Agentic AI in the digital workplace: From recommendations to autonomous action

You've probably seen "agentic AI" everywhere lately. While organizations work to extract value from generative AI, this next evolution of technology is here and demanding attention. When it comes to the digital workplace, how should you prepare? 

Listeners of The Digital Workplace Deep Dive podcast got an answer to this question in episode 57, “Why agentic AI is the colleague you've been waiting for.” Host Weston Morris spoke with Aron Meyer, solution manager for Digital Workplace Solutions at Unisys, to discuss what agentic AI means for workplace technology and how organizations can start implementing it. 

What makes AI truly ‘agentic’ 

Traditional AI systems in the workplace provide recommendations that humans act on. What separates these helpful systems from truly agentic AI is an autonomous action. 

Agentic AI systems have three defining characteristics: 

• Analysis: They evaluate complex, variable workplace situations in context 
• Decision-making: They determine the best course of action based on objectives and constraints 
• Independent action: They execute without requiring human approval at every step 

"This is a key difference and why agentic is critical, next-level capability," Aron explained. These systems learn from interactions and improve automatically over time, adjusting their approach in real time as conditions change. 

To illustrate the difference, Aron offered a comparison most of us encounter daily. "When you use a navigation app in your car, that route planning is using AI to determine the best route," he explained. "It may offer suggestions when those conditions change, but it's not actually taking any action for you." 

An autonomous vehicle operates differently. You provide one objective: "Take me to the grocery store." From there, the AI handles everything, route planning, traffic adjustments, lane changes, and obstacle avoidance, while continuously adapting to changing conditions. 

To put agentic AI in context, consider the progression: Standard AI analyzes data and surfaces insights. Generative AI creates content based on patterns. Agentic AI acts autonomously to achieve defined objectives. And choreography, the next evolution, coordinates multiple specialized agents, allowing them to work together towards complex outcomes. 

Real applications transforming digital workplace management 

Autonomous PC life cycle operations 

Consider how a pharmaceutical company might manage PC life cycles, ordering, shipping, staging, and recycling devices across the organization. Traditional AI could analyze usage data, performance metrics, and OEM catalogs to generate purchase recommendations. A human purchaser would then review these insights and execute orders. 

An agentic system starts with clear workplace objectives: maximize employee productivity by ensuring appropriate devices are available, minimize costs while maintaining quality, reduce unused inventory, and prioritize critical roles like research scientists working on time-sensitive projects. 

Given these objectives, the system makes autonomous ordering decisions. It might recognize that shifting existing inventory between locations is more cost-effective than purchasing new devices, while also factoring in risk probabilities for future fulfillment needs at each location. It balances immediate requirements against broader implications, operating the way a human would think through the problem, but continuously and at scale. 

Employee onboarding automation and the power of choreography  

Enterprise onboarding demonstrates the power of choreography in digital workplace operations. Imagine an HR bot managing personnel data and training schedules, a benefits bot handling enrollment, an IT bot provisioning devices, and a facilities bot coordinating badge access and office assignments. 

A master agent coordinates these specialized bots, each operating autonomously while communicating with others to achieve successful onboarding. Aron compares it to an orchestra: "Much like an orchestra, where the conductor selects a piece from a vast music catalogue, chooses the appropriate instrument sections precisely when they're needed, and then maintains tempo to deliver music with all those components synchronized in harmony." 

Rather than a single agent working independently, choreography coordinates multiple specialized agents – each operating autonomously while reacting to messages and events from one another – to achieve complex outcomes no single agent could accomplish alone. 

But with this power comes responsibility. The same autonomous capabilities that make agentic AI valuable for digital workplaces also create new security risks. Cybersecurity organizations report threat actors using agentic systems to improve phishing attacks, learning from failed attempts and refining their approach to create increasingly sophisticated threats. This underscores why strong governance and ethical frameworks must be built into your agentic AI strategy from day one. 

Getting started with agentic AI in your digital workplace 

Aron emphasized that successful agentic AI deployment in digital workplace environments requires building proper foundations. Organizations should think bottom-up: develop individual agents for specific tasks first, then combine them into more complex systems as capabilities mature. 

Establish quality data foundations: Agentic systems require accurate, current information to make sound decisions. Organizations must continuously curate knowledge content and data sources that feed their agents. 

Implement robust governance frameworks: When systems make autonomous decisions that affect digital workplace operations and employee experiences, trust becomes critical. Organizations need strong governance practices and data privacy processes to ensure these systems operate within appropriate boundaries. 

Build your automation catalog: Start by creating individual agentic capabilities for discrete tasks. As this catalog expands, you can identify opportunities to combine agents into choreographies. Begin with two or three agents working together, learn from that experience, then scale progressively. 

What comes next 

Weston asked, "Is this the end game?"

"It's not likely the end game," Aron replied. "Here at Unisys, we're already working on the next generation of our Service Experience Accelerator to perform choreography." 

Unisys Service Experience Accelerator is an AI-driven technology platform that transforms digital workplace service operations by combining automation, real-time insights, and multilingual support to accelerate issue resolution and secure service delivery. The platform currently powers conversational support, pre-built automation for routine actions, agent copilot capabilities, and automated compliance features. 

The next generation of this technology is being designed with choreography capability, enabling decentralized, specialized agents to work together seamlessly in digital workplace environments – each contributing their expertise while the system ensures coordination at scale. 

For digital workplaces ready to rethink how work gets done, the opportunity is substantial. 

Contact Unisys today to learn how we can help you implement agentic AI solutions and modernize your digital workplace.