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4 in 10 Online Shoppers Give Product Discovery Experiences a ‘C’ Grade or Below, According to New Study

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Constructor survey highlights opportunities for retailers to ‘make the grade,’ as shoppers express interest in more tailored search results and AI-driven features that help them find the best items

SAN FRANCISCO, Sept. 16, 2024 /PRNewswire/ — Cookie-cutter online shopping experiences. Pages (and pages) of search results that don’t fit the bill. Filtering functionality that’s so limited, it might as well not be there. If scenarios like these sound familiar — and you have trouble finding the right items when shopping online — you’re not alone. According to a new survey from Constructor, more than 4 in 10 online shoppers (42%) give product discovery experiences on retail websites a “C” grade or below.

The data — part of Constructor’s second annual State of Ecommerce Search and Product Discovery report — highlights implications for retailers of not “making the grade,” along with high-impact opportunities for improvement. Nearly 900 shoppers across the US (585) and UK (311) participated in this year’s study, completing an online survey about their preferences and experiences when they search and browse on ecommerce websites, excluding Amazon.

Search struggles & slowdowns
Shoppers cite search difficulties — nearly 7 in 10 (68%) think the search function on retail websites needs an upgrade. This was felt more strongly in the US (71%) than UK (61%), with search challenges including:

Scrolling, scrolling, still scrolling… 44% of shoppers say it takes at least 3 minutes to wade through search results and locate the product they need. For more than 1 in 5 (21%), it takes at least 8 minutes. Only a quarter of shoppers (24%) describe finding products online as “quick.”Query doesn’t compute: 41% of shoppers say they have to “frequently” or “always” reformulate search queries to get ecommerce search engines to understand what they mean. 85% do so at least “sometimes.”That’s not quite right: For 42% of shoppers, although search results are technically relevant to their queries (e.g., they search for “shirts,” and shirts get returned), the products aren’t what they’re hoping to see and don’t reflect their preferences. This is a bigger pain point in the UK (48%) than US (38%).

Additional struggles across both search and product discovery include:

Have we met… ever? Despite their previous engagement and purchase history, more than 4 in 10 shoppers (44%) say that when they shop with their favorite retailer online, the site treats them like a total stranger — with generic recommendations and a total lack of personalization across the buyer journey.Can this be more fun? Less than 1 in 3 shoppers (32%) say finding products online is “enjoyable” — highlighting an opportunity for retailers to create experiences that drive deeper engagement.

The benefits of getting it right
Poor product discovery experiences often breed poor results. For instance, online shoppers say when they can’t find what they want, they’re more likely to leave the retail site (52%) and take their wallets elsewhere — buying the item(s) from a different retailer (48%) such as Amazon (38%) or through Google (27%).

But shoppers say if they knew an excellent search and product discovery experience awaited them at an ecommerce site, they would:

Shop more at that retailer — 62%Choose that retailer first for their shopping needs — 42%Leave a positive review — 41%Pay 5-10% more for the item(s) they’re searching for — 24% (up from 15% last year)

“Good product discovery experiences literally pay off,” said Nate Roy, strategic director of ecommerce innovation, Constructor. “The bar for a good digital experience continues to rise, and successful retailers work to meet and exceed shopper expectations. As technology and cost barriers drop, it’s even easier for retailers to make incremental changes that measurably improve both the shopper experience and business results.”

Charting a path for improvement
For retailers looking to enhance product discovery and realize more of the benefits above, shoppers provided a wishlist for improvements. They say their search experiences would be better with:

Results that better reflect what they’re looking for — 45%Better filtering of results — 1 in 3 (33%)Personalization of search results — 30% (however, only 18% among those 60 years and older)Autocomplete functionality — 27%Better integration of online and in-store experiences — 24%; a bigger priority in the US (28%) than UK (16%)

Mobile matters
Mobile commerce continues to soar; survey data shows more than 6 in 10 shoppers (61%) do at least half of their online shopping from their mobile device. What’s more, 1 in 5 shoppers (21%) do all their online shopping via mobile — underscoring the importance of unfettered product discovery in mobile environments.

Shoppers ages 60+ were just as likely as other age groups to do all their online shopping from a mobile device (21%). However, those 60+ were also far more likely than other age groups to do their online shopping exclusively from a computer (30%). In contrast, only 4% of those ages 18-29, 3% of those 30-44, and 10% of those 45-60 years old use only the computer (not mobile) for online shopping.

Tapping into GenAI
Generative AI (GenAI) is becoming increasingly pervasive across both business and consumer landscapes. More than half of shoppers (51%) say they’ve tried ChatGPT and other GenAI tools (e.g., Bing Chat, Google Bard) in their daily lives — up significantly from 29% last year. Usage varies by age group: Among those 60+, 26% have tried GenAI tools, while those 18-29 years old report the greatest use (64%).

This increased comfort and familiarity has important implications across ecommerce product discovery — highlighting opportunities for retailers to take advantage of the technology:

More receptive to GenAI: More than half of shoppers (52%) say they’d be “very” or “somewhat” comfortable using ChatGPT and other GenAI tools that understand human language to help discover the best products for them — up 10 points from last year (42%).This varies by age: Among those 60+, only 34% note they would be “very” or “somewhat” comfortable.Let’s be clear: Nearly half of shoppers (49%) say it’s “very important” that retailers are transparent about their use of GenAI (53% in the US and 41% in the UK).

Given shoppers’ interest in, and openness to, the technology, it’s incumbent on retailers to identify use cases that will drive the most value. And with 80% of shoppers “often” or “sometimes” going to ecommerce websites unsure of what to buy (e.g., when they’re looking for a gift, starting a new hobby, etc.), there’s an opportunity to use GenAI to improve search processes and paradigms.

That’s because when shoppers are uncertain or wavering, sites often don’t help them out: Nearly 1 in 3 shoppers (32%) say when they’re unsure what to buy, retail sites make it “somewhat difficult” or “nearly impossible” to find the right item(s). But shoppers think GenAI-based features and technologies can help, and it behooves retailers to listen:

Searching in sentences: 44% of shoppers would like the ability to explain themselves in longform sentences in search (not just typing terse keywords) — and have the search bar understand.Engaging with AI assistants: When they’re unsure, more than 6 in 10 shoppers (61% — and 44% of those 60+) would “definitely” or “probably” be willing to let an AI shopping assistant help them — for example, explaining to the assistant through the search interface what they’re trying to accomplish, and getting personalized, in-stock suggestions.

In addition to AI assistants, looking across the ecommerce landscape, shoppers think GenAI has high potential to improve areas including:

Product recommendations — 41%Visual/image search — 36%Personalization — 33%Fraud detection — 25%; a bigger priority in the US (29%) than UK (19%)Virtual try-on — 21%Customer support — 20%

“There’s great interest in applying GenAI to ecommerce and, correspondingly, there’s also been a great pace of innovation,” said Nate Roy, strategic director of ecommerce innovation, Constructor. “But implementing GenAI for GenAI’s sake isn’t a smart move, and retailers are moving beyond what’s simply flashy to what’ll drive sustained value for them and their customers. Uses like AI assistants are already meeting shoppers’ needs and improving retail results. We encourage retailers to look at where product discovery is heading, and how consumers will increasingly want to engage — and then use AI and other technology strategically to cement themselves as leaders in this future.”

For more information, and to download the full State of Ecommerce Search and Product Discovery report, please visit bit.ly/state-of-ecommerce-2024

About Constructor
Constructor is the only search and product discovery platform tailor-made for enterprise ecommerce where conversions matter. Constructor’s AI-first solutions make it easier for shoppers to discover products they want to buy and for ecommerce teams to deliver personalized experiences in real time that drive impressive results. Optimizing specifically for ecommerce metrics like revenue, conversion rate and profit, Constructor generates consistent $10M+ lifts for some of the biggest brands in ecommerce, such as Sephora, Petco, Birkenstock, The Very Group, home24, Grove Collaborative and Fisheries Supply. Constructor is a U.S.-based company that was founded in 2015 by Eli Finkelshteyn and Dan McCormick. For more, visit: constructor.com

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SAP AppHaus and NTT DATA Expand Global SAP AppHaus Alliances to Accelerate Human-Centered SAP Business AI at Scale

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WALLDORF, Germany and LONDON, May 11, 2026 /PRNewswire/ — SAP AppHaus and NTT DATA Business Solutions today announced the expansion of their global SAP AppHaus Alliances partnership. The next phase of the collaboration focuses on helping organizations move beyond isolated AI experiments and accelerate the adoption of scalable, business–driven AI embedded into Cloud ERP environments.

Building on its established role within the SAP AppHaus Alliances, NTT DATA Business Solutions is taking a leading role in operationalizing human–centered solutions built with SAP Business AI at global scale. Central to this approach is the combination of the SAP AppHaus methodology with NTT DATA Business Solution’s GenAI Accelerated toolkit, enabling customers to identify high–value AI use cases, rapidly prototype solutions on SAP Business Technology Platform (SAP BTP), and industrialize them across SAP Cloud ERP landscapes.

Through its Global Enablement Program, using the SAP AppHaus methodology, NTT DATA Business Solutions is equipping multidisciplinary teams worldwide with a repeatable framework for AI exploration, design and delivery. The initiative brings together SAP AppHaus human–centered design, NTT DATA Business Solution’s proprietary GenAI Accelerated assets, and SAP technologies such as Joule and SAP Business Data Cloud.

This integrated approach helps customers achieve faster time–to–value, lower development risk and higher adoption by directly connecting AI innovation to core business processes. Rather than treating AI as a standalone initiative, NTT DATA Business Solutions embeds it into Cloud ERP transformation programs – an approach reflected in its global customer engagement theme Cloud ERP Supercharged.

“With Cloud ERP Supercharged, we are deliberately redefining how SAP Business AI is used in SAP environments. It is not about isolated use cases, but about embedding AI directly into Cloud ERP processes, from master data and partner collaboration to document handling and logistics,” said Nicolaj Vang Jessen, Executive Managing Director Consulting GIIC and Nordics & Eastern Europe, NTT DATA Business Solutions.  “By combining human–centered design, ready–to–run AI extensions and Joule capabilities, we enable our customers to automate, run and continuously learn, turning Cloud ERP into a platform for sustained business performance.”

The expanded alliance builds on successful joint customer engagements, including organizations such as Amey and Aspen Pumps, where NTT DATA Business Solutions applied the SAP AppHaus approach and the GenAI Accelerated toolkit to deliver tangible outcomes – from smarter decision–making and process automation to improved operational resilience.

“Our expanded partnership with SAP AppHaus reflects a deliberate shift from isolated AI use cases to enterprise–wide SAP Business AI,” said Mark Wheeler, Global Head of Product Engineering & AI Customer Success, NTT DATA Business Solutions. “By combining human–centered design with our GenAI Accelerated toolkit, we enable customers to translate AI ambition into solutions that improve speed, quality and competitiveness, directly within their SAP Cloud ERP environments.”

With operations in more than 30 countries and over 15,000 SAP specialists worldwide, NTT DATA Business Solutions continues to differentiate itself in the SAP ecosystem by combining industry expertise, proprietary AI assets and global delivery at scale. The SAP AppHaus methodology further reinforces the company’s ambition to actively shape how SAP Business AI is designed, deployed and scaled within SAP–centric enterprises.

For more information, please visit nttdata-solutions.com.

About NTT DATA Business Solutions

NTT DATA Business Solutions is focused on SAP and works within a strong ecosystem of partners including Microsoft and ServiceNow. We enable midmarket and lower large enterprise companies worldwide to become Intelligent Enterprises – from consulting and implementation to managed services. We are part of NTT DATA a $30+ billion business and technology services leader, serving 75% of the Fortune Global 100. Together, we are committed to accelerating client success and positively impacting society through responsible innovation. We are one of the world’s leading AI and digital infrastructure providers, with unmatched capabilities in enterprise-scale AI, cloud, security, connectivity, data centers and application services. Our consulting and industry solutions help organizations and society move confidently and sustainably into the digital future. As a Global Top Employer, we have experts in more than 70 countries. We also offer clients access to a robust ecosystem of innovation centers as well as established and start-up partners. NTT DATA is part of NTT Group, which invests over $3 billion each year in R&D.

Press Contact NTT DATA Business Solutions

Jasmin Straeter

Head of Global Communications

NTT DATA Business Solutions AG

Königsbreede 1, 33605 Bielefeld,

Germany

T: +49 521 9 14 48 108

Email: Jasmin.Straeter@nttdata.com

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AnySearch Launches as Search Infrastructure Built for AI Agents

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HONG KONG, May 11, 2026 /PRNewswire/ — As AI agents rapidly evolve from experimental tools into productivity systems, AnySearch, a next-generation AI search product purpose-built for AI agents and enterprise AI systems, has officially launched, offering AI agents unified access to high-quality information.

Unlike traditional search engines or AI search products built primarily around public web content, AnySearch is founded on a fundamentally different premise: much of the information most valuable to AI agents is not publicly searchable.

A significant portion of high-value data does not reside on the open web, but within authenticated professional systems such as industry databases, real-time financial terminals, code repositories, academic platforms, and structured API services. As AI agents begin handling increasingly sophisticated tasks — including research and analysis, software development, and security audits — efficiently connecting to and accessing high-quality, fragmented data across multiple sources has become a key challenge for the next stage of AI application development.

The AnySearch team said, “Traditional search engines can only access a small fraction of the internet. But AI agents need far more than webpages — they require secure, reliable, structured, and real-time information that can support reliable reasoning and execution.”

To address this challenge, AnySearch aggregates extensive vertical data sources spanning finance, legal, academic research, cybersecurity, energy, and corporate intelligence, among other specialized domains. Through a single unified API, AI agents can directly retrieve accurate, structured results without requiring developers to manage dozens of disparate data interfaces. AnySearch natively supports Skill, MCP, and API connectivity, enabling seamless integration into AI agents, enterprise systems, and automated workflows.

The product is now available across multiple developer ecosystems, including GitHub, skills.sh, ClawHub, SkillHub, and Glama, with users currently receiving 1,000 free API calls per day.

As momentum in the AI search space continues to build, AnySearch is pursuing a distinct path from traditional search engines such as Google, focusing on high-precision, structured search capabilities purpose-built for AI agents.

According to internal benchmark evaluations across Frames, FreshQA, and WebWalkerQA, AnySearch delivered stronger results than public-web-based AI search products in both answer accuracy and execution efficiency. In complex real-world scenarios — including code retrieval, security analysis, real-time business decision-making, and industry research — agents integrated with AnySearch also demonstrated stronger capabilities in information seeking and task completion. Rather than sifting through vast amounts of unstructured web content, AnySearch intelligently routes queries to the most relevant specialized data sources and returns accurate, concise, and execution-ready results.

A growing number of industry observers believe AI is fundamentally reshaping the underlying logic of search. For decades, search engines have focused on helping people access webpages and information. As AI agents become more active across the digital ecosystem, the next generation of search infrastructure will focus on enabling AI systems to better understand the world and autonomously complete tasks.

From this perspective, AnySearch is not positioning itself as just another AI search product, but as a new form of infrastructure for the AI era.

Learn more about AnySearch:

Website: https://www.anysearch.com/

Github: https://github.com/anysearch-ai

X: https://x.com/AnySearchAI

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eclicktech Explores What Happens When AI Agents Start Owning KPIs

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XI’AN, China, May 11, 2026 /PRNewswire/ — Over the past year, the conversation around artificial intelligence in Silicon Valley has undergone a subtle yet significant shift.

From OpenAI introducing Agent-based solutions, to Anthropic launching Computer Use and Claude Cowork, and the emergence of autonomous AI systems like Devin and Manus, the industry focus is no longer centered solely on model performance or parameter competition. Instead, a new theme is becoming increasingly dominant: AI is moving beyond the “tool layer” and entering the “organizational layer.”

When financial giants like Goldman Sachs begin referring to AI coding assistants as “employee number one,” and SaaS companies shift discussions from “adding AI features” to “whether AI Agents could eventually take over the software control plane,” one thing becomes increasingly clear: AI is no longer just a copilot for human workers — it is beginning to function as an actual organizational participant.

This transformation is no longer limited to discussions in Silicon Valley. At eclicktech, a growing number of AI Agents have quietly “joined the workforce.” They are taking briefs, conducting analysis, drafting proposals, managing workflows, optimizing campaigns, and even operating with their own KPIs.

Drawing from eclicktech’s recently completed “AI Implementation Hackathon” and its large-scale AI Agent practices, a new question is emerging:

What happens when AI Agents start owning KPIs? And how will that reshape the growth systems of global enterprises?

Why Global Marketing Became One of the First Industries to “Organize Around AI”

“The rise of organizational AI wasn’t accidental — it was driven by the complexity of the business itself,” said Aodi Zhang, Chief Product Officer at eclicktech.

Global marketing today is no longer a competition of isolated creative ideas. It has evolved into a highly complex, real-time operating system involving multiple markets, platforms, languages, and creative assets running simultaneously. Millions of impressions, clicks, and conversions are generated daily, all requiring immediate analysis and response.

In this environment — one defined by high-frequency decisions, data intensity, and rapid iteration — traditional linear growth models built on scaling headcount are quickly reaching their limits. They can no longer match the increasing complexity or real-time responsiveness required by modern global businesses.

At the same time, AI capabilities have crossed a critical threshold.

Previously, AI functioned primarily as an assistive tool for isolated tasks. Today, AI Agents are capable of long-chain execution, tool orchestration, autonomous collaboration, contextual understanding, and independent decision-making.

For the first time, AI is beginning to meet the standard of an “organizational teammate.” It no longer requires constant human supervision at every step. Instead, it can understand objectives, autonomously plan execution paths, and deliver outcomes.

According to the 2025 China Enterprise AI Agent Application Research Report published by First Voice Research Institute, China’s enterprise AI Agent market reached RMB 23.2 billion in 2025, with a projected compound annual growth rate of 120% from 2023 to 2027. Behind this rapid growth is a strong enterprise demand for efficiency gains, cost optimization, and smarter decision-making.

Global marketing — with its complexity and need for real-time responsiveness — has naturally become one of the first large-scale testing grounds for organizational AI.

What Do These “AI Coworkers” Actually Look Like?

eclicktech’s recent “AI Implementation Hackathon” served as something closer to an organizational-level A/B test — placing AI directly into live business workflows to observe how organizations evolve around it.

“We no longer think of AI as a tool sitting in a browser bookmark bar,” Zhang explained. “We think of it as a teammate that can be assigned tasks, held accountable for outcomes, and integrated into operational workflows.”

Several standout projects emerged from the hackathon. But viewing them simply as “efficiency tools” would significantly underestimate their value. Once these systems are viewed through an organizational lens, it becomes clear that eclicktech has already introduced a new category of “AI coworkers” into its business operations.

These AI systems collaborate directly with human employees across the full global marketing workflow.

Hubert: The Always-On Collaboration Hub

In traditional workflows, communication between sales teams, account managers, campaign optimizers, and designers often resembled a relay race full of information leaks and disconnects.

Now, an AI system called Hubert has taken over much of that coordination.

Functioning like an always-online executive assistant, Hubert listens to fragmented requests across teams, automatically structures client information into centralized systems, and proactively alerts relevant stakeholders whenever updates occur.

Instead of relying on fragile human memory, Hubert transforms organizational knowledge into a shared operational intelligence system.

Dexter: The Data Specialist Built for Operational Problem-Solving

Anyone working in campaign optimization knows that analysts often spend the majority of their time reconciling data, identifying discrepancies, and tracing traffic sources.

Dexter now automates much of that process.

The AI system continuously monitors monetization and campaign performance dashboards. When anomalies occur, Dexter can identify root causes within minutes and generate attribution analysis and optimization recommendations before the workday ends.

By handling repetitive analytical work, Dexter enables senior analysts to focus on higher-level strategic decision-making while preserving organizational expertise as scalable operational intelligence.

Hunter & Link: AI Systems Reshaping Customer Acquisition

Within eclicktech’s business development and operations teams, two additional AI systems — Hunter and Link — are redefining sales workflows.

Hunter functions like a constantly active prospecting engine, scanning emails, LinkedIn, and websites to identify high-potential leads. It can autonomously generate personalized outreach emails and even optimize messaging through automated A/B testing.

Meanwhile, Link operates as an intelligent workflow assistant inside messaging platforms, automating inquiry collection, order notifications, and operational coordination.

Together, these systems allow human business development teams to focus less on repetitive prospecting and more on high-value negotiations and strategic relationship building.

AI Agents Are Becoming Infrastructure

These examples represent only part of eclicktech’s broader AI ecosystem.

Today, dozens of AI coworkers are embedded across eclicktech’s operations, supporting creative generation, campaign optimization, budget allocation, data attribution, intelligent customer service, and technical operations. Together, they form a goal-oriented organizational AI ecosystem.

Zhang emphasized that this does not mean organizations can completely remove humans from the loop.

“The more powerful AI becomes, the more important clear operational boundaries become,” he said. “AI handles execution and operational tasks, while humans remain responsible for oversight, judgment, and final decision-making. That human-AI collaboration model is critical for maintaining operational safety and business reliability.”

The scale of adoption is already significant.

According to preliminary estimates, eclicktech’s internal AI systems currently consume more than 4 billion tokens per day. Behind that figure is a growing number of AI Agents operating across real production environments, transforming AI computing power into measurable business growth.

Supporting this ecosystem is EC-Agent, eclicktech’s proprietary enterprise AI Agent development platform. The company says customized AI Agents can now be built in as little as five minutes, reducing development costs by up to 80% and enabling large-scale AI deployment across the organization.

From Silicon Valley’s evolving AI conversations to eclicktech’s real-world implementation, one trend is becoming increasingly evident:

When AI Agents begin owning KPIs, they are not simply improving operational efficiency — they are fundamentally reshaping how global enterprises function.

AI is no longer just an assistive tool. It is becoming an organizational participant working alongside humans. And as AI systems continue evolving, enterprises that successfully redesign themselves around human-AI collaboration may gain a significant competitive advantage in the next era of global business.

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