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.

With agentic AI rollouts and trials more common, companies plan to invest in employee training, Liferay survey finds.

Customer service teams are increasing investments in the technology even as economic value remains elusive, Gartner finds.

As agentic AI moves from pilot projects to deployment in CX workflows, the effects are visible for both customer service agents and customers.

Agentic AI deployment is only as effective as the foundational state of the enterprise data infrastructure underpinning it. Leaders must master the fundamentals, from data context to siloed sources.

Organizations need new ways to determine whether AI tools improve team performance and leadership effectiveness or merely accelerate administrative work.

As more enterprises incorporate agentic AI into collaborative task flows, the question persists: Who owns the decisions and outputs generated via AI?

Contact centers and IT departments have been at the forefront of agentic AI-powered workflows, and now agentic AI use is being scaled to other business operations in the organization.

Growth in subscriptions, customer appreciation for multichannel solutions and fear of missing out on AI all powered profits.

The integration addresses a pivotal pain point for customers — having to repeat themselves on a call — by conveying more context between AI and human agents.

A new study from MIT and Google Cloud confirms that solid AI execution starts with a reliable data foundation and up-to-date data management practices.

AI is shifting the managerial task list from chasing updates to judging outcomes, raising the value of business context, trust and human intervention.

Metrigy’s research found that consumer preferences hinge on a variety of factors, so there is no one-size-fits-all deployment strategy.

Unsanctioned AI usage is a risk for every enterprise; counter it with solid sanctioned-use policies and better AI tools for doing the work.

Messaging, voice, software add-ons and AI boosted revenue, but Twilio warned of headwinds in the second half of the year.