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Rethinking AI in the workplace
Enterprise leaders must think—and act—fast when making AI-related decisions in today’s quickly evolving landscape. Recently, many of those decisions have begun to involve the role of AI in the workplace. One application in particular has captured the interest—and fueled the excitement—of enterprises everywhere: the AI agent. Capable of augmenting human skills and enhancing employee productivity, AI agents are quickly redefining the nature of work itself. However, developing workplace agents requires a new approach to AI. As demand for agentic AI grows, more organizations are realizing they need an enterprise AI platform to effectively build and deploy domain-specific agents using their own enterprise data. But that change is coming—and it’s coming soon.
Just how much—and how quickly—will the agent revolution impact AI in the workplace? Gartner predicts that 33% of enterprise software applications will include agentic AI by 2028. CIOs are even more bullish on the matter. More than half (53%) see AI agents as a core component of business operations within the next two years, according to a recent CIO article. Their enthusiasm is well founded: agentic AI offers enterprises a wealth of operational benefits. And if recent history is any indication, those that adopt agentic AI in the workplace early on will have a considerable advantage over their late-to-the-game peers.
Benefits of deploying agentic AI in the workplace
Much of what attracts enterprise leaders to apply agentic AI in the workplace is its ability to proactively optimize core functions. At its core, agentic AI is a type of AI that’s capable of making decisions, taking actions and learning autonomously for the purpose of a specific function. Because of its ability to quickly draw logical conclusions from massive volumes of enterprise data, it is increasingly being used to power domain-specific AI agents and employee copilots.
“You can define agentic AI with one word: proactiveness,” said Enver Cetin, an expert on AI, in a recent Harvard Business Review interview. “The agentic AI system understands what the goal or vision of the user is and the context to the problem they are trying to solve.”
In an enterprise setting, that “problem” is often an inefficient and/or inconsistent process or task. By deploying agentic AI in the workplace, enterprises cannot only solve these key issues; they can optimize their supporting functions as well. Examples include:
Augmenting employee skills
Deployed as an employee copilot or enterprise agent, agentic AI can enhance employee performance with instant access to relevant, domain-specific knowledge and workflow automation across multiple steps. For example, a sales copilot can feed sellers actionable cues during live sales presentations while automating backend tasks, such as data capture and CRM entry, follow-up actions and more.
Driving consistency and quality of work
AI agents don’t just help individual employees in their day-to-day functions; they also help drive consistency and quality of work enterprise wide. For example, a recruiting copilot can ensure that all hiring decision makers—from talent scouts to hiring managers—are aligned during the talent acquisition process, with automated interview summaries, AI-generated insights and other shared resources.
Fuel process efficiency
One of the biggest benefits of deploying AI in the workplace is its ability to improve process efficiency across the enterprise. In addition to automating manual human tasks, as mentioned earlier, AI agents can work “behind the scenes” to improve data, enabling AI systems to work more effectively and efficiently..
Challenges implementing agentic AI
Despite its many benefits, there are several challenges to deploying agentic AI in the workplace that impact its speed of adoption. In fact, Forrester predicts that three out of for organizations that attempt to build AI agents in house will fail. The reasons include:
Difficulty building AI agents from scratch
The same article that found CIOs bullish on agentic AI development noted that IT practitioners are notably more cautious about the speed of development. Their reservations are justified: developing, testing and deploying custom, domain-specific AI agents requires massive amounts of time and effort when performed in house.
Data quality and access challenges
A big part of what makes building AI agents in house so difficult is data availability. To work effectively, AI agents need access to huge sums of high-quality, domain-specific data. Much of this data, however, is often siloed or in a low-quality, unstructured format that AI engines can’t access or use in its current state.
Architectural challenges
Quality and availability aren’t the only data-related concerns when developing agentic AI in the workplace. Data architecture poses another challenge. Many enterprises operate on a rigid, monolithic architecture that limits how data may be composed or used. (A composable and reusable architecture solves this by transforming data into reconfigurable “modules.”)
User adoption challenges
Enterprises looking to deploy agentic AI in the workplace must also address the elephant in the room: employee resistance to change. To drive user adoption, AI agents must be intuitive and easy to use, accompanied by training and education programs and driven by executive-level advocacy.
To overcome these challenges, businesses will need to adopt an AI-native platform approach that connects and converts the whole of enterprise data into usable knowledge that’s ready for agentic AI development. By applying layers to this underlying data fabric, enterprises can then address specific challenges related to data, AI modeling and even agent development itself.
Why enterprises need an Agent Layer
A core component of this multilayered, AI-native platform approach is the Agent Layer. This layer empowers functional users to build and manage intelligent and personalized AI agents for specific business needs. It allows users to deploy pre-built AI agents across core functions such as customer service, marketing, HR, sales and more. This gives enterprises the flexibility to custom-make function-specific agents with accelerated speed-to-value. (Read more about the layers involved in developing AI in the workplace.)
Agentic AI use cases
There are many applications of agentic AI in the workplace that are already improving core functions. Some, including sales and recruiting AI copilots, were alluded to earlier. Pioneering developers are even offering prebuilt, use case-specific agents to help enterprises accelerate time-to-value and unlock the potential for scaling and build-your-own agents. Let’s explore these and other agentic AI use cases more in depth here:
Agentic AI in customer service
Customer service is a popular, and relatively easy, gateway for enterprises to begin implementing agentic AI in the workplace. By integrating its customer data (in its various, multimodal sources) within a multimodal AI and data platform, enterprises can easily build their own customer service AI applications, agents or copilots. To save time, advanced platforms, like Uniphore's Platform, include pre-built AI agents for several core functions. U-Assist, for example, is a real-time agent assistance program that acts as an in-call copilot for customer service representatives.
Agentic AI in sales
Sure, sales copilots can augment seller skills and automate backend tasks, as mentioned before. But there’s more to it than that. AI-powered sales assistants like Q for Sales give sellers a significant advantage in remote engagements by turning buyer language, expression and behavior cues into actionable insights. Armed with this additional source of intelligence, sellers can adjust their tactics on the fly, keeping buyers engaged and deals moving forward.
Agentic AI in HR
We already touched on how recruiting copilots can keep hiring decision makers aligned; but just how do AI recruiting agents work? Q for Recruiting, Uniphore’s AI-powered recruiting copilot for faster, smarter hiring acts as an in-screen talent acquisition assistant. Using AI, the HR talent agent can summarize job descriptions, generate relevant interview questions and even guide recruiters through remote interviews with real-time prompts and automated notetaking.
The future of agentic AI in the workplace
There’s no doubt: agentic AI is the future of AI in the workplace. And the future is happening now. According to Gartner, AI agents are on track to reach the top of the Gartner Hype Cycle this year. Enterprises everywhere are realizing the full potential of AI as a human assistant, empowering core business functions and enabling new ways of work. As more organizations embrace AI-native platforms, the number and type of AI agents they develop will explode. And, as is so often the case, the leaders in the agentic revolution will soon outpace the laggards.
Agentic AI is transforming the workplace at breakneck speed. In the rush to join the race, enterprises must consider how their AI platforms can support agent development. By adopting an AI-native platform with a built-in Agent Layer, businesses can accelerate the creation and deployment of this game-changing technology.
Uniphore can help. Our first-of-its-kind Zero Data AI empowers functional users and citizen developers to build and manage intelligent and personalized AI agents for specific business needs and workflow automation.
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Read more about our Zero Data AI or contact us to discuss how agentic AI can reshape your workplace firsthand.
