experimentation: AI as a Cultural Catalyst
The real revolution with agentic AI is not solely technical but cultural. Success hinges on moving past traditional, hierarchical IT-driven rollouts to a democratized innovation environment, where employees at every level are empowered to experiment, iterate, and share best practices.Kantar’s approach provides a masterclass in this new paradigm. Every employee, from entry-level staff to the CEO, is encouraged to use and improve AI agents. The company boasts over 15,000 Copilot users, with a remarkable 81% engagement rate—a figure that stands out even when compared industry-wide. Rather than piloting tools with small, siloed test groups, Kantar builds, tests, and scales iteratively, with users involved from the outset.
This grassroots experimentation flips the traditional IT script. Instead of central teams specifying requirements, every employee becomes a potential innovator, able to build and refine agents suited to their daily workflows. This approach not only accelerates adoption but also surfaces unanticipated use cases, expanding the strategic value of AI across the entire organization.
Risk Analysis: Experimentation vs. Control
While this open culture is crucial for maximizing innovation, it can also introduce significant risks. Without appropriate guardrails, unsupervised experimentation can lead to fragmented user experiences, security vulnerabilities, or compliance lapses. Kantar’s success is underpinned by strong executive sponsorship and a culture of sharing, but not all organizations will find it natural to strike this balance. Experts advise clear, evolving usage policies, training schemes, and a robust feedback loop to ensure innovation does not outpace safety.3. Making Work More Human: Productivity, Satisfaction, Accessibility
Agentic AI’s biggest promise may be in restoring humanity to work. For years, employees have grappled with overwhelming digital demands and fragmented workloads. Microsoft’s Work Trend Index reveals nearly half of employees and more than half of leaders perceive their work as chaotic and fragmented—a sentiment exacerbated by complex digital tools that often add friction rather than removing it.Agentic AI offers an antidote. At Arup, engineers have started using agents to strip away routine administrative burdens, freeing valuable time for creative and client-focused efforts. This isn’t just about cutting busy work; it’s a strategic move to allow staff—especially highly-trained professionals—to operate at the peak of their expertise. “We see agents as a way of reducing repetitive everyday tasks so our people can best fulfil their potential,” says Tom Heath, Arup’s Global Director of Data Science and AI.
The potential is especially pronounced among new “AI-native” graduates, who enter the workforce expecting intelligent, supportive digital tools. AI agents help bridge the expectation gap, aligning daily tasks with their skills and ambitions. But the benefits are not restricted to the young: workers of all ages report greater job satisfaction and purpose when freed from low-value drudgery.
Accessibility is another area where agentic AI shines. At Arup, Copilot Agents have been highlighted for supporting neurodivergent workers, helping them navigate dense information landscapes and prioritize focus areas. By enabling information to surface contextually, agentic AI reduces cognitive overhead, making work more inclusive.
Balanced View: Humanizing vs. Mechanizing Work
Critics of AI warn about potential depersonalization and job displacement, but agentic AI, as practiced by leaders like Arup and Kantar, appears to run counter to this narrative. The best deployments emphasize augmentation, not automation for its own sake. Still, vigilance is required; organizations must ensure that humans remain in the driver’s seat and that AI tools do not inadvertently introduce bias or erode skills over time.4. Securing a Strategic Future: From Task-Based Automation to Autonomy
While today’s agentic AI deployments are typically retrieval- or workflow-based, the technology road map is veering towards far greater autonomy. Leading organizations are already planning for a future in which agents carry out complex, multistep processes—often making context-informed decisions on the fly.Arup, for instance, is developing agents capable of handling sophisticated engineering design workflows, bridging domain expertise with the power of automation. The potential upside is immense, pointing to fundamentally new ways of organizing expertise, knowledge management, and project delivery. In construction, healthcare, legal, and other specialized fields, autonomous agents could enable domain experts to solve previously intractable problems or design innovative solutions at unprecedented speed.
However, as agentic AI grows in power, so do the risks. Security and compliance must evolve in lockstep. Arup has responded by creating “centre line guidance”—principles for navigating ethical, secure AI usage, distinct from traditional policy documents. This empowers staff to explore and innovate but always within clear, safe boundaries. The value of such institutional guidance cannot be overstated; as AI systems act with more autonomy, the human oversight layer must become more sophisticated, too.
Security Risks and Mitigation
AI security threats are not hypothetical. As agents act on behalf of users and have access deep within business processes, the scope for data breaches, accidental leaks, or even malicious manipulation expands. Research indicates that approximately 30% of AI-related security incidents in 2024 involved agent-based automation being exploited through social engineering or misconfiguration. The lesson for all organizations is clear: agentic AI promises vast rewards, but only those who build in robust governance and security from the outset will realize those advantages safely.Accelerating the Agentic AI Journey: Practical Steps for Leaders
With agentic AI adoption picking up pace, any organization hoping to stay ahead needs to act strategically—balancing speed, experimentation, training, and governance. Marked differences are already emerging: according to Microsoft’s Work Trend Index, 71% of employees at leading “Frontier Firms” feel their organization is thriving, compared with just 37% of employees globally. This gap underscores the value of early, thoughtful AI integration.Starting Small, Scaling Fast
The most effective approach is targeted: start with limited, high-impact use cases. Find pain points that can be alleviated quickly and visibly through AI agents, such as document retrieval, report generation, or workflow automation. Demonstrate value, then iterate rapidly—scaling up successful prototypes, learning from feedback, and inviting cross-team input.Invest in People as Much as Technology
Agentic AI is only as effective as the workforce that adopts and shapes it. Successful companies prioritize training, establishing learning frameworks that cover both technical usage and the ethical, secure management of AI. Employees should feel empowered and accountable; agent bosses will need skills in design, delegation, and oversight, not just passive tool usage.Transform Knowledge Management
The next leap forward will come from treating knowledge as a living asset. AI agents thrive when they have access to curated, structured, and well-managed data. This requires shifting knowledge management from IT-managed archives to decentralized, federated systems, where data is discoverable, securely accessible, and constantly updated. Organizations should explore solutions like Microsoft Copilot Studio Agent Builder, which enable custom agent creation using natural language and robust prebuilt connectors, integrating seamlessly with business systems.Real-World Tools: Microsoft Copilot Studio Agent Builder
A pivotal enabler for these advancements is the emergence of platforms like Microsoft Copilot Studio Agent Builder. This suite allows organizations—with or without deep technical resources—to build AI agents that perform complex, multi-step tasks and integrate natively within business apps.Key capabilities include:
- Natural language agent creation: No-code interfaces allow business users to specify agent logic conversationally.
- Integration with business systems: Prebuilt connectors facilitate seamless data integration, spanning cloud storage, CRM, ERP, and more.
- Customizable workflows: Agents can be tailored to individual or team needs and iterated upon as requirements evolve.
- Scalable deployment: Solutions can be embedded directly within familiar platforms such as Teams and Office, accelerating user adoption.
The Road Ahead: Opportunities, Obstacles, and Responsible Innovation
Agentic AI is poised to radically change how work gets done—but the transition is not without friction. The primary benefits—productivity, empowerment, human-centered design, and strategic innovation—are achievable today, as demonstrated by firms like Kantar and Arup. These organizations’ success stories show that fast-moving, democratized experimentation, backed by strong governance and executive sponsorship, can generate real, repeatable value.But the risks—security, compliance, fragmented user experience, and skills atrophy—loom large. Organizations that ignore these vulnerabilities risk immediate setbacks and long-term trust erosion. Successful leaders will strike a dynamic balance: nurturing a culture that celebrates experimentation, while wrapping AI initiatives in strong, evolving frameworks for ethics, security, and inclusion.
Practical recommendations for organizations accelerating their agentic AI journey:
- Define clear strategic outcomes before embarking on deployment—know what success looks like.
- Start narrowly, scale wisely, and iterate based on measurable impacts.
- Empower employees as active designers and users of AI agents, but put strong feedback and training systems in place.
- Invest in data and knowledge management so agents can unlock meaningful insight.
- Prioritize security, compliance, and ethical guardrails as organizational AI matures and increases in autonomy.
As agentic AI becomes mainstream, its impact will be determined by the alliances between humans and their digital co-workers. Far from replacing the workforce, the most effective agents will empower people—making work more impactful, meaningful, and inclusive for all.
Source: The Guardian Four ways agentic AI is transforming work today
