By combining platform-native accessibility with federated access to documentation, enterprises provide AI agents with context while preserving data integrity.

An AI agent’s persistent identity should also include records that document the permissions granted and the business authority behind the agent.

Unsanctioned AI use can lead to data leaks, alongside security and compliance issues, requiring organizations to have the proper governance in place.

While transcripts and operational records show what happened, the harder challenge is capturing why an employee recognized a risk data failed to reveal.

Agentic AI has changed workflows, providing context-aware automation, but it still has security and governance risks.

Incident response has to go a step further when AI agents are involved, requiring organizations to reconstruct, and potentially reverse, an agent’s chain of actions and decisions.

Copilots have begun routing customers, prioritizing cases and triggering resolutions, requiring CX leaders to set clear boundaries defining what AI may decide.

Without governance guardrails in place, enterprise data is at risk, undermining AI’s ROI.

Who's responsible for telling AI agents to work and for the results of agentic AI workflows? Organizations are grappling with these questions.

As AI gives all contact center platforms the same performance-boosting features for human agents, the competitive gap will move to outcomes customers can feel.

By automating some customer interactions, clearly defined digital agents allow organizations to extend their CX resources without a drop in service.

These three priorities offer platform providers more of an advantage than any individual type of channel partner.

Agentic AI has changed how organizations work — but not all the changes are for the better, and some of those changes create more work for humans.

The AI disclosure banner is here for customer service, but trust will take more work. What should meaningful disclosure for interactions with enterprise AI systems look like in contact centers?

Not only would this solve a big problem for SMBs, but it helps extend a business-grade user experience to mobile devices.

AI has affected how providers set pricing models, bringing new challenges to CRM, UCaaS and CCaaS.

Built into the native Zendesk Contact Center experience, the new feature is notable for what it lacks — no separate application, vendor agreement or custom integration required for use.

On Sep. 30, 2026, No Jitter will stop publishing. We’re grateful to all those who read our publication over the last two decades.

Traditional CX was built for a human-only approach. Now organizations have to consider how AI has changed it.

IDC’s Kathy Lange discusses synthetic data and how companies can use it, as well as what they should be aware of.

While giving AI agents human characteristics can make adoption smoother, it can lead to over-reliance or issues with visibility and ownership.

Customer presenters provided a practical balance to Genesys’s ambitious agentic orchestration message.

AI feeds off data, but organizations don’t always keep their data clean.

We don’t foresee a contact center run entirely by AI agents or entirely by humans — but there are three different models we do see.

From agentic AI pricing models to CCaaS-CRM convergence, 2026's first eight months reveal how context and human oversight drive customer experience gains.

Businesses need voice-based AI agents that can interact naturally, quickly and follow established processes. With Alma, Phonely seeks to meet those needs.

Analyst Dave Schubmehl speaks with No Jitter about agentic AI and what organizations should know when implementing this tool.

Increasingly, customers can buy work rather than access to the software used to do it. This changes what customers want to buy and what vendors need to sell.

Four new products aim to govern agentic capabilities in Genesys Cloud, and Genesys also updated its own virtual agent.

Think through what happens when an AI agent is no longer useful because it outlived its project, retained access after its human owner moved on, or became embedded into obsolete workflows.

Users can now interact with enterprise customer data through Claude — and Salesforce is handing its interface, user workflow and engagement layer to Anthropic.

Assessing gen AI benefits in an organization isn’t easy, but the technology does appear to be delivering on some ROI promises.

However, challenges with ownership, data access and governance still stand in the way.

Issues with data security, governance, AI trust, system integration, poor data quality and skill gaps limit agentic AI adoption.

Salesforce in Claude leverages Anthropic’s models to help knowledge workers more easily unlock insights from business data.

Organizations need guardrails separating useful assistance from opaque performance management, intrusive monitoring and diminished worker autonomy.

AI is beginning to evaluate some employees in workplaces like call centers. But it may be unclear if workers can appropriately see, understand and challenge these assessments.

AI ROI is expected to continue to grow, but budgeting remains a challenge.

Zoom’s growth across various contact center and digital labor products validates its ‘AI-first system of action vision,’ CEO Yuan said.

Semantic search sifts through unstructured data to find information based on context and intent rather than keywords.

Organizations want to roll out agentic AI, but their systems were never made to handle them.

Most companies have deployed AI for various CX-centric use cases, but real gains may prove elusive without properly tagged data.

With a new CEO at the helm, a large installed base and new products in-market and about to land, Avaya has to prove it can innovate.

Technology and CX leaders need to coordinate systems to address application fragmentation.

By combining careful financial planning and continuous usage monitoring, AI pricing complexity can move from a budget threat into an opportunity for optimization.

While approving specific AI applications can mitigate the shadow AI’s spread, it won’t reveal everything that happens to enterprise data in an AI workflow.

As demand for data, orchestration, governance and trust capabilities increase, so will opportunities for industries to evolve. But some sectors will shrink in the wake of AI-generated outcomes.

With nearly 17 hours spent coordinating among their teams, most managers reported less-than-stellar results from using AI assistants

POINT includes validation for unstructured data — a type of data typically not governed or visible — and insight into AI tool actions.