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The 2026 State of CX Outsourcing
As AI continues to permeate a number of business processes, organizations are becoming more familiar with the technology. By now, employees either know what generative AI can do for their workflows or have already embedded it into processes.
Yet generative AI is just one step to workforce optimization. What we’re seeing now is a move from generative AI (which assists with content creation) to agentic AI (which can autonomously plan and execute marketing goals). This technological shift is designed to reduce manual workloads by enabling systems to independently optimize campaigns and respond to real-time user behavior.
Table of Contents
From Generative to Agentic
When it comes to artificial intelligence and its effect on the digital workplace, 57% expect AI to have the most impact on automating simple, repetitive processes, according to recent research. This is where agentic AI has a distinct advantage over generative AI. One of the biggest drawbacks to generative AI is its reliance on prompting and refinement. With agentic AI, employees can let the program do its work without needing to be there every step of the way.
The primary difference between generative AI and agentic AI task execution lies around autonomy and the scope of work each type of technology handles. Compared to generative AI, agentic AI can typically use tools, connect to other services (typically via MCP servers), read their data and execute specific tasks.
Put simply, if you want to do something, use generative AI. If you want something done for you, then agentic AI is the way to go.
From Search to Conversation
There’s room for improvement around enterprise search capabilities. According to research, nearly half of organizations believe their enterprise search and collaboration tools are either merely “Satisfactory” or “Needs work” to be truly optimized.
Unlike traditional search, which simply lists results based on keywords, agentic AI capabilities turn on-site search into a natural language conversation. This allows the system to clarify intent and guide decisions. With the AI acting as a virtual teammate that listens and asks clarifying questions, it can understand what the user truly needs and steer the conversation toward insight, effectively delivering better results and shortening decision times.
From Backlog to Action
One of the natural outputs of automating repetitive tasks is freeing up employees’ time. The less time workers spend on administrative tasks, the more time they have to work on higher-level projects — or tasks that have been on the back burner. If your organization has a large number of backlogged projects, then agentic implementation might be an answer to free up workers’ time and allow them to tackle those tasks.
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Offloading administrative tasks to AI has the potential to spur innovation within your organization. When workers spend less time doing repetitive work they’re able to spend more time working cross-functionally or going deeper into their area of expertise. This simple shift can both open your business to new areas it hasn’t explored and keep your employees happy at the same time. A recent report found that organizations deploying AI at an operational level outperformed their competitors by 44% in employee retention and revenue growth.
Keep Control
As AI becomes embedded in nearly every business solution, users are taking more of an interest in keeping control over the data used and content produced. Many leaders expect AI to help them minimize risk — which can be accomplished with good data governance and a solution that prioritizes privacy and control.
When evaluating an agenticAI solution, make sure that the agents won’t act without your review and prior approval. A good agentic AI solution maintains privacy and ensures user control through a system of governed execution, where agents function as virtual teammates with strict boundaries rather than unchecked autonomous tools. Keeping humans in the loop allows for proper oversight, ensuring critical actions don’t happen without review.
There is a place for both technologies within a tech ecosystem, but it’s important to align each tool with your objectives. Prompt-dependent generative AI relies heavily on human direction. While it excels at producing outputs (text, images or code), it can’t independently decide what to do next. If the output requires adjustment, a human must re-prompt the system.
On the other hand, agentic AI goes beyond simple asset creation by acting autonomously to pursue broader objectives. Once an employee defines the goal and sets constraints, an agentic system can plan required actions, execute tasks across different tools and monitor results without constant human intervention. If your organization needs to scale and move work forward actively rather than assisting passively, an agentic system is what you need.
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