Three SuccessKPI AI releases automate quality review work for human and AI agents while leaving human judgment in place

New AI capabilities compress the setup, search, and scoring work that slows quality teams down with a human in the loop at each step

FAIRFAX, Va., Sept. 03, 2026 (GLOBE NEWSWIRE) -- SuccessKPI, the performance platform for agentic and human customer experience, today announced three AI capabilities targeted at contact center quality management. The three releases share the principle that AI should absorb the volume work inside quality management and leave judgment with people.

The new capabilities address three activities that most commonly stall quality review work: scoring increasing volumes of calls, deciding what to listen for, and finding the interactions that require a human review.

“Automated Quality Management using AI is used by many customers,” said Dave Rennyson, CEO of SuccessKPI. “But simply surfacing more data using Auto QM can overwhelm supervisors and call center execs. With additional AI capabilities, SuccessKPI helps surface the data trends in aggregate, helps managers listen to the right calls to improve agent handling, and points executives to what improvements to make.

“In addition,” continued Rennyson, “our platform allows Auto QM, data trend analysis, recommendations, and improvement tracking across all agents, human and agentic.”

An agentic co-pilot to help administrators build functions

An agentic co-pilot is now available within the product where configuration work happens. A user describes what they want in plain language, and the co-pilot reads the customer's own data, plans the build, and drafts the function.

An evaluation form is the clearest example. A quality leader describes the program in a sentence or two. The co-pilot drafts the questions, categories, and weighting, then builds the form upon approval. Work that took specialist knowledge and several hours now takes a description and a review pass, and nothing goes live without human review and approval.

Find the interactions that matter without guessing through metrics

Quality teams have historically selected interactions using handle time, disposition codes, and survey scores. Those signals record what happened but rarely explain why. The new QA capability lets a reviewer describe, in plain language, the interactions they want, such as compliance exposure, repeat contact, or calls that are coaching candidates. The system returns a ranked short list, recommends whether to evaluate or coach, and shows the basis for that recommendation. A set of common cases is preconfigured, and teams can run and save their own custom queries. Reviewers can also open an AI summary of any interaction, with sentiment tracked across the call.

Evaluation scoring drafted by AI, decided by an evaluator

The third capability uses AI to automatically provide a score or draft an answer to every question on an evaluation form. Most importantly, it shows the reasoning and the evidence behind each answer the AI gave. Evaluators accept, edit, or override each answer. Quality leaders tune the model's judgment with natural-language guidance written per question, and test that guidance against real conversations before it goes live. The intended outcome is throughput, not headcount reduction, as reviewers can cover more interactions in the same hours because the first draft is already provided.

One data layer underlying everything

The three capabilities read from the same data layer as reporting, quality management, and speech analytics within the SuccessKPI platform. Selection, scoring, and analysis all draw on the same transcripts, evaluations, and behavioral signals. This is critical for cross-workflow recommendations, which are difficult in competitive platforms where each AI feature only sees its own module. The platform also runs across multiple contact center as a service (CCaaS) environments. Customers can choose best-of-breed CCaaS and AI agent platforms, and SuccessKPI can work across all of them.

Explainability as a control, not a talking point

Every AI output in this release carries its reasoning. Human overrides are recorded. The guidance shaping AI judgment is written and edited by the customer, not buried in a model. For buyers in regulated sectors, that trail is what makes an AI-assisted score defensible. It also gives quality leaders a way to correct AI behavior without a costly vendor ticket and change fee.

"This set of capabilities is one of several AI releases we'll issue over the coming months,” added Praphul Kumar, Chief Product Officer at SuccessKPI. “The pattern behind these innovations won't change. AI carries the volume, and humans provide the judgment."

About SuccessKPI

SuccessKPI is the performance platform for agentic and human customer experience, enabling an AI governance layer for contact centers. The company's cloud-native platform unifies Workforce Engagement Management, analytics, quality management, automation, and operational intelligence to help organizations optimize performance, improve customer outcomes, reduce risk, and accelerate business results. SuccessKPI is trusted by some of the world's largest government, BPO, financial, healthcare, and technology contact centers in the United States, Europe, and Latin America. With over 180 integrations and recognition as a leader in Frost & Sullivan's WEM Frost Radar™ and CRM Magazine's Industry Leader Awards, SuccessKPI continues to set the standard for AI-driven contact center optimization solutions.

Digital Networks:
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LinkedIn: https://www.linkedin.com/company/successkpi/

A photo accompanying this announcement is available at https://www.globenewswire.com/NewsRoom/AttachmentNg/7661a8ce-9281-456c-8133-7f46f855da53

Contact: press@successkpi.com

Three SuccessKPI AI releases automate quality review work for human and AI agents while leaving human judgment in place

New SuccessKPI AI capabilities compress the setup, search, and scoring work that slows quality teams down, with a human in the loop at each step. The new capabilities address three activities that most commonly stall quality review work: scoring increasing volumes of calls, deciding what to listen for, and finding the interactions that require a human review.