Hospitals have long blamed bed shortages for overcrowding and delayed discharges. Kontakt.io argues the real issue lies elsewhere—and today it put a name to it. The company introduced Patient Flow Agent, a patient flow orchestration solution designed to reduce length of stay, eliminate discharge delays, and convert operational efficiency into measurable revenue gains.

At its core, Patient Flow Agent tackles a familiar but costly problem: during a patient’s journey from admission to discharge, hundreds of decisions are made with incomplete operational context. Those blind spots add up, often extending hospital stays by days. Kontakt.io’s new agent reframes the process by placing every key moment of care into a single, real-time operational view, enabling frontline teams to act faster and with greater confidence.

Turning Fragmented Data Into Coordinated Action

Patient Flow Agent runs on Kontakt.io’s Care Orchestration platform, which blends real-time location and care signals from RTLS with EHR data. The result is a continuously updated clinical and operational context that predicts next steps in a patient’s journey—rather than reacting after delays occur.

The agent identifies care progression interventions, tracks their outcomes, and automates actions that simplify and accelerate discharge workflows. For clinicians and care teams, that means fewer manual handoffs and less guesswork. For hospitals, it means smoother throughput and better utilization of scarce resources.

“Hospitals don’t have a bed problem; they have a patient flow orchestration problem,” said Philipp von Gilsa, CEO of Kontakt.io. “Patient Flow Agent turns fragmented data into coordinated real-time action using existing EHR interfaces and workflows, and surfaces time-critical interventions.”

Predictive Flow Across the Care Continuum

Unlike traditional throughput tools that focus narrowly on bed management, Patient Flow Agent models the full care continuum. It predicts patient journeys, care resource needs, bed availability, discharge timing, barriers, and post-discharge dispositions. Based on those insights, the system initiates interventions that free up beds sooner and dynamically redistribute staff and resources.

The timing is notable. U.S. hospitals are facing renewed strain from the ongoing influenza epidemic, with patient volumes surging and capacity stretched thin. In that environment, shaving even a fraction of a day off average length of stay can have outsized operational impact.

Kontakt.io claims Patient Flow Agent can reduce length of stay by full days, not hours—an assertion that, if borne out at scale, positions patient flow as one of healthcare’s most underleveraged levers for efficiency.

The Financial Case for Better Flow

The operational gains translate directly into financial performance. A recent study cited by Kontakt.io found that 22% of U.S. inpatient hospital days are not clinically necessary. For a typical 200-bed hospital, eliminating those avoidable days could unlock $4 million in annual cost savings and generate an additional $3 million in yearly revenue through improved capacity and throughput.

For hospital executives grappling with labor shortages, rising costs, and reimbursement pressure, Patient Flow Agent reframes patient flow as both a clinical quality issue and a revenue optimization opportunity.

Why This Matters Now

As healthcare systems increasingly adopt AI-driven decision support, Kontakt.io’s approach stands out for its focus on orchestration rather than alerts. Instead of adding another dashboard, Patient Flow Agent embeds predictive intelligence into existing workflows, aiming to reduce friction rather than add cognitive load.

If successful, the platform could help hospitals move from reactive capacity management to a system of continuous flow—where beds, staff, and patients move with fewer bottlenecks and better outcomes for all involved.

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For decades, healthcare’s biggest paradox has been this: clinicians know what works, guidelines are well established, yet millions of patients who qualify for evidence-based care are never identified in time. Qualified Health and Anthropic are betting that large-scale, governed AI can finally close that gap.

The two companies have launched what they describe as a landmark AI deployment across the University of Texas System (UT System)—one of the largest academic health networks in the U.S.—aimed at systematically identifying patients who meet guideline-based criteria and ensuring they are evaluated for appropriate, high-quality care. The initiative brings together Qualified Health’s clinical governance platform and Anthropic’s Claude AI models, applied across vast and complex clinical datasets.

Why Evidence-Based Care Still Falls Through the Cracks

The issue isn’t a lack of medical research. Clinical guidelines and appropriateness criteria have been refined over decades. The problem is operational reality. Determining whether an individual patient meets those criteria often requires painstaking chart review across fragmented EHRs, unstructured clinician notes, lab results, imaging, and historical records.

At population scale—millions of patients and petabytes of data—this work has historically been infeasible. The consequences are significant. Tens of millions of Americans who qualify for evidence-based care are never evaluated in time. In Texas alone, an estimated 4–6 million patients fall through the cracks each year, contributing to preventable complications, higher mortality, inequities in access, and mounting pressure on already strained clinicians.

Qualified Health and Anthropic argue that this is precisely the kind of problem modern AI is suited to solve—if deployed with the right safeguards.

How the System Works

Under the new deployment, Qualified Health’s AI system—powered by Claude—continuously analyzes clinical data across the UT System. It integrates information from multiple sources, parses complex and unstructured data, and applies validated clinical guidelines and appropriateness criteria to maintain a continuously updated, population-level view of care gaps.

Rather than replacing clinical judgment, the system surfaces patients who may warrant further consideration directly into existing care team workflows. Supporting clinical context is automatically assembled, allowing clinicians to review cases efficiently and make informed decisions without wading through fragmented records.

“Healthcare is one of the most demanding environments for AI,” said Eric Kauderer-Abrams, Head of Life Sciences at Anthropic. “It requires parsing vast amounts of unstructured clinical data while operating safely within strict governance frameworks. Claude can do that reliably, and when paired with Qualified Health’s platform and a visionary health system like the UT System, it creates the conditions to deploy advanced AI safely at scale.”

From Theory to Live Clinical Use

After extensive evaluation and testing, the system is now live at the University of Texas Medical Branch (UTMB), the first deployment site within the UT System. The initial focus is cardiology, an area where delayed identification can have serious consequences.

The system evaluates unified patient profiles against precise guideline-based criteria, covering everything from guideline-directed medical therapy and medication dosing to appropriate interventional treatments for heart failure and valvular disease. Importantly, appropriateness criteria are surfaced alongside recommendations, reinforcing quality and consistency in clinical assessment.

Early results suggest the approach is resonating with clinicians:

• Complex clinical data were successfully unified into comprehensive patient profiles

• Large cohorts of previously unrecognized, high-likelihood candidates were identified

• Clinician review showed high agreement with AI-generated outputs

• Care pathways for eligible patients were accelerated

For healthcare leaders, that last point may be the most compelling. Speed matters—not just in emergencies, but in reducing the slow, systemic delays that prevent patients from ever reaching the right point of care.

Augmenting Clinicians, Not Replacing Them

Qualified Health is careful to frame the system as an augmentation tool rather than an automated decision-maker.

“The challenge isn’t that we don’t know what works,” said Justin Norden, MD, MBA, MPhil, CEO of Qualified Health. “It’s translating decades of evidence and appropriateness guidance into consistent clinical practice at scale. The system is designed to augment, not replace, clinical judgment.”

What once required extensive manual chart abstraction and coordination across systems can now happen continuously, across entire populations. In effect, the AI handles the detection and synthesis work, allowing clinicians to focus on judgment, nuance, and patient interaction.

Scaling Across Texas—and Beyond Cardiology

Building on early success at UTMB, the platform is expanding across the UT System. By the end of 2026, additional deployments are planned across primary care, vascular, gastrointestinal, rheumatology, and neurology specialties.

That expansion aligns with broader system-level goals. According to Zain Kazmi, Chief Digital & Analytics Officer and Associate Vice Chancellor of Health Affairs at the UT System, the initiative is about more than a single AI use case.

“Rather than laying solutions on top of existing systems, we are building a new shared foundation across the UT System’s health enterprise that allows new AI deployments to be introduced with consistency, accountability, and long-term impact,” Kazmi said.

The deployment is also part of the UT REAL Health AI initiative, which emphasizes two priorities: expanding access to evidence-based treatment—particularly for underserved populations—and setting a new standard for safe, responsible AI in clinical environments.

Why This Matters for Healthcare AI

As health systems nationwide evaluate population-scale AI, the UT System deployment stands out for its scope and governance-first approach. Rather than experimental pilots or narrow point solutions, this initiative aims to operationalize evidence-based medicine across entire populations.

It’s also a signal moment for Anthropic, whose Claude models are increasingly being positioned for high-stakes, regulated environments. The project has already been highlighted in Anthropic’s public communications and at industry forums such as the J.P. Morgan Healthcare Conference, underscoring growing interest in AI that can move from promise to production.

If the results continue to scale, the partnership could offer a replicable blueprint for how health systems translate clinical evidence into consistent practice—without burning out clinicians or leaving patients behind.

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Middle-market professional services firms are under pressure from all sides: rising client expectations, tighter margins, talent constraints, and accelerating AI adoption. Strategy decks alone are no longer enough. Idea Innovate Consulting is betting that what firms need now is something far more operational—and far more accountable.

The firm has announced the launch of a new advisory platform designed specifically for middle-market services firms and their investors, spanning audit, tax and advisory, financial services, managed services, and legal services. The goal is straightforward but ambitious: help firms unlock revenue growth by turning organizational complexity into clear decisions and decisive action.

Unlike traditional consultancies, Idea Innovate positions itself as a partner that operates inside real-world constraints. The platform was built through a partner-led consortium of senior industry practitioners—leaders who have run firms, managed P&Ls, navigated regulatory pressure, and executed growth strategies firsthand.

Advisory Built for Execution, Not Presentations

At the core of Idea Innovate’s platform is a clear rejection of advisory models that stop at recommendations. Instead, the firm operates at the intersection of strategy and execution, working directly with leadership teams through live decision-making environments.

Rather than delivering static reports, the platform emphasizes:

• Live working sessions where decisions are made in real time

• Narrative briefs that clarify trade-offs and priorities

• Facilitated execution models that lock choices into action

This approach is designed to address a common failure point in professional services transformations: good strategies that never fully translate into operational reality.

“Clients need clarity, momentum, and outcomes,” said Nita Sanger, Founder and CEO of Idea Innovate. “We work live, in the room, helping leadership teams make decisions they can stand behind, with accountability that lasts.”

AI as an Accelerator, Not a Replacement

While AI is embedded into the platform, Idea Innovate is careful to frame technology as an enabler—not the centerpiece. The advisory model is human-led and AI-enabled, combining senior operator judgment with AI-driven tools that improve decision speed, execution discipline, and accountability.

AI capabilities are used to:

• Surface insights faster from complex business data

• Improve prioritization and sequencing of initiatives

• Track execution progress against defined outcomes

In a market saturated with AI-first promises, this positioning stands out. The platform is not about automating leadership decisions, but about equipping leadership teams to make better, faster, and more defensible choices—especially in high-stakes growth scenarios.

Designed for Middle-Market Realities

Middle-market firms often sit in an uncomfortable middle ground: too complex for lightweight advisory models, but without the scale or tolerance for the cost and abstraction of large consultancies. Idea Innovate’s platform is explicitly designed for this segment.

Its partner-led operating model brings hands-on support from leaders who understand:

• Regulatory and compliance pressures in professional services

• The economics of subscription models and platform-based growth

• The operational friction that slows execution in partner-driven firms

This practical orientation extends to the firm’s long-term vision. Idea Innovate aims to go beyond episodic advisory by co-creating solutions, building platforms, launching subscriptions, and delivering scalable, repeatable products alongside its clients.

Three Commitments That Define the Platform

The operating model is anchored around three core commitments that reflect the firm’s execution-first philosophy:

Real-time strategy and execution
Decisions are made live, with a focus on immediate action rather than deferred alignment.

Human-led, AI-enabled transformation
Technology amplifies expertise, while human context ensures decisions translate into business value.

Outcome-driven impact
Success is measured by results, with a clear emphasis on customer impact and revenue growth.

Together, these principles aim to address what many services firms struggle with most: moving from intent to impact.

A Broader Shift in Modern Advisory

Idea Innovate’s launch reflects a broader shift underway in the advisory and consulting market. As AI lowers the barrier to analysis and insight generation, differentiation is increasingly moving toward execution capability, accountability, and industry fluency.

For professional services firms navigating growth, consolidation, and rapid technological change, the value equation is changing. Advisory partners are being judged less on frameworks and more on whether they can help leadership teams make—and implement—hard decisions.

With its integrated delivery approach and operator-led model, Idea Innovate is positioning itself as part of this next wave of advisory—one focused on measurable outcomes rather than abstract transformation narratives.

Whether that model scales will depend on results, but the premise is clear: in a market overloaded with insight, decisive action is the new differentiator.

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Plume, best known for its cloud-managed Wi-Fi and smart home services for Internet Service Providers, is signaling a sharper go-to-market push as competition in broadband intensifies. The company has appointed Rebecca Stone as its new Chief Marketing Officer, bringing in a seasoned marketing executive with deep roots in networking, cloud, and service provider ecosystems.

Stone joins Plume with more than two decades of experience leading global marketing organizations across brand, product marketing, growth, and revenue strategy. Most recently, she served as Senior Vice President of Revenue Marketing and Customer Solutions Marketing at Cisco, where she helped steer marketing for the company’s massive $50 billion networking portfolio—a role that spanned media, integrated campaigns, demand generation, content, and marketing operations.

Why This Hire Matters Now

Plume’s timing is deliberate. ISPs are facing rising subscriber expectations, relentless price pressure, and a growing need to differentiate beyond raw connectivity. Wi-Fi performance, cybersecurity, analytics, and customer experience are no longer “nice to have”—they’re table stakes.

Stone’s mandate is to help Plume translate its expanding technical capabilities into clear, differentiated value for ISPs, particularly around:

• Intelligent Wi-Fi management

• Network telemetry and analytics

• Cybersecurity and customer care

• AI-powered insights and orchestration

That mission has become more urgent as Plume integrates Sweepr, a recent acquisition that adds AI-driven customer experience and engagement technology to its platform.

“In a market this crowded, clarity wins,” said Dan Herscovici, CEO of Plume. “Rebecca has built and scaled world-class marketing teams across cloud, networking, and service providers. Her ability to translate complex technology into differentiated value will be critical as we help partners stand out, grow, and compete.”

From Data to Decisions for ISPs

Plume’s competitive advantage lies in scale. Its cloud platform connects nearly half a billion devices, generating one of the industry’s richest datasets of real-world network and device telemetry across diverse home environments. The challenge—and opportunity—is turning that data into action.

Under Stone’s leadership, marketing will play a central role in reframing Plume not just as a Wi-Fi management vendor, but as a data- and AI-powered partner that helps ISPs:

• Improve subscriber confidence and retention

• Proactively identify and resolve network issues

• Deliver more personalized, insight-driven services

This positioning aligns closely with broader industry trends, where AI is increasingly shaping service delivery and customer engagement in broadband and networking.

A Track Record of Scaling Marketing Engines

Stone’s resume reads like a tour through some of the most influential names in networking and data infrastructure.

Before Cisco, she was Chief Marketing Officer at Meraki, where she:

• Doubled the global marketing team to 150 people

• Led a major rebrand and messaging overhaul

• Grew marketing-sourced pipeline contribution to 35%

Earlier, as VP of Marketing at LiveRamp, she helped scale the company from $20 million to more than $300 million in revenue in just four years, leading a combined marketing and sales organization of 70 people.

Her earlier roles at DataSift and Calix further cemented her familiarity with service provider dynamics, data platforms, and growth-stage technology companies.

Marketing as a Growth Lever in an AI Era

For Plume, Stone’s appointment underscores a broader shift: marketing is no longer just about awareness—it’s a strategic growth lever in an AI-driven market.

“I’ve spent my career focused on building innovative marketing teams that accelerate growth and drive customer success,” Stone said. “There’s nothing more valuable than hearing from your customers, learning from them, and turning those insights into action that propels their success.”

Her focus on customer empathy and insight-driven storytelling fits Plume’s ambition to engage ISPs more deeply and consistently across channels, especially as the company enters what it describes as its “next chapter”: one unified platform, stronger customer experience, and greater value extracted from data and AI.

The Bigger Picture

As broadband markets mature, differentiation is shifting away from speed claims and toward experience, intelligence, and operational efficiency. Vendors that can clearly articulate how AI and analytics translate into revenue growth and customer loyalty will have the edge.

By bringing in a marketing leader who has operated at scale across Cisco, Meraki, and LiveRamp, Plume is betting that clear messaging and disciplined go-to-market execution can be as decisive as technical innovation.

For ISPs navigating an increasingly complex and AI-shaped landscape, that clarity may be exactly what resonates.

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For years, B2B buyers have complained—quietly at first, then loudly—that getting a product demo feels harder than buying the product itself. Calendars don’t align. Sales engineers are stretched thin. And by the time a demo finally happens, buyer intent has cooled.

Saleo thinks it has a fix.

The AI-native demo platform has launched its AI Demo Agent, a fully conversational, always-on agent designed to deliver autonomous, multilingual product demos—without a human sales engineer on the call. The goal: eliminate time to first demo entirely, while giving presales teams their time back for deals that actually require human judgment.

It’s a bold claim, but one that taps directly into a growing pressure point across SaaS, MarTech, and B2B tech more broadly: presales has become a bottleneck at exactly the moment buying cycles demand speed.

A Demo Agent That Acts Like a Tenured Sales Engineer

Unlike static demo videos or scripted chatbots, Saleo’s AI Demo Agent is designed to run live, interactive product walkthroughs that adapt in real time based on buyer input.

The agent conducts discovery through natural conversation, responds to objections, and adjusts the demo flow based on use case—much like a seasoned sales engineer would. Buyers can ask questions, change direction mid-demo, or dig into specific features without restarting the experience.

One standout capability is True Co-browsing, which allows buyers to actively click through and explore the product themselves while the agent guides the experience. Instead of passively watching, prospects can interact directly with the interface—something that’s historically been difficult to scale without human involvement.

Under the hood, the agent relies on full-context reasoning trained on a company’s actual product, demo environments, and go-to-market messaging. That means it isn’t improvising based on generic AI knowledge; it’s operating from a product-specific understanding that mirrors how real demos are delivered internally.

Powered by Live™ Demo Data—Not Guesswork

What differentiates Saleo’s approach from many AI-powered sales tools is its reliance on Live™ demo data. Rather than interpreting screens visually or inferring product behavior, the agent has direct access to structured demo data that explains how features connect, what actions trigger what outcomes, and how data flows across the product.

In practical terms, this allows the AI Demo Agent to understand exactly what’s happening on screen and tailor the demo narrative accordingly—without hallucinating or misrepresenting functionality.

This matters in complex MarTech and B2B platforms, where a single incorrect claim during a demo can derail trust. By grounding the experience in live demo data, Saleo aims to solve one of AI’s biggest credibility challenges in sales environments.

A Presales Multiplier, Not a Replacement

Saleo is careful not to frame the AI Demo Agent as a replacement for presales teams. Instead, it positions the agent as a presales multiplier—handling repetitive, early-stage walkthroughs and qualification demos automatically, while freeing sales engineers to focus on high-value, strategic conversations.

For revenue teams, the impact is immediate:

• Instant demo coverage for inbound leads

• Faster handoffs between marketing, SDRs, and sales

• Continuous qualification based on real demo engagement

• Rich analytics capturing buyer questions, objections, and intent signals

Built-in demo analytics surface insights that typically get lost after live calls, giving sales teams better context before engaging directly. In an era where first-party buyer signals are increasingly scarce, demo-level intent data could become a meaningful differentiator.

Why Demo Automation Is Having a Moment

The launch lands amid a broader shift in how B2B buyers want to engage. Self-serve product experiences, once limited to PLG startups, are now table stakes across midmarket and enterprise software. At the same time, AI-driven automation is pushing deeper into revenue workflows—beyond email and chat, into core sales motions.

Competitors in the demo automation space have focused on sandbox environments or guided tours, but Saleo’s conversational, autonomous approach suggests the category is moving toward AI-led presales execution, not just enablement.

If successful, tools like this could reshape how top-of-funnel sales operates—turning demos from a scheduling problem into an always-available product experience.

What’s Next for Saleo

To support the launch, Saleo is hosting a webinar on January 22 at 1 PM ET, titled “Market Forces Driving the Next Wave of Demo Automation.” Founders Justin McDonald and Daniel Hellerman will discuss how buyer behavior, AI maturity, and sales efficiency pressures are converging to reshape demo technology.

The company is also hitting the road with a six-city tour alongside the PreSales Collective, hosting executive dinners, solution engineer training sessions, and community events in New York, London, Boston, Chicago, Atlanta, and Dallas.

It’s a signal that Saleo isn’t just shipping a feature—it’s betting on demo automation as a category-defining shift.

And if buyers truly can “see faster” without waiting for a calendar invite, that bet may pay off.

Get in touch with our MarTech Experts.

Aesthetic practices don’t suffer from a lack of software. They suffer from too much of it.

EMRs for records. Separate tools for marketing. Another system for patient communication. Yet another for lead management. The result is fragmented workflows, missed revenue opportunities, and staff spending hours toggling between dashboards instead of engaging patients.

Podium wants to change that equation.

The AI-powered customer communications company has launched its AI Operating System (OS) for Aesthetics, positioning it as the first unified platform built specifically for medspas and aesthetic clinics. At the center of the system is Avery, Podium’s AI Employee—an always-on agent designed to handle patient engagement, scheduling, and follow-ups autonomously.

The pitch is clear: stop managing software, and let software manage the work.

Beyond EMR: From System of Record to System of Growth

Traditional EMRs are built to document what already happened. Podium’s AI Operating System is designed to influence what happens next.

The platform integrates EMR functionality with patient communications, marketing automation, and lead management—bringing what are typically three or more disconnected systems into a single operating layer. Podium estimates that practices waste an average of eight hours per week switching between platforms, time that could otherwise be spent on patient care or business development.

More importantly, disconnected systems mean disconnected data—making it harder to respond quickly to leads, personalize outreach, or track the true revenue impact of marketing efforts.

Podium’s approach reflects a broader MarTech trend: vertical-specific operating systems that prioritize growth, not just compliance or record-keeping.

The AI Shift: Software That Does the Job

What Podium is really selling isn’t consolidation—it’s automation at a new level.

“The AI Operating System performs the job itself,” said Jason Brand, Director of Product, MedSpa at Podium. Instead of staff using tools to respond to patients, book appointments, or chase leads, Avery does it autonomously.

This marks a shift from what Brand calls “static systems of record” to active systems of agents—software that doesn’t wait for human input but acts continuously on behalf of the business.

That framing aligns closely with where AI-powered MarTech is heading. As generative AI matures, vendors are moving beyond copilots toward fully delegated workflows, especially in high-volume, time-sensitive customer interactions.

Avery: An AI Employee With Full Practice Visibility

The core differentiator of Podium’s AI OS is the depth of Avery’s system access.

Rather than operating as a narrow chatbot, Avery has complete visibility into calendars, patient histories, services, inventory, provider schedules, and communication channels. That context allows it to act less like an assistant—and more like a trained front-desk employee.

Avery can autonomously:

• Respond to inbound leads from web forms, texts, calls, and social channels in under two minutes, compared to an industry average of two hours

• Book appointments directly onto provider calendars, accounting for room availability, equipment, and staff schedules

• Manage the patient journey end-to-end, including nurturing unbooked leads, sending intake forms and reminders, requesting reviews, and delivering post-care instructions

With Avery 2.0, practices can also customize and coach the AI to reflect their clinic’s tone, workflows, and playbooks—addressing one of the biggest concerns around AI adoption in patient-facing environments: brand and voice consistency.

Why Speed Matters More Than Ever in Aesthetics

In aesthetics, speed isn’t a nice-to-have—it’s a revenue driver.

Leads often come from high-intent channels like paid social or local search, and response time directly impacts conversion. If a practice takes hours to respond, patients simply move on to the next provider.

By responding within minutes, Avery turns inbound interest into booked appointments before intent fades. That capability mirrors what high-performing revenue teams aim for in B2B—but applied to a consumer-facing, appointment-driven vertical.

It’s also a reminder that AI’s biggest near-term value isn’t creativity—it’s responsiveness at scale.

Measurable Impact, Not Just Automation Theater

Podium is backing its claims with early performance data.

According to a recently released OpenAI case study, customers using Podium’s AI agents saw, on average:

• A 45% increase in lead conversion

• A 30% increase in annual revenue

Those numbers won’t apply uniformly across every practice, but they highlight why AI-driven operating systems are gaining traction: they connect faster responses directly to revenue outcomes.

For clinics struggling to hire and retain front-office staff—or simply looking to do more with lean teams—the ROI argument is hard to ignore.

As Victoria Murillo of ZO Skin Centre Dallas put it, Podium’s AI has improved response times while freeing staff to focus on “the people in the room,” not the inbox.

The Bigger Picture: Vertical AI Platforms Are the Next MarTech Wave

Podium’s AI Operating System fits into a larger shift across MarTech and vertical SaaS: horizontal tools are giving way to industry-specific AI platforms.

Rather than bolting AI onto generic software, vendors are embedding agents directly into workflows where speed, context, and automation matter most. In healthcare-adjacent verticals like aesthetics—where compliance, personalization, and customer experience intersect—that approach may prove especially powerful.

If successful, Podium’s model could set expectations for what “modern practice management” looks like: not a dashboard, but a digital employee that never clocks out.

Get in touch with our MarTech Experts.

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.

Get in touch with our MarTech Experts.

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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