Even as education budgets tighten and burnout deepens, educators aren’t rejecting technology. They’re leaning into it—especially AI. The problem isn’t whether tools work. It’s that they don’t work together.

That’s one of the clearest takeaways from Jotform’s newly released report, EdTech Trends 2026: A Survey of What’s Working, What’s Not, and Where AI Is Heading. Based on responses from 50 K–12 and higher education professionals, the study paints a picture of a resilient but overextended workforce trying to do more with less—and increasingly turning to AI to bridge the gap.

The respondents, split roughly evenly between K–12 and higher education, include teachers, instructors, and professors navigating an increasingly complex digital ecosystem under growing financial pressure.

Budget Cuts, Burnout—and a Turn Toward AI

The backdrop to the report is sobering. More than half of educators surveyed (56%) say they are very concerned about recent cuts to U.S. education infrastructure. At the same time, burnout remains a persistent challenge as workloads expand and resources contract.

Yet rather than retreat from technology, educators appear to be embracing AI faster than many might expect.

According to the report, 65% of respondents are actively using AI. Nearly half of those users (48%) apply AI across both student-facing activities and administrative work—ranging from supporting learning experiences to summarizing long documents and automating feedback.

This dual use underscores a key shift in EdTech adoption: AI isn’t viewed solely as a teaching aid. It’s increasingly a productivity layer, helping educators reclaim time in an environment where time is in short supply.

The Integration Gap: Tools That Work—But Not Together

Ironically, the biggest frustration educators report isn’t poor technology. It’s fragmentation.

While 77% of respondents say their digital tools work well individually, 73% cite lack of integration between systems as their primary challenge. In practice, that means jumping between platforms just to complete basic tasks—grading, communications, reporting, and content management.

One respondent summed it up bluntly: “The No. 1 thing I would like for my digital tools to do is to talk to each other.”

This disconnect reflects a familiar MarTech and EdTech problem: point solutions proliferate faster than ecosystems mature. The result is operational drag, even when the tools themselves are well-designed.

Platform Fatigue Is Becoming the Norm

That drag adds up quickly.

Educators report using an average of eight different digital tools, with half saying they feel overwhelmed by “too many platforms.” Instead of simplifying workflows, technology often adds cognitive load—forcing educators to remember logins, workflows, and data silos across systems.

Despite widespread digitization, respondents still spend an average of seven hours per week on manual tasks, highlighting a gap between digital adoption and actual automation.

This is where expectations around AI are rising. Educators aren’t just looking for smarter tools—they’re looking for fewer steps.

AI’s Primary Role: Productivity First, Pedagogy Second

While AI is often discussed in the context of student learning, the report suggests its most immediate value lies elsewhere.

Among respondents using AI, 58% say they use it most frequently for productivity tasks such as research, brainstorming, and writing. These use cases are low-risk, high-impact, and directly tied to reducing workload—making them easier to justify amid ethical and institutional scrutiny.

That doesn’t mean teaching applications are off the table. But it does suggest AI adoption in education is following a pragmatic path: start where efficiency gains are clear, then expand cautiously.

Ethics and Data Security Remain Top of Mind

Caution is still very much part of the equation.

Educators cite ethical implications and data security as their top concerns when implementing AI. This reflects broader anxieties across regulated and people-centric sectors, where misuse of data or opaque AI behavior can erode trust quickly.

For EdTech providers, that concern raises the bar. It’s no longer enough to ship AI features. Platforms must clearly communicate how data is handled, how models are used, and how institutions remain in control.

As Lainie Johnson, Director of Enterprise Marketing at Jotform, noted, the surprise wasn’t dissatisfaction with tools—but the friction between them. “While the tools themselves are great, their inability to work together causes a problem.”

The Bigger Picture: Less Tech, Better Systems

The EdTech Trends 2026 report mirrors what’s happening across MarTech, HRTech, and RevOps: users don’t want more software. They want systems that reduce complexity.

AI, in this context, isn’t a silver bullet. But it’s increasingly seen as a connective layer—one that can automate handoffs, reduce manual work, and make fragmented ecosystems feel cohesive.

For educators navigating budget constraints and burnout, that promise may matter more than any individual feature.

The message from the field is clear: technology adoption in education isn’t slowing down—but tolerance for friction is.

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BizzyCar made its name helping dealerships tackle one of their most painful operational problems: recall management. Now, the company is pushing its AI deeper into the service lane.

The automotive MarTech provider has launched Service Engine, an AI-powered outbound service solution designed to identify service opportunities, engage customers via SMS, and book appointments automatically—without dealerships needing to add headcount or run manual campaigns.

The move signals a broader shift for BizzyCar: from a recall-focused specialist to a full-service AI engagement engine aimed squarely at dealership profitability.

From Recall Automation to Revenue Growth

Service departments are under mounting pressure. Fixed ops margins are tightening, customer expectations for instant responses are rising, and staffing remains a chronic challenge. Filling service bays consistently—especially with customer-pay work—has become a growth problem, not just an operational one.

Service Engine is BizzyCar’s answer.

Built on the same AI foundation as its recall management platform, the new solution automates outbound service engagement end to end. The system identifies eligible service opportunities, initiates two-way SMS conversations, answers questions, and schedules appointments directly into the dealership’s scheduler.

According to BizzyCar, the AI agent driving these interactions delivers a 52% conversion rate, outperforming traditional human call centers at a fraction of the cost.

That performance metric matters in a category where outbound service calls often struggle to break through voicemail and low response rates.

Dealer Demand Drove the Expansion

BizzyCar says Service Engine wasn’t a speculative product—it was built in response to direct dealer demand.

After seeing success with recall campaigns and mobile service coordination, dealerships pushed BizzyCar to extend the same AI-driven approach to non-recall service opportunities and broader customer engagement.

“Dealers are under constant pressure to keep service lanes full without adding staff,” said Ryan Maher, CEO of BizzyCar. “Service Engine lets them do just that.”

The expansion reflects a larger industry trend: AI agents moving beyond single-use cases into persistent, multi-purpose customer engagement roles.

AI Built for the Service Lane—Not Just Marketing

Unlike generic messaging automation tools, Service Engine is purpose-built for service operations. The AI agent understands dealer-specific rules, remembers past conversations, and adapts responses based on service history and customer context.

The system can autonomously manage campaigns for:

• Driving first service visits

• Managing next service intervals

• Recovering declined services

• Reengaging lost or inactive customers

Only when a conversation hits a predefined threshold—based on dealer-defined rules—does the AI hand the interaction to a human BDC agent. When that happens, staff receive a concise summary, allowing them to step in without restarting the conversation.

This “humans in the loop” model aims to balance automation scale with customer experience—an increasingly important distinction as dealerships experiment with AI-driven communications.

How Service Engine Works in Practice

At a workflow level, Service Engine automates what is typically a fragmented, manual process:

• BizzyCar identifies service opportunities from dealership data

• The platform launches outbound SMS campaigns on the dealer’s behalf

• The AI agent manages conversations and books appointments

• Only flagged interactions are escalated to dealership staff

• Appointments sync directly with the dealer’s scheduling system

• All activity and performance data flows into the Service Engine dashboard

For dealerships, the appeal is simple: more booked appointments, fewer manual touchpoints, and clearer visibility into what’s driving results.

Centralized Data and Operational Visibility

Service Engine also functions as a centralized command center for service engagement.

Through DMS integration, the platform brings together customer profiles, service history, conversations, and appointments into a single interface. BDC agents and service staff can view both AI-handled and AI-escalated interactions, access detailed customer records, and schedule appointments that write directly back to the dealer’s systems.

Managers, meanwhile, gain real-time insight into both AI and human performance—tracking appointment volume, show rates, and team-level metrics from the Service Engine dashboard.

That level of transparency is critical as dealerships evaluate whether AI is actually improving outcomes—or just shifting workload.

Why This Matters for Automotive MarTech

Service Engine underscores a growing pattern across MarTech and vertical SaaS: AI agents are becoming operational employees, not just marketing tools.

In automotive retail, where margins are thin and labor is expensive, AI-driven service engagement offers a compelling value proposition. Automating outbound service doesn’t just save time—it directly impacts revenue, retention, and long-term customer value.

By extending its AI beyond recalls into everyday service operations, BizzyCar is positioning itself at the intersection of customer experience, revenue operations, and automotive MarTech.

If the promised conversion rates hold up at scale, Service Engine could push more dealerships to rethink how much of their service engagement really needs to be human-led.

Availability

Service Engine will be officially rolled out at NADA Show 2026 and will be available as an add-on for dealerships already using BizzyCar’s Recall Management platform. Onboarding includes DMS-based service interval configuration and setup for BDC agents and managers.

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ID Dataweb is sharpening its go-to-market strategy as identity-based attacks surge—and it’s doing so by bringing in a familiar face from the cybersecurity playbook.

The identity threat detection and risk mitigation company has appointed Dr. Torsten George as its first Chief Marketing Officer, signaling a more aggressive push to expand market visibility, customer engagement, and demand for its SaaS platform across highly regulated industries.

For ID Dataweb, the hire marks a transition from product-driven growth to market-led scale, at a moment when identity fraud, account takeover, and AI-assisted attacks are forcing enterprises to rethink how they secure digital interactions.

A Strategic Hire at a Critical Inflection Point

George steps into the role with a clear mandate: elevate ID Dataweb’s brand, sharpen its positioning, and help more organizations understand why identity risk can no longer be addressed with credentials alone.

“Torsten’s strategic marketing acumen, combined with his deep cybersecurity expertise, make him an ideal fit to help lead ID Dataweb into its next phase of growth,” said Dave Coxe, co-founder and CEO of ID Dataweb.

The timing matters. Identity has become the front line of modern cyber risk, cutting across employees, partners, customers, and third parties. As attacks grow more sophisticated—and harder to detect with traditional IAM tools—vendors that can clearly articulate differentiated value are gaining an edge.

Three Decades at the Intersection of Security and Growth

George brings more than 30 years of experience leading marketing and product organizations at fast-growing cybersecurity and identity companies, many of which culminated in successful acquisitions.

His résumé includes senior leadership roles at:

• ConnectWise

• Absolute Software (acquired by Crosspoint Capital)

• Centrify (acquired by Thoma Bravo; now Delinea)

• RiskSense (acquired by Ivanti)

• ActivIdentity (acquired by HID Global)

Across these roles, George has built a reputation for repositioning brands, refining go-to-market strategies, and translating complex security capabilities into narratives that resonate with buyers in crowded markets.

He is also a frequent author and speaker on digital identity, data protection, and compliance—areas that increasingly overlap with MarTech, CX, and customer trust.

Why Identity Security Needs Better Storytelling

ID Dataweb’s platform focuses on identity threat detection, sitting alongside—and enhancing—traditional identity and access management (IAM) systems. Rather than relying solely on credentials, the platform evaluates risk continuously across interactions, helping organizations detect fraud without adding friction for legitimate users.

That distinction is becoming more important as enterprises balance security with experience.

“Credential-only authentication is no longer sufficient to combat identity-related attacks,” George said. “ID Dataweb is uniquely positioned to address this challenge and enhance traditional IAM tools.”

From a market perspective, that message lands squarely in a growing gap: many organizations know IAM alone isn’t enough, but struggle to articulate what should come next.

George’s appointment suggests ID Dataweb plans to own that conversation—especially in sectors such as financial services, insurance, healthcare, travel, hospitality, and the public sector, where identity risk directly impacts revenue, compliance, and customer trust.

The Broader MarTech and CX Implications

While ID Dataweb sits firmly in cybersecurity, the implications stretch into MarTech and customer experience.

As brands push toward passwordless login, personalization, and omnichannel engagement, identity becomes a shared responsibility across security, marketing, and digital teams. Fraud prevention can no longer come at the cost of user experience—and experience can no longer ignore risk.

Platforms that can reduce fraud and preserve seamless interactions are increasingly viewed as business enablers, not just security controls.

That positioning challenge—bridging security outcomes with growth narratives—is exactly where seasoned CMOs like George tend to have outsized impact.

What to Watch Next

With George leading marketing, expect ID Dataweb to:

• Clarify how identity threat detection complements IAM and CX stacks

• Speak more directly to business and risk leaders—not just security teams

• Increase visibility through thought leadership around identity fraud and AI-driven attacks

• Tighten its narrative around measurable business outcomes, not just risk reduction

As identity threats continue to rise, vendors that can translate technical capability into strategic relevance will stand out.

ID Dataweb’s first CMO hire suggests the company is ready to do just that.

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Printemps has never been a conventional retailer. Since its founding in Paris in 1865, the luxury department store has built its reputation on reinvention—turning shopping into spectacle and curation into an art form.

As its New York flagship enters its second year of operations, that same philosophy is now being supported by something far more modern: data-driven retail infrastructure.

The iconic French retailer continues to run its U.S. store on Jesta I.S.’s Vision Suite, relying on the platform’s Merchandising ERP, Mobile Store Inventory Management, and Analytics solutions to manage one of the most complex luxury retail concepts to land in the U.S. market in years.

The system went live in March 2025, just days before Printemps New York officially opened its doors at One Wall Street—an Art Deco landmark that now houses the brand’s first American location.

Why Printemps Chose Jesta—After Reviewing 30+ Vendors

Printemps’ decision wasn’t casual. In 2024, the retailer evaluated more than 30 technology providers before selecting Jesta, with advisory support from Sophelle, a global retail management consultancy.

For a luxury brand redefining its footprint in a new market, the stakes were high. Printemps needed technology that could support deep merchandising complexity, real-time inventory visibility, and data-backed decision-making—without compromising the experiential nature of the store.

Jesta’s Vision Suite now underpins core merchandising, inventory, and analytics processes at Printemps New York, acting as the operational backbone for a retail concept that blends luxury shopping with hospitality, dining, and curated experiences.

A Flagship Store Built on Complexity

Printemps New York isn’t a traditional department store. Spread across two levels of One Wall Street’s 50-story Art Deco tower, the location is designed as an immersive destination rather than a transactional space.

Unlike concession-heavy luxury models, Printemps owns nearly all inventory on the sales floor, giving the brand tighter control over presentation, pricing, and customer experience. Roughly 25% of the brands featured are exclusive to the U.S. market, reinforcing the store’s differentiated positioning.

That ownership model, while powerful, dramatically increases operational complexity—making real-time inventory accuracy and SKU-level visibility critical.

Merchandising ERP as the System of Record

At the core of the Vision Retail Management Suite is Jesta’s Merchandising ERP, which serves as the single source of truth for inventory, orders, and product data.

With real-time updates across the system, Printemps New York teams operate from consistent, shared insights—reducing friction between merchandising, operations, and finance. The platform also supports:

• Demand planning and allocation

• Replenishment and price management

• Financials and sales audit

• SKU-level inventory tracking and profitability analysis

For a luxury retailer balancing exclusivity with scale, that level of precision is essential—not optional.

Mobile Inventory and Analytics Close the Loop

On the store floor and behind the scenes, Jesta’s Mobile Store Inventory Management streamlines inbound and outbound merchandise flows, stockroom management, vendor returns, and drop shipping.

Paired with Jesta’s advanced Analytics capabilities, Printemps gains near real-time insight into performance, allowing teams to respond quickly to shifts in demand, product velocity, and customer behavior.

In a retail environment where timing and availability directly affect brand perception, analytics becomes less about reporting—and more about protecting the experience.

Technology as an Enabler, Not the Experience

Both Printemps and Jesta are careful to frame technology as an enabler, not the star.

“Printemps New York represents a bold reinvention of luxury retail in the U.S.,” said Arvind Gupta, President of Jesta I.S. “Since going live, the Printemps team has been actively leveraging Jesta’s Vision Suite to unify data, improve inventory visibility and enable more agile merchandising decisions.”

Gupta noted that the store’s immersive nature can’t be fully understood without visiting in person—a reminder that, even in data-led retail, experience still leads.

Printemps New York CEO Thierry Prevost echoed that sentiment, emphasizing how technology supports rather than defines the concept. “Jesta’s retail technology has become a critical enabler of our operations—bringing structure, visibility and efficiency to a highly curated and complex retail environment.”

A Signal for Luxury Retail’s Next Phase

Printemps’ U.S. expansion highlights a broader shift in luxury retail. As brands experiment with experiential formats, exclusive assortments, and ownership-driven models, legacy systems are no longer sufficient.

Modern luxury demands real-time data, integrated inventory, and analytics that operate at the same pace as creative ambition. Retailers that can’t support that complexity operationally risk undermining the very experience they’re trying to elevate.

By pairing a historic brand with a modern retail technology stack, Printemps New York offers a glimpse into how luxury retail may scale in the U.S.—not by sacrificing curation, but by underpinning it with smarter systems.

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FGS Global, the world’s largest stakeholder strategy firm, is making a decisive move to formalize its AI ambitions. The firm has launched a dedicated AI Advisory practice and acquired Memetica, a specialist consultancy focused on AI-driven threat detection across social, dark web, and fringe platforms.

The new practice sits within FGS Global’s recently established AI and Innovation group, led by Aaron Kwittken, Global Head of AI and Innovation, and reflects growing client demand for practical, executive-level guidance on using AI—while also defending against its darker applications.

From Experimentation to Enterprise-Grade AI Advisory

While the announcement marks a formal launch, FGS Global has been investing in AI for years. The AI Advisory practice consolidates that work into a single, global offering designed to help organizations improve communications performance, protect reputation, and drive operational efficiency in an environment increasingly shaped by automation and algorithmic influence.

The practice is backed by more than 200 specialists worldwide, spanning AI strategy, digital influence, analytics, engineering, data science, crisis management, campaigns, and earned, paid, and owned media. It operates globally and integrates closely with FGS Global’s established strengths in crisis and reputation management, government and public affairs, and financial communications.

At the core of this effort is FGS Labs, the firm’s global development team responsible for building bespoke client and internal technology. That includes Fergus, FGS Global’s AI-powered agentic platform, now used by more than 1,500 consultants across 31 cities.

“Organizations today face a dual imperative,” said Kwittken. “They must harness AI to optimize workflows and strengthen stakeholder engagement—while also safeguarding against AI-generated threats to reputation and operations.”

Memetica Deal Expands Digital Threat Intelligence

The acquisition of Memetica significantly deepens FGS Global’s capabilities in early-warning threat detection and mitigation. The consultancy specializes in monitoring and analyzing online narratives across mainstream social channels, fringe networks, and deep and dark web environments—areas where reputational risks increasingly originate.

Memetica’s work focuses on identifying tipping points where digital narratives can spill into real-world impact, including cybersecurity incidents, AI-driven disinformation campaigns, SEO manipulation, doxxing, and even violent threats.

“With AI accelerating the scale and sophistication of online narratives, advanced threat detection and response will only become more critical,” said Alex Geiser, Global CEO of FGS Global.

Memetica founder Ben Decker described the move as a natural evolution of a long-standing partnership, positioning the combined offering as a differentiated, AI-driven approach to reputation and risk advisory.

Four Pillars of the AI Advisory Practice

FGS Global’s AI Advisory practice is structured around four core capability areas:

Generative Engine Optimization (GEO)
Helping brands remain visible and authoritative as generative AI reshapes search, through multimodal content strategy and real-time brand monitoring.

AI Risk & Crisis Management
Detecting and countering deepfakes, managing AI-driven misinformation, and establishing governance frameworks for responsible AI use.

Strategic Intelligence & Analytics
Delivering predictive insights, advanced stakeholder mapping, and scenario planning powered by proprietary analytics.

Organizational AI Transformation
Supporting technology audits, vendor selection, upskilling, and change management to help communications teams adopt AI competitively and responsibly.

A Signal of Where Communications and Reputation Are Headed

The launch underscores a broader shift in communications, public affairs, and reputation management. As AI reshapes how narratives are created, amplified, and manipulated, firms advising global organizations are being forced to evolve from message crafting to systems-level intelligence and defense.

By combining AI strategy, agentic tools, and deep threat intelligence under one umbrella, FGS Global is positioning itself not just as an advisor on AI—but as a firm built to operate inside its realities.

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Linnworks is betting that the next phase of ecommerce growth won’t be driven by more dashboards—but by smarter automation. The Connected CommerceOps platform has launched Spotlight AI, the first product in its new Commerce Ops Intelligence portfolio, aimed squarely at eliminating the manual work that quietly creeps back in as online retailers scale.

Launching platform-wide on January 20, Spotlight AI will be available to all Linnworks customers. Its promise is straightforward: continuously analyze operational workflows, surface inefficiencies retailers often overlook, and prescribe the most impactful automations—before those inefficiencies turn into costly risks.

Early adopters are already seeing tangible results. In testing, customers who implemented automation rules based on Spotlight AI’s recommendations saved more than 30 hours per month on average, a meaningful gain for lean ecommerce teams juggling growth, fulfillment complexity, and margin pressure.

Turning Operational Blind Spots Into Action

As ecommerce brands grow, many assume automation naturally keeps pace. In reality, Linnworks argues, the opposite often happens. New sales channels, higher order volumes, and expanding catalogs introduce layers of manual intervention—each one adding friction and risk.

Spotlight AI is designed to address what Linnworks calls the commerce paradox of scale versus risk. Instead of relying on teams to manually audit workflows, the AI continuously monitors day-to-day operations, identifies repetitive actions, and highlights where automation would deliver the biggest return.

Crucially, the system doesn’t just flag problems—it prioritizes them. Spotlight AI diagnoses why a task is slowing operations, quantifies the impact, and recommends the next best automation to deploy. The result is targeted, measurable optimization rather than blanket process changes.

From Insight to Automation—Without the Guesswork

According to Linnworks CEO Jon Bahl, Spotlight AI removes a common tradeoff retailers face as they scale.

“Retailers shouldn’t have to choose between growing fast and operating reliably at scale,” Bahl said. “Spotlight AI gives our customers visibility into where manual work is still slowing them down and provides clear, actionable guidance on what to automate next.”

That emphasis on clarity is key. Many ecommerce platforms surface performance metrics but stop short of telling teams what to fix. Spotlight AI is positioned as a prescriptive layer—one that not only reveals inefficiencies but helps eliminate them systematically.

Chief Product Officer Diana Nolting underscored the risk angle, noting that every manual step introduces potential failure.

“Most businesses don’t realize how much manual work still exists in their day-to-day operations,” Nolting said. “Spotlight AI was built to surface those blind spots automatically and turn them into practical automation opportunities.”

A Broader Push Toward End-to-End Automation

Spotlight AI is more than a point feature. Linnworks describes it as a foundational step toward automating the entire order lifecycle, from order ingestion to fulfillment and beyond. By embedding continuous optimization directly into the platform, Linnworks is signaling a shift from static workflow tools to adaptive, AI-driven operations.

That strategy aligns with a broader trend in commerce technology, where brands are increasingly seeking resilience and predictability—not just growth. As fulfillment costs rise and customer expectations tighten, eliminating hidden inefficiencies can be as valuable as acquiring new customers.

For Linnworks, Spotlight AI positions CommerceOps not as a backend necessity, but as a competitive advantage—one that scales alongside the business rather than holding it back.

Get in touch with our MarTech Experts.

From firmographics to market signals, AI promises deeper account intelligence than traditional data providers ever delivered. But pushing untested enrichment directly into Salesforce risks polluting core systems, breaking reporting, and eroding confidence across sales, marketing, and ops.

Traction Complete’s new product, Complete Discover, is designed to close that gap.

The company has introduced Complete Discover as a way to turn Google Sheets into an experimentation layer for AI-driven account enrichment—a place where teams can test prompts, validate outputs on real accounts, and uncover go-to-market insights without touching production data in Salesforce.

In short, it’s a playground for AI curiosity—with guardrails.

From AI Ambition to Operational Reality

The launch addresses a growing tension inside RevOps teams. Leaders want to explore AI enrichment that goes far beyond static firmographics—think sub-industry detail, market-level context, growth signals, and competitive insights. But operations teams are tasked with keeping Salesforce clean, consistent, and auditable.

According to Traction Complete CEO David Nelson, too many organizations are forced to choose between those two priorities.

“What we’re seeing in the market is a growing disconnect between AI ambition and operational reality,” Nelson said. “Too many teams are forced to choose between innovation and data integrity.”

Complete Discover is positioned as the missing middle layer—where AI enrichment can be explored, pressure-tested, and refined before it ever becomes operational.

Why Google Sheets Is the Right Testing Ground

Choosing Google Sheets isn’t accidental. It’s where revenue teams already explore ideas, test hypotheses, and share early insights before committing them to systems of record.

Complete Discover effectively turns Sheets into an account data lab, allowing teams to:

• Experiment with AI enrichment prompts

• Compare AI-generated insights against known data

• Identify what’s useful, what’s noisy, and what’s wrong

• Iterate quickly without governance risk

This approach mirrors how analytics teams validate models before deployment—but applied to AI-driven GTM data, where mistakes can directly impact pipeline, targeting, and sales execution.

Beyond Basic Firmographics

One of the key themes behind Complete Discover is that enrichment has outgrown traditional data categories.

Basic firmographics—company size, location, industry—are now table stakes. AI makes it possible to surface richer, harder-to-find insights, but only if teams can trust the outputs.

Complete Discover enables revenue teams to explore and validate enrichment such as:

• Hard-to-find firmographics, including private SMB data and companies outside North America

• Validation and supplementation of location, headcount, and industry fields

• Automatic industry normalization across records

• Revenue estimates and year-over-year growth rates derived from company name or domain

• Real-world sales intelligence, including M&A activity, technology usage, and competitor relationships

This shift toward sub-industry and market-level context reflects a broader MarTech trend: precision targeting over volume-based enrichment.

From Experiment to Execution With Complete AI

Crucially, Complete Discover isn’t a dead-end sandbox.

Once teams identify prompts and enrichment logic that consistently deliver value, they can deploy those workflows directly into Salesforce using Complete AI, Traction Complete’s no-code automation layer.

That handoff is where governance comes back into play. Complete AI allows RevOps teams to scale validated insights with:

• Consistent application across accounts

• Clear rules and controls

• No engineering dependency

• Protection of Salesforce as a trusted system of record

The result is a structured pipeline from experimentation to execution—something that’s been largely missing as AI tools flood the RevOps stack.

Why This Matters for RevOps Teams

As AI moves from novelty to necessity, revenue operations teams are increasingly responsible for deciding how AI gets used—not just if it does.

The risk isn’t underusing AI. It’s deploying it too quickly, without validation, and undermining trust in core data systems.

Complete Discover reframes AI enrichment as a RevOps-led discipline, not a vendor-driven black box. It gives teams a way to answer critical questions before scaling:

• Does this enrichment actually improve segmentation or targeting?

• Is the data consistent enough to automate?

• Where does AI outperform traditional providers—and where does it fall short?

Stephen Daniels, VP of GTM & Strategic Operations at Cresta, highlighted the appeal of that nuance.

“The product delivers nuanced, sub-industry insights that go far beyond what typical data platforms provide,” Daniels said. “It puts the information I’ve always wanted right at my fingertips—precise, comprehensive, and effortless to capture.”

The Bigger Picture: AI Needs a Staging Environment

Complete Discover reflects a larger shift happening across MarTech and RevOps: AI needs staging environments, not just production endpoints.

Just as modern data teams rely on dev, test, and prod environments, AI-driven enrichment demands a similar lifecycle. Tools that jump straight into Salesforce risk backlash when data quality slips or insights fail to translate into results.

By positioning Google Sheets as the “AI test kitchen” and Salesforce as the execution layer, Traction Complete is aligning AI enrichment with how operations teams already think about risk, governance, and scale.

As AI continues to expand what’s possible in go-to-market strategy, platforms that respect operational reality—not just innovation hype—may be the ones that actually stick.

Get in touch with our MarTech Experts.

The corrugated and folding carton industry isn’t known for speed. Quotes can take days. Estimating depends on internal handoffs. And sales teams often lose deals before pricing ever lands in a customer’s inbox.

Pakked believes that’s no longer acceptable.

The packaging tech startup has launched Maverick AI, positioning it as the industry’s first AI-powered estimating chatbot built specifically for corrugated and folding carton manufacturers. The product is designed to overhaul front-end sales workflows—cutting quote times from days to minutes while improving accuracy, consistency, and customer experience.

It’s a focused application of AI to a problem that’s plagued packaging manufacturers for decades: estimating friction.

Built by Insiders Who Know the Pain

Pakked’s credibility starts with its origin story.

Founded in 2023 by brothers Philip and Wesley Webb, the company is led by third-generation packaging operators who grew up inside box plants. Their family previously owned and operated Fleetwood-Fibre Packaging & Graphics in Southern California, giving the founders firsthand exposure to the inefficiencies that define traditional estimating processes.

Those experiences shaped Maverick’s development over the past 18+ months. Rather than retrofitting generic sales software, Pakked built a tool tailored to how packaging estimates actually work—materials, colors, quantities, CAD files, and constant revisions.

“The corrugated industry is ready for a change,” said Philip Webb, co-founder of Pakked. “Pakked is modernizing the front-end sales process for manufacturers while improving the customer experience through faster response times and technology built for today’s expectations—not yesterday’s systems.”

Maverick AI: A Digital Estimating Teammate

Maverick functions less like a form and more like a conversational coworker.

Sales and estimating teams can interact with the chatbot in natural language—running internal estimates, adjusting quantities, changing colors, creating multiple pricing scenarios, and refining details in real time. Instead of restarting the estimating process with every revision, teams iterate instantly.

Once finalized, Maverick generates fully branded quotes that can be downloaded or shared with customers within minutes. The experience mirrors how sales teams already think and communicate, but without the delays imposed by legacy systems.

This conversational approach reflects a broader MarTech trend: AI agents replacing rigid workflows with adaptive, context-aware interactions—especially in industries that have historically relied on manual processes.

Fixing the Quote-to-Hit Rate Problem

Speed is only part of the story.

In corrugated packaging, quote-to-hit rates typically fall below 20%, a figure that underscores how many opportunities die due to slow responses and internal bottlenecks. By the time a quote arrives, customers have often moved on.

Maverick directly targets that failure point. Unlike traditional estimating systems that depend on departmental availability and sequential handoffs, Maverick works 24/7, generating estimates instantly without waiting for internal responses.

By eliminating operational noise and hidden costs tied to manual workflows, Pakked argues Maverick can help manufacturers respond faster, reduce friction between teams, and capture opportunities that would otherwise be lost.

Trained on Each Plant’s Own Data

One of Maverick’s most important design choices is customization.

Each deployment is trained on a manufacturer’s own internal data and configured specifically for that box plant. That approach avoids the “one-size-fits-all” problem common in horizontal AI tools and helps ensure accuracy in highly variable production environments.

In testing, Pakked reports Maverick achieved up to 99.3% accuracy when benchmarked against existing legacy estimating systems currently in use across the industry.

For manufacturers wary of AI-driven pricing errors, that figure matters. Accuracy isn’t just a technical metric—it’s a trust requirement.

More Than Estimating: A Workflow Hub

Maverick AI also serves as the central hub of the broader Pakked platform.

The system brings together estimate requests, CAD files, artwork, and approvals into a single workflow, reducing the need to juggle email threads, shared drives, and disconnected tools.

Pakked is working toward enabling customers to place orders directly from approved quotes—a move that would extend automation beyond estimating and into the full order lifecycle.

If executed well, that could shift packaging sales from a fragmented, back-and-forth process to a more continuous, digital experience.

Why This Matters Now

Packaging manufacturers face increasing pressure from faster-moving competitors, rising customer expectations, and tighter margins. In that environment, front-end efficiency isn’t just an operational issue—it’s a growth lever.

Maverick AI reflects a growing pattern across industrial and B2B sectors: AI isn’t being used to replace craftsmanship, but to remove friction around it. By modernizing estimating—a historically slow and opaque process—Pakked is betting that speed, clarity, and responsiveness will become competitive advantages in a traditionally conservative industry.

For corrugated and folding carton manufacturers, that could mark the beginning of a long-overdue shift from manual bottlenecks to AI-assisted sales execution. 

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