First AI agent-conducted benchmark of enterprise customer experience finds automation is everywhere, but resolution and connection are rare

NEW YORK, Apr. 22, 2026 /PRNewswire/ — Before a chatbot fumbles a question or an interactive voice response (IVR) tree traps a caller in menu purgatory, there is a more fundamental problem. Nearly half of the world’s largest companies display no visible phone number or chat option on their websites, leaving customers with no obvious way to reach support, according to a new large-scale study by agentic platform developer and global CX leader Parloa.

Releasing today, the State of Agentic CX in 2026 publishes in-depth findings from their market study, in which Parloa deployed purpose-built AI discovery agents to review more than 10,000 enterprise websites, including those of the Forbes Global 2000, then conducted nearly 4,000 “secret shopper” interactions across more than 800 companies in 27 industries. The three-phase operation, conducted in early 2026, is the largest AI agent-led benchmark of global enterprise CX across web, chat and voice channels. The conclusions describe an enterprise CX landscape in which modern interfaces mask outdated infrastructure, and system design consistently prioritizes customer deflection and dissuasion over resolution and connection.

Findings across channels highlight room for improvement

The “digital front door” is closed at 43.3% of the websites analyzed, which displayed neither a visible phone number nor a chat option for customer support. Among those that did, phone remained dominant at 54.1%, while only 28.9% offered chat. More than half of all sites required scrolling to locate support information, and nearly 95% of chat widgets appeared only on the homepage rather than on the support pages where customers with active problems land.

“We developed this study with the hypothesis that companies think of customer experience, particularly the service aspect, as a cost center, and not as a chance to deepen customer relationships,” said Latane Conant, chief marketing officer at Parloa. “The data show this to be the case, given a clear contact deflection strategy starting at the front door. Savvy companies that shift their CX execution to focus on and enrich the actual customer experience, including service journeys, will gain the competitive advantage and rightfully earn stronger customer lifetime value.”

Is chat all that?

Chatbots, a once highly anticipated option for digital self-service, proved to be rarely effective at actually solving customer problems. Of thousands of customer agent-led chat sessions, fewer than 1 in 11 resulted in the resolution of the customer’s stated goal. When automation failed, and customers tried to reach a human, the handoff succeeded only 10% of the time. Regarding the adoption of more conversational AI to better the chat experience, less than 8% of classifiable chatbots showed modern large language model capabilities. The remaining 92.5% still ran predominantly on restrictive rule-based systems. The study also found that 4 out of every 5 chatbots did not proactively disclose they were AI-powered, a finding currently attributed to brand preference but with growing regulatory implications.

Voice is choice, but stumbles when agents call

Voice systems told a similar story. Parloa researchers mapped more than 100 enterprise IVR systems after its own production AI voice agents, deployed early in the study, were blocked by touch-tone menus, rigid authentication gates and call flows built exclusively for humans. Reaching a person typically required three to four menu levels. Hold times stretched past 90 minutes. Most systems offered neither callback nor queue transparency, deepening the divide in connecting with the customer.

That breakdown pointed to the report’s most forward-looking finding: just 1% of tested enterprises demonstrated readiness for agent-to-agent interaction, the emerging model in which a customer’s personal AI agent communicates directly with an enterprise service agent. As consumers continue to leverage AI solutions to manage their day-to-day lives – initiating product returns, tracking deliveries, lowering bills, booking reservations, and more – the brands they’re trying to contact are treating their personal agents as security threats, reinforcing misconceptions about how AI works at scale. Legacy systems cannot process natural-language requests from AI callers, and authentication designed for humans breaks down when the caller is software.

“The limited readiness is not a reflection of insufficient ambition,” the report states. “It is a reflection of architectural constraints.”

The study outlines strategic imperatives and tactical approaches for enterprise CX leaders to overcome these findings and process gaps, and is designed for annual repetition and industry-level segmentation. The full report is available now at stateofcx.parloa.com.

Parloa empowers global enterprises to build, train, and manage AI agents for premier customer experience. Founded by Malte Kosub and Stefan Ostwald, Parloa began with the belief that every great conversation is the start of a relationship, a principle that still guides how the company builds technology today. Leading global brands use Parloa’s advanced AI agents to improve service at scale, increase customer loyalty, and unlock new revenue. Parloa employs 430 people across offices in New York, Berlin, London and Munich.

Parloa Press Team
press@parloa.com

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New benchmarks show EDB Postgres® AI can deliver 5x–12x faster vector indexing and up to 57% lower token usage, as the company proves that the most effective path to AI efficiency runs through the data layer, not the GPU.

WILMINGTON, Del., April 22, 2026 /PRNewswire/ — EnterpriseDB (EDB), the leading sovereign AI and data company, today announced a suite of validated performance efficiencies within EDB Postgres AI (EDB PG AI), designed to drastically reduce data center power consumption, lower token usage, and deliver an unprecedented “intelligence per watt” standard for the enterprise.

By 2027, half of enterprises will be using AI agents to redefine how humans and machines collaborate. By 2030, more than 1 billion agents will be actively deployed and will execute roughly 217 billion actions a day. These agents will consume trillions of tokens daily, and electricity demand from data centers worldwide will more than double by 2030 to around 945 terawatt-hours (TWh), with AI being the most significant driver of that increase.

“The AI energy conversation has been about what happens with the models and GPUs. Almost nobody is talking about what happens at the data layer that every agent, every model, every inference call depends on,” said Quais Taraki, CTO at EDB. “You can’t control consumption at the model layer. Agents consume what they consume. But you can control efficiency at the data layer, and for most enterprises, that’s the only lever they actually have.”

Sovereign architecture unlocks efficiency at the core and the data layer

EDB PG AI addresses the agentic energy challenge on two complementary fronts: first, by shrinking the core infrastructure footprint required to run enterprise applications; and second, by making the data-layer operations that power agentic AI—especially search, retrieval, and vector indexing—far more efficient. Together, those gains improve not just how much infrastructure enterprises need but how effectively that infrastructure is used per unit of energy.

At the infrastructure level, EDB PG AI helps enterprises reduce the servers and cores required to run applications, lowering both data center energy use and emissions. An analysis of three BFSI customers operating more than 120 data centers, independently validated by Incendium Consulting, showed up to 94% reduction in compute cores in one case, resulting in up to 87% expected emissions reduction—approximately 153,000 metric tons of avoided CO2e, equivalent to removing 33,000 cars from the road.

At the workload level, EDB is targeting one of the most underappreciated drivers of AI energy cost: the intensive data-layer operations as agents create databases, adjust queries, and move data across enterprise environments 24/7/365. Building and maintaining vector indexes is among the most resource-intensive activities in modern databases—and one that scales directly with the number of agents in production.

New benchmarks show EDB PG AI delivers:

5x–12x faster vector index builds with comparable or superior throughput at 1 billion vectors on a 128 GB server versus 1,000+ GB for traditional vector engines—a step-change reduction in the compute and memory required for AI-scale data retrieval 1

Up to 57% reduction in AI token consumption, with 90% quality preserved and a 72% scenario win rate, demonstrated in a pilot with a leading global telecom provider—directly reducing the energy cost of every agentic interaction

These results build on EDB PG AI’s broader efficiency gains, including 50x–100x faster analytical workload completion on live operational data, and up to 58% lower cost with the lowest concurrency degradation among leading cloud analytics platforms. These are capabilities that reduce the energy overhead of data storage, retrieval, and analysis across the enterprise stack.

The intelligence per watt framework

Building on these demonstrated efficiency gains, EDB PG AI delivers an “intelligence per watt” standard for global enterprises to measure, improve, and operationalize AI efficiency at scale, as autonomous systems create more databases, pipelines, and queries over time.

The platform is built around three principles that compound as agentic workloads scale:

Measure: Quantify the energy and infrastructure cost per unit of AI intelligence produced, extending the Incendium-validated methodology to agentic, RAG, and multi-agent workloads.

Optimize: Reduce compute, storage, and network demand per AI operation through database consolidation, storage tiering, query acceleration, vector indexing, and token reduction.

Govern: Maintain visibility and control over data layer operations as autonomous agents create databases, indexes, pipelines, and queries at machine speed.

“Enterprises succeeding with AI at scale are 275% more likely to prioritize energy-efficient data infrastructure than the rest of the market. They’re also seeing 5x the ROI. That’s the connection most of this industry is missing. This idea of ‘intelligence per watt’ isn’t just an environmental metric—it’s a performance indicator. The companies getting the most from AI are the ones demanding the most from their data layer,” said Kevin Dallas, CEO of EDB.

Organizations can quantify their own intelligence per watt with the EDB PG AI Efficiency Calculator at www.enterprisedb.com/calculator/efficiency, or visit enterprisedb.com to learn more.

About EDB

EDB Postgres® AI (EDB PG AI) is the first open, enterprise-grade sovereign data and AI platform—secure, compliant, and scalable, on-premises and across clouds. Built on Postgres, the world’s leading database, EDB PG AI unifies transactional, analytical, and AI workloads, enabling organizations to operationalize their data and LLMs while maintaining control over sovereign environments. EDB PG AI is supported by a global partner network and delivers up to 99.999% availability as well as hybrid management and a built-in AI factory. As one of the most active contributors to the PostgreSQL project, EDB is deeply invested in the vitality of the global community. To learn more, visit www.enterprisedb.com.

EnterpriseDB and EDB are registered trademarks of EnterpriseDB Corporation. Postgres and PostgreSQL are registered trademarks of the PostgreSQL Community Association of Canada and used with their permission. All other trademarks are owned by their respective owners.

¹ Based on completed and independently validated EDB benchmarks. Published report forthcoming.

Media Contact:
Steph McGuirk
412618@email4pr.com
(845) 269-8868

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SOURCE EnterpriseDB

PANAMA CITY, April 22, 2026 /PRNewswire/ — Aurise Foundation today announced the launch of XAUE, a yield-bearing gold token designed as a Treasury Layer for Tether Gold (XAU₮). Built for qualified institutional participants, XAUE introduces crypto-native yield generation to traditionally non-yielding gold, transforming it into a programmable and capital-efficient on-chain asset.

At launch, ecosystem partners Aurelion and Antalpha have jointly committed 16,052 XAU₮ into XAUE (approximately $76 million as of April 22). As the ecosystem expands, XAUE may potentially integrate with leading more decentralized financial protocols, positioning itself as a foundational collateral and settlement asset across on-chain financial markets.

From Passive Store of Value to Productive On-Chain Asset

Gold has long served as a hedge against inflation and systemic risk, yet its capital efficiency has remained structurally limited. While gold ETFs improved accessibility, they lack 24/7 liquidity. Digital gold assets such as XAU₮ and PAXG have enhanced transferability and global accessibility, but still primarily rely on price appreciation without intrinsic yield generation.

XAUE is designed to bridge this gap. By preserving exposure to the underlying value of XAU₮ while introducing a gold-denominated yield mechanism, XAUE enables holders to benefit from compounding growth measured in gold units. The protocol adopts a monotonically increasing exchange rate model, where the gold value backing each XAUE token might grow over time as net yield accrues.

For example, Deposit 1 XAU₮ to receive 1,000 XAUE (1000:1). With a 2% annual yield, reserves grow to 1.02 XAU₮ while supply stays fixed, so 1,000 XAUE redeems for 1.02 XAU₮—delivering passive, auto-compounding gold returns.

About XAUE

XAUE is a decentralized asset enhancement protocol built on Ethereum, designed as a Treasury Layer for Tether Gold (XAU₮). By introducing gold-denominated yield, compliant access frameworks, efficient reserve verification, and a 1000:1 fractionalization model, XAUE redefines how gold can function within on-chain financial systems.

About Aurise Foundation

XAUE is a DeFi protocol on Ethereum and the Treasury Layer for Tether Gold (XAU₮), issued by Aurise Foundation (Panama). It enables yield generation on gold via quantitative strategies and institutional lending, while remaining fully backed by physical gold or XAU₮. Aurise Foundation and its partners oversee governance, audits, Proof of Reserves, and AML/KYC compliance. Access is limited to whitelisted, KYC/KYB-verified institutions in eligible jurisdictions. More information: xaue.com.

Disclaimer

This press release does not constitute any offer or solicitation. The disclaimer is available here

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SOURCE XAUE