AI sales agents are getting smarter. The data they rely on? Not always.

People.ai today announced a Model Context Protocol (MCP) integration for its SalesAI Platform, aiming to solve one of revenue AI’s biggest problems: incomplete and inaccurate data. The integration allows revenue teams to connect AI agents—including Claude, Microsoft Copilot, and ChatGPT—directly to People.ai’s Answer Platform, which unifies structured CRM data and the unstructured reality of sales activity.

In plain terms, sales teams can now ask pipeline questions inside the AI tools they already use—and get answers grounded in both CRM records and what’s actually happening in emails, meetings, and calls.

The AI Revenue Problem: Garbage In, Confident Answer Out

Enterprise AI adoption is accelerating. Gartner predicts that 33% of enterprise software will include agentic AI by 2028. Revenue teams are already using AI agents to forecast pipelines, identify risks, and prioritize opportunities.

But there’s a catch.

Research suggests 80% of CRM data is inaccurate. Reps forget to log calls. Opportunity stages lag reality. Buying committees evolve without updates. When AI models analyze that incomplete data, they can produce answers that sound authoritative—but aren’t.

For sales leaders asking high-stakes questions like:

• Where is risk building in my pipeline?

• Which deals are stalling?

• Who actually has buying power?

A wrong answer doesn’t just skew a dashboard. It can cost deals.

People.ai’s new MCP integration is designed to address that foundational flaw by expanding what AI agents can “see.”

What the MCP Integration Actually Does

Through its Answer Platform, People.ai automatically collects and connects:

• Emails

• Meetings

• Chats

• LinkedIn interactions

• Call transcripts

• CRM opportunity data (stage, close date, deal size)

Its patented matching technology links unstructured activity data to the correct CRM accounts, contacts, and opportunities. NLP-based filtering removes sensitive content while preserving business context.

With MCP, that unified data layer can now be accessed directly from external AI tools. Instead of exporting reports or toggling between systems, revenue teams can query their preferred AI assistant and receive responses enriched with full activity intelligence.

This is less about adding another dashboard and more about embedding revenue intelligence into existing AI workflows.

From Activity Capture to Composable AI Infrastructure

Many activity capture tools rely on basic email or domain matching. That approach can create data duplication or incorrect associations—poisoning the AI models downstream.

People.ai is differentiating on data fidelity. Its platform enriches structured CRM records with persona data, buying power insights, and historical win rates. That enables AI agents to evaluate not only who is in the deal—but what they’re actually saying.

Jason Ambrose, CEO of People.ai, framed it succinctly: revenue teams don’t need more dashboards; they need complete answers at decision time.

Andrew Brown, Chief Revenue Officer at Red Hat, tied the announcement to a broader enterprise AI shift. Red Hat is orchestrating a company-wide move toward becoming an AI-enabled enterprise, and Brown highlighted the value of open architecture and MCP in building composable AI infrastructure. According to him, the approach has helped improve win rates by more than 50 percent.

That comment underscores a key trend: enterprises are moving away from siloed AI tools toward interoperable systems where AI agents can reason across unified data layers.

Why Model Context Protocol Matters

The Model Context Protocol (MCP) is gaining traction as a way to standardize how AI models access external systems. Rather than simply passing static datasets, MCP enables dynamic exchange of context between tools.

In this case, People.ai’s AI model doesn’t just send raw records to Claude or Copilot. It exchanges structured intelligence, enabling deeper reasoning instead of data dumps.

That distinction matters. Modern AI agents thrive on context-rich inputs. By providing both structured CRM fields and conversational insights, People.ai is aiming to give those agents a more complete understanding of pipeline health.

Competitive Context: Revenue Intelligence Gets Agentic

The revenue intelligence space has evolved from basic activity tracking to predictive analytics. Now it’s entering an agentic phase, where AI agents autonomously surface risks, suggest next actions, and answer complex business questions.

But as AI tools proliferate, integration becomes the bottleneck.

Rather than forcing teams into a proprietary interface, People.ai is leaning into accessibility:

• No additional logins

• No context switching

• AI queries within existing tools

• Answers enriched with complete activity data

For enterprises standardizing on Copilot, ChatGPT, Slack bots, or internal AI agents, that flexibility could be a strategic advantage.

The Bottom Line

AI in revenue operations is only as strong as the data foundation beneath it. And that foundation has historically been shaky.

With its MCP integration, People.ai is positioning itself not as another AI layer—but as the intelligence substrate powering enterprise sales agents. By bridging structured CRM data with the messy, unstructured reality of customer engagement, the company is attempting to close a critical gap in agentic revenue workflows.

As AI becomes embedded in more enterprise decision-making, the winners won’t just be the tools that answer questions fastest. They’ll be the ones that answer them correctly.

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For years, AI content tools have promised speed. What they’ve rarely delivered is brand discipline.

Now, Hightouch wants to change that. The company announced the launch of Content Assembly, its first dedicated content capability, designed to help marketers generate on-brand campaign materials using the layouts, assets, and brand guidelines they already have.

The move expands Hightouch’s broader “agentic marketing” strategy—where AI agents don’t just generate text, but operate across data, orchestration, and now content. And unlike generic AI writing tools that start from a blank page, Content Assembly starts with your actual marketing infrastructure.

AI That Knows Your Brand (Not Just the Prompt)

Content Assembly is built around a simple premise: content production doesn’t need to restart from scratch every time.

According to Tejas Manohar, Co-CEO and Co-Founder of Hightouch, the tool is designed to understand approved layouts, imagery, brand rules, and historical campaigns so outputs are consistent and compliant from the start.

Here’s how it works in practice:

A marketer describes a campaign—say, a seasonal promotion or product launch. The platform then:

• Selects the optimal layout from existing templates

• Pulls relevant creative assets from connected systems

• Reviews past campaigns to identify effective messaging patterns

• Applies brand guidelines and business objectives

• Generates a ready-to-edit campaign draft

Teams can refine the output using prompts or manual editing tools. A built-in compliance layer, powered by custom agents trained on legal and brand standards, performs an initial review before export. From there, campaigns can be pushed directly into channel platforms or downloaded as production-ready HTML.

It’s less “AI writer” and more “AI production coordinator.”

Why This Matters: AI Fatigue Meets Brand Risk

The launch lands at a time when marketers are dealing with two conflicting pressures:

1. Produce more content, faster, across more channels

2. Maintain brand integrity and legal compliance

Generative AI has dramatically increased content velocity—but often at the cost of consistency. Generic AI tools don’t inherently understand a company’s brand book, compliance requirements, or historical campaign performance.

That gap has created friction. Legal reviews slow things down. Brand teams get nervous. And design teams get flooded with variant requests for personalization efforts.

Content Assembly aims to solve that by grounding outputs in pre-approved layouts and assets. If the AI isn’t inventing new visual structures or untested messaging formats, review cycles get shorter. Legal and brand teams spend less time redlining. Designers aren’t pulled into every iteration.

For enterprises scaling personalization across regions and audiences, that’s not a minor tweak—it’s operational leverage.

Reusability as a Growth Lever

One of the most interesting aspects of Content Assembly is its focus on asset reusability.

Large marketing organizations often sit on vast libraries of approved creative stored in cloud data warehouses, digital asset management systems (DAMs), and design tools. The bottleneck isn’t content scarcity—it’s discoverability and assembly.

Hightouch’s platform integrates directly with:

• Cloud data warehouses

• DAMs

• Design tools

• Broader martech platforms

That integration layer provides context: what campaigns performed well, which layouts are approved, what imagery aligns with brand guidelines, and how messaging patterns evolved.

Instead of AI generating in isolation, it generates within a company’s marketing system of record.

In a market where competitors often pitch AI as an autonomous creative engine, Hightouch is positioning its approach as structured and governed—AI with guardrails, not freeform improvisation.

The Bigger Play: Agentic Marketing

Content Assembly builds on Hightouch’s broader Agentic Marketing Platform, which aims to give marketers AI agents that operate across:

• Data

• Campaign orchestration

• Now content production

The “agentic” framing reflects a broader industry shift. Rather than standalone tools for writing, segmentation, or reporting, vendors are racing to create AI agents that act across workflows.

But that ambition raises governance concerns. When AI touches customer data, campaign execution, and creative assets, the risk of misalignment—or compliance missteps—increases.

Hightouch’s pitch is that governance isn’t an afterthought. Because its AI is grounded in connected enterprise systems and pre-approved frameworks, it can act without compromising brand integrity.

Whether that promise holds at scale will depend on implementation. But the strategic direction is clear: AI as an extension of the marketing stack, not a detached content generator.

Competitive Landscape: From Writers to Workflow AI

The AI content market is crowded with tools optimized for speed and volume. What’s emerging now is a second phase—AI embedded directly into enterprise marketing workflows.

In that environment, differentiation hinges on:

• Deep integrations

• Compliance automation

• Brand-safe personalization

• Production readiness

Content Assembly appears designed to compete on those axes, rather than on raw generative capability.

If successful, it could help enterprises close the gap between personalization ambitions and production capacity—without expanding headcount.

The Bottom Line

AI has made it easy to generate content. It hasn’t made it easy to generate the right content.

With Content Assembly, Hightouch is betting that marketers don’t need another blank-page generator. They need a system that understands their brand, assets, and history—and assembles campaigns accordingly.

In an era where personalization demands are rising but brand risk tolerance is not, that distinction could prove more valuable than another AI copy button.

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Across Europe, data sovereignty has shifted from policy buzzword to boardroom mandate. Now, Genesys is betting that the next phase of AI-powered customer experience in the EU will depend less on flashy automation—and more on where the data lives.

The company announced plans to make its Genesys Cloud platform available on the AWS European Sovereign Cloud, Amazon’s new independent cloud environment built specifically for Europe. As a launch partner, Genesys expects to be among the first experience orchestration providers operating within the new sovereign region.

The move is designed to help organizations pursue AI-driven innovation while meeting strict requirements for data residency, governance, and operational control—especially across regulated industries in the European Union.

Why This Matters Now

European regulators have been tightening the screws on data governance for years. Between the General Data Protection Regulation (GDPR), the Digital Operational Resilience Act (DORA), and national-level requirements such as Germany’s C5 cloud compliance framework, companies operating in finance, healthcare, government, and critical infrastructure are under mounting pressure to modernize—without losing control of sensitive data.

According to a Digital Sovereignty Report conducted by Genesys with AWS and PAC, 88% of European business leaders say driving innovation without compromising digital sovereignty is a core concern.

In practical terms, that means:

• Keeping customer data within EU borders

• Ensuring access is governed under EU jurisdiction

• Reducing exposure to extraterritorial legislation

• Avoiding operational dependencies that could introduce risk

For AI-powered systems—especially those handling voice recordings, customer histories, biometric data, and automated decision-making—those requirements are not trivial.

What Genesys Is Actually Delivering

The planned Genesys Cloud European Sovereign region will run entirely on infrastructure located within the EU under the AWS European Sovereign Cloud framework.

That gives organizations:

• Full access to Genesys Cloud’s AI-powered experience orchestration tools

• EU-only data residency

• Strict access controls aligned with European governance requirements

• EU-based security, services, and support teams

In other words, companies won’t need to choose between advanced AI capabilities and regulatory compliance. The platform aims to offer both.

Olivier Jouve, Chief Product Officer at Genesys, put it plainly: data sovereignty is no longer optional for European organizations deploying AI at scale. By expanding deployment models to include AWS’s sovereign cloud, Genesys is trying to remove a key friction point in enterprise AI adoption.

The Bigger Industry Trend: Sovereign AI

This announcement isn’t happening in isolation. Sovereign cloud and sovereign AI initiatives are accelerating across Europe as governments and enterprises seek alternatives to globally centralized infrastructure models.

Cloud providers are responding with regionally controlled architectures designed to:

• Limit cross-border data flow

• Provide transparent governance structures

• Align with EU legal frameworks

For customer experience platforms, this shift is especially significant. Modern CX systems process massive volumes of conversational data across voice, chat, email, and messaging channels. When AI models analyze that data for automation, personalization, or predictive routing, regulatory scrutiny increases.

IDC Research Director Oru Mohiuddin called digital sovereignty a “foundational requirement” for cloud and AI adoption in Europe. From a market perspective, this suggests that vendors unable to provide sovereign deployment options may face competitive disadvantages in regulated sectors.

Competing in a High-Stakes CX Market

The global experience orchestration space is crowded, with providers racing to layer generative AI, agentic automation, and predictive analytics into their platforms. But in Europe, compliance capability is becoming a core product differentiator.

Genesys Cloud currently operates across 21 AWS Regions worldwide. The addition of a European Sovereign deployment model extends that footprint into a new category: infrastructure designed specifically to minimize jurisdictional ambiguity.

For public sector agencies and regulated enterprises, that distinction could be decisive. Many modernization projects stall not because of lack of technology—but because of legal uncertainty.

By positioning itself as an early launch partner within the AWS European Sovereign Cloud, Genesys is signaling that it intends to compete aggressively for Europe’s most compliance-sensitive customers.

Compliance Credentials: More Than a Checkbox

Genesys emphasizes that its cloud platform aligns with global and regional standards, including:

• SOC 2 Type 1

• ISO/IEC 27001, 27017, 27018, 27701

• GDPR

• DORA

• Germany’s C5 framework

While compliance certifications are now table stakes in enterprise SaaS, combining those frameworks with sovereign infrastructure may help organizations reduce risk assessments and procurement friction.

For IT and risk leaders, fewer red flags in due diligence can translate into faster deployment cycles.

Timing and Availability

The Genesys Cloud European Sovereign region is expected to become available during the company’s fiscal Q2, between May 1 and July 31, 2026.

If delivered on schedule, it will arrive at a time when many European enterprises are reassessing their AI roadmaps under tightening regulatory oversight.

The Bottom Line

AI-powered customer experience isn’t slowing down in Europe—but it’s evolving under stricter governance expectations. The race is no longer just about smarter bots or faster routing. It’s about controlled innovation.

By aligning with the AWS European Sovereign Cloud, Genesys is making a calculated move: bring advanced AI orchestration into environments where sovereignty, transparency, and jurisdictional clarity are non-negotiable.

In today’s European enterprise landscape, that may be the real competitive edge.

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Enterprise AI is easy to demo. It’s harder to deploy in industries where regulators, auditors, and risk officers are watching every move.

That’s the problem Kingland Systems aims to solve with its new applied AI suite, built on the Kingland Cloud & AI platform. The company, long known for enterprise data and regulatory software, is introducing an orchestration layer designed to embed AI directly into document-heavy workflows across public accounting, banking and capital markets, and insurance.

The pitch isn’t flashy generative AI for chat interfaces. It’s something more pragmatic: automating high-stakes, compliance-driven processes without breaking governance controls.

AI With Guardrails, Not Guesswork

At the center of the announcement is the Kingland Cloud & AI platform, which layers orchestration, document intelligence, structured data, and configurable workflows on top of Kingland’s existing regulatory-grade data foundation.

The goal: enable firms to deploy AI quickly across high-impact use cases—without sacrificing auditability, security, or process controls.

That positioning matters. Many enterprises remain cautious about introducing AI into regulated workflows. Hallucinations, opaque decision logic, and uncontrolled data flows are non-starters in environments governed by independence rules, capital requirements, or insurance compliance standards.

Kingland’s approach emphasizes controlled deployment. Rather than offering single-purpose AI tools, the platform is designed as a scalable framework that can evolve as models and use cases mature. For organizations wary of AI sprawl, that controlled upgrade path could be as important as the automation itself.

Public Accounting: Automating Independence Checks

One of the first applied AI use cases targets public accounting firms—a sector where independence and conflict-of-interest rules are both strict and operationally burdensome.

Traditionally, professionals manually review brokerage statements to identify financial interests and cross-check them against restricted lists. The process is time-intensive and prone to human error.

Kingland’s platform automates that reading process. Using document intelligence, it extracts financial holdings from brokerage statements and compares them against restricted entity lists to flag potential independence issues.

The platform also addresses another complex pain point: identifying related entities from intricate corporate structure documents. By extracting client hierarchy information, firms can more effectively detect conflicts and maintain compliance with independence standards.

In an industry where audit failures can carry reputational and regulatory consequences, reducing manual oversight without compromising control is a significant proposition.

Banking and Capital Markets: Tackling Private Credit Complexity

In banking and capital markets, the same AI orchestration layer is applied to private credit and client relationship documentation.

Private credit agreements are dense, often bespoke documents packed with critical data points—loan terms, payment schedules, collateral details, related parties. Extracting and structuring that data manually slows onboarding and risk monitoring.

Kingland’s AI solutions can read and extract these elements automatically, enabling faster processing and more accurate data capture. The structured outputs can then feed downstream risk models, compliance checks, and operational dashboards.

For capital markets firms grappling with increased regulatory scrutiny and tighter margins, automation here isn’t just about speed—it’s about visibility. More timely data extraction supports proactive risk monitoring instead of reactive remediation.

Insurance and Beyond: A Platform Play

While the announcement highlights accounting and banking use cases, the architecture is built to extend across insurance and other regulated verticals.

The key differentiator is the orchestration layer. Instead of deploying isolated AI models to solve one document type at a time, Kingland provides a framework that integrates document intelligence with enterprise data and configurable workflows.

This platform-first strategy mirrors broader enterprise software trends. Companies increasingly want AI capabilities embedded into existing systems of record, not layered on as experimental side tools.

By anchoring AI in its established regulatory software stack, Kingland is effectively telling customers: you don’t need a separate AI vendor to modernize your compliance operations.

Competing in a Crowded AI Landscape

The enterprise AI market is saturated with point solutions promising automation. What differentiates vendors increasingly is governance.

Regulated industries have unique constraints:

• Auditability requirements

• Data residency and security mandates

• Model explainability expectations

• Strict change management processes

Kingland’s regulatory heritage gives it credibility in these areas. Its applied AI solutions are less about AI novelty and more about operational integration within controlled environments.

That could resonate as organizations shift from experimentation to scaled deployment. Many enterprises have already piloted AI tools; the next phase is embedding them into core workflows without triggering compliance alarms.

Why This Matters Now

AI adoption in regulated sectors is entering a new phase. Early enthusiasm is giving way to pragmatic evaluation: where does AI truly reduce manual effort, improve data quality, and enhance oversight?

Kingland’s focus on document-heavy processes is strategic. These workflows are:

• High volume

• Labor intensive

• Error prone

• Critical to regulatory compliance

Automating them delivers measurable efficiency gains while improving consistency and traceability.

Moreover, by combining document intelligence with structured data and configurable workflows, the platform addresses a common failure point in AI projects: outputs that aren’t operationalized. Extracted data is only useful if it feeds actionable systems.

From Automation to Augmentation

Kingland positions its applied AI suite as a way to free professionals from repetitive document review and enable them to focus on higher-value analysis and decision-making.

That framing aligns with the broader narrative around AI augmentation rather than replacement. In public accounting, banking, and insurance, human oversight isn’t optional. The opportunity lies in reallocating expert attention from mechanical extraction tasks to strategic judgment calls.

If the platform delivers on faster processing, improved accuracy, and enhanced risk monitoring, it could offer a practical blueprint for AI adoption in compliance-driven industries.

In a market awash with AI promises, Kingland’s announcement stands out for its restraint. It’s not promising a reinvention of enterprise operations—just a more intelligent way to handle the documents that already define them.

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Datacor is kicking off 2026 with more than a routine product refresh. The company’s Winter 2026 Product Release marks its first major update since consolidating its portfolio of process manufacturing, chemical distribution, and engineering software under a single Datacor brand—and it signals a clear shift toward AI-infused, cross-platform cohesion.

For customers juggling regulatory complexity, volatile supply chains, and margin pressure, the message is straightforward: more automation, tighter workflows, and intelligence embedded where operational friction tends to hide.

A Unified Platform, Not a Patchwork

Datacor’s rebrand and portfolio unification were about more than logos. The company historically operated a collection of specialized solutions tailored to niche segments—process manufacturers, chemical distributors, engineering teams. The Winter 2026 release is the first tangible product milestone that shows what integration looks like in practice.

Instead of isolated upgrades, Datacor is positioning this as a coordinated step toward centralized data, shared analytics, and AI-driven automation across functional domains.

In an era where many industrial software vendors are stitching together acquisitions with loose integrations, Datacor appears intent on tightening the seams. The Winter 2026 release leans heavily into cross-functional intelligence rather than siloed feature enhancements.

AI Moves Into Core Workflows

The headline theme is AI-driven automation—though not in the generative AI, chatbot-everywhere sense that dominates SaaS headlines. Datacor’s approach is more operational and grounded.

The update introduces AI-backed automation across:

• Financial workflows

• Sales and customer management

• Manufacturing operations

• Asset intelligence

These capabilities are supported by centralized data and analytics, aimed at improving visibility, consistency, and accuracy across departments.

For process manufacturers and chemical distributors, where margins are often thin and compliance burdens high, workflow inefficiencies can quickly cascade into cost overruns. Embedding AI into financial reconciliation, demand forecasting, asset tracking, and production scheduling could reduce manual intervention and decision latency.

That’s particularly relevant as industrial firms grapple with workforce constraints. Skilled labor shortages in manufacturing and engineering have made automation less about convenience and more about continuity.

Animal Nutrition: Sustainability Meets Formulation

One of the more specialized—but strategically important—enhancements lands in Datacor’s animal nutrition solutions.

The Winter 2026 release integrates formulation and sustainability capabilities, giving users visibility into environmental impact alongside cost and performance metrics. In practical terms, that means balancing feed efficiency, input costs, and carbon or environmental considerations within a unified workflow.

This aligns with broader industry pressure. Agricultural and feed producers face growing scrutiny from regulators and downstream food brands around sustainability metrics. By embedding environmental visibility directly into formulation tools, Datacor positions itself to help customers operationalize sustainability rather than treat it as an afterthought.

It’s a sign that ESG considerations are becoming native features in industry-specific software—not bolt-ons.

Engineering: Faster Simulation, Tighter Collaboration

Engineering software sees performance-focused enhancements in this release, particularly around process simulation and modeling.

Datacor says updates improve the speed and accuracy of modeling, supporting design and analysis from R&D through production operations. In process industries—chemicals, specialty manufacturing, and related sectors—simulation accuracy directly impacts product quality, safety, and time to market.

The emphasis on collaboration suggests tighter integration between engineering and operational teams. That’s notable because digital transformation efforts in industrial sectors often stall at the handoff point between design and execution. If Datacor can smooth that transition through shared data models and workflows, it strengthens its value proposition beyond individual departments.

The Competitive Context

Industrial software is undergoing its own AI reckoning. Enterprise vendors across ERP, supply chain, and PLM markets are embedding predictive analytics, automation, and generative interfaces into legacy systems.

Datacor’s Winter 2026 release doesn’t attempt to reinvent the category. Instead, it focuses on practical AI applications within the operational realities of process manufacturing and chemical distribution.

That’s a defensible strategy. While enterprise giants chase horizontal AI platforms, specialized vendors like Datacor can differentiate by tailoring intelligence to domain-specific pain points—regulatory tracking, formulation optimization, production scheduling, and asset lifecycle management.

The unification under one brand also signals a response to market consolidation. Customers increasingly prefer fewer vendors with deeper, more integrated ecosystems. Fragmented toolsets add integration costs and governance headaches.

By aligning its offerings under a cohesive architecture, Datacor is effectively telling customers: you don’t need five vendors to modernize your industrial stack.

Why It Matters Now

The timing is significant. Industrial sectors face a convergence of challenges:

• Increasing regulatory scrutiny

• Sustainability mandates

• Supply chain volatility

• Talent shortages

• Digital transformation pressure

AI-driven workflow automation addresses all five—at least in theory. Reducing manual reporting lowers compliance risk. Centralized analytics improves supply chain visibility. Intelligent scheduling offsets labor constraints. Sustainability dashboards support reporting mandates.

Tom Jackson, Datacor’s president, frames the release as a step toward helping organizations “operate with greater clarity, scale more effectively, and prepare for what’s next.” While that language is familiar in tech announcements, the substance lies in whether centralized intelligence and cross-portfolio automation deliver measurable gains in efficiency and cost control.

From Integration to Intelligence

What makes the Winter 2026 release noteworthy isn’t any single feature. It’s the structural shift toward portfolio-wide intelligence.

First came brand unification. Now comes functional unification.

If Datacor continues to align data models, analytics engines, and automation frameworks across its solutions, it could evolve from a collection of industry tools into a vertically integrated industrial software platform.

For process manufacturers and chemical distributors—industries often underserved by mainstream SaaS platforms—that’s a meaningful development.

The Winter 2026 release suggests Datacor is less interested in flashy AI headlines and more focused on operational AI embedded in everyday workflows. In industrial environments, that may be exactly the right bet.

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Two decades ago, building an online form meant calling a developer—or becoming one. Today, it often means dragging and dropping fields in a browser. That shift is part of the legacy of Jotform, which this week marks its 20th anniversary with numbers that underscore its evolution from scrappy form builder to full-fledged workflow automation platform.

Founded in 2006, Jotform set out to simplify online form creation. In 2026, it counts more than 35 million users worldwide, operates across 190+ countries, supports over 40 industries, and processes roughly $2 billion annually through payment forms. The company says revenue has grown 248% since 2021, reflecting demand for no-code automation tools as organizations look to streamline operations without adding developer headcount.

For a product that started with a narrow focus—forms—that’s a notable expansion. And it mirrors a broader industry trend: the rise of no-code and low-code platforms as foundational infrastructure for digital business.

From Form Builder to Workflow Backbone

Jotform’s early differentiator was accessibility. Before SaaS form builders were ubiquitous, collecting data online typically required custom code. Jotform abstracted that complexity, giving non-technical users a visual interface for building forms and embedding them on websites.

Over the past 20 years, the company has layered on features that move it well beyond simple data capture:

• Advanced form logic and conditional workflows

• Compliance-ready solutions for regulated industries

• Remote and touchless features introduced during the COVID-19 pandemic

• A growing suite of AI-assisted tools designed for end-to-end workflow automation

Today, Jotform positions itself less as a “form builder” and more as a digital workflow foundation. That’s a competitive repositioning in a market crowded with platforms like Salesforce, HubSpot, and other SaaS providers that increasingly bundle forms into larger CRM and marketing automation stacks.

What distinguishes Jotform is its no-code-first philosophy. Rather than building outward from a CRM core, Jotform builds around data intake and workflow orchestration—then integrates outward.

The Integration Play: Payments, Productivity, and Platforms

A major pillar of Jotform’s growth has been third-party integrations. The platform connects with tools such as Google Drive, Dropbox, Salesforce, HubSpot, Mailchimp, Microsoft Teams, and Slack, allowing form submissions to flow directly into downstream systems.

That interoperability is critical in today’s fragmented SaaS environment, where few enterprises rely on a single platform. Instead of forcing customers into a closed ecosystem, Jotform acts as connective tissue between systems.

Payments are another differentiator. The company says it supports the largest collection of payment processing integrations in the industry, enabling billions of dollars in transactions to flow through its forms. In 2026 alone, Jotform reports approximately $2 billion collected annually via payment forms.

For SMBs, nonprofits, and educational institutions, that means a lightweight alternative to building custom checkout systems. For enterprises, it offers a fast way to embed transactional capabilities into digital workflows without launching a full e-commerce overhaul.

AI Enters the Workflow

If the first decade was about digitizing forms, and the second about expanding into workflows, the third appears to be about intelligence.

Jotform now touts AI-assisted products and agent-driven automation. The company reports 300,000 AI Agent conversations annually, signaling a growing appetite for AI-powered assistance in form building, data handling, and process design.

CEO and founder Aytekin Tank says the company’s next chapter centers on “agentic AI” and smart automation—tools that help users design, connect, and scale workflows without writing code.

That aligns with broader industry momentum. As vendors from CRM giants to startup workflow tools embed generative AI into their platforms, the competitive battlefield is shifting from basic automation to autonomous workflows. The promise: systems that not only execute predefined steps but also recommend optimizations, flag risks, and adapt over time.

For Jotform, which already sits at the front lines of data intake, AI presents a logical extension. Forms are often the first touchpoint in a business process—whether it’s a donation, job application, patient intake, or contract submission. Embedding intelligence at that entry point could amplify downstream impact.

Scale by the Numbers

Anniversary announcements often lean on nostalgia. Jotform leans on metrics:

• 35+ million users

• 190+ countries served

• 600+ employees

• Seven global offices

• 248% revenue growth since 2021

• $2 billion in annual payment volume

These figures position Jotform as more than a niche tool. With adoption across nonprofits, healthcare, education, government, and over 40 industries, it has carved out a cross-sector footprint.

Notably, heavily regulated industries—healthcare and government in particular—have gravitated toward the platform. Jotform highlights its secure, certified, compliance-ready solutions as a strength over the past two decades. In sectors where data sensitivity is non-negotiable, that credibility is table stakes.

Competing in a Crowded No-Code Market

The no-code and low-code market has exploded in recent years, fueled by digital transformation initiatives and developer shortages. Enterprises increasingly want business teams to build and iterate processes independently, reducing IT bottlenecks.

Jotform competes in this space alongside dedicated automation platforms and broader SaaS ecosystems. While it doesn’t attempt to replace enterprise-grade workflow engines, it occupies a valuable middle ground: powerful enough for structured processes, simple enough for business users.

That positioning could prove resilient. As automation tools grow more complex—often adding layers of AI, analytics, and orchestration—ease of use becomes a differentiator. Tank’s emphasis on “removing friction instead of adding complexity” reads as both product philosophy and competitive jab.

What 20 Years Signals for the Market

Surviving two decades in SaaS is no small feat. Thriving in a category that has evolved from basic web utilities to mission-critical enterprise infrastructure is even rarer.

Jotform’s trajectory reflects three major market shifts:

1. The democratization of development through no-code tools

2. The convergence of data collection and workflow automation

3. The integration of AI into everyday business processes

As workflows grow more autonomous and cross-functional, the humble form is no longer just a data capture mechanism. It’s the front door to business logic, compliance, analytics, and revenue.

Looking ahead, Jotform’s challenge will be maintaining simplicity while layering in intelligence. If it can embed AI in a way that feels assistive rather than intrusive, it could extend its relevance well into its third decade.

For now, the company’s 20-year milestone is less about celebration and more about signal: no-code is no longer a fringe convenience. It’s a strategic layer of the modern tech stack.

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Enterprise automation isn’t the problem. Seeing what it’s actually doing—that’s the real challenge.

Redwood Software has rolled out a significant observability upgrade to RunMyJobs by Redwood, aiming to make automation intelligence accessible beyond IT and into the wider business.

The update expands native analytics inside the platform, introduces a new integration with SAP Cloud ALM, and deepens ties with major observability platforms. The timing is strategic: according to Redwood’s Enterprise Automation Index 2026, 61% of enterprises say their automation tools are underutilized.

In other words, companies have automated plenty. They just can’t always measure, manage, or optimize it effectively.

From “Single Pane of Glass” to Role-Specific Visibility

Observability has long been marketed as a “single pane of glass” vision—a centralized dashboard for everything. In practice, that often becomes a cluttered control center that satisfies no one.

Redwood’s new approach is layered and ecosystem-driven. Instead of forcing every stakeholder into the same dashboard, the platform now delivers role-specific visibility across automation environments.

At the center is Redwood Insights, the platform’s built-in analytics layer. It provides:

• Pre-built and customizable dashboards

• Real-time performance tracking

• Bottleneck detection

• SLA risk monitoring

• Compliance-ready reporting

The goal is to move automation data out of technical silos and into the hands of operations leaders, finance teams, compliance officers, and executives.

That’s a meaningful shift. Automation can’t scale if only a small group of engineers understands its impact.

Native Analytics Meets Enterprise Observability Stacks

The upgrade doesn’t stop at built-in dashboards. Redwood is strengthening integrations with leading observability platforms, including:

• Dynatrace

• Splunk

• New Relic

• AppDynamics

By correlating automation telemetry with application and infrastructure performance data, enterprises can accelerate root-cause analysis and reduce mean time to resolution (MTTR).

This matters because automation failures rarely happen in isolation. A stalled workflow might originate in an infrastructure bottleneck, a database issue, or a misconfigured application dependency.

Full-stack telemetry correlation gives teams the context they need—without toggling between tools.

SAP-Centric Operations Get First-Class Treatment

For SAP-heavy enterprises, Redwood’s new SAP Cloud ALM connector may be the headline feature.

SAP Cloud ALM is increasingly positioned as a centralized control tower for SAP operations. With the new integration, RunMyJobs execution data flows directly into SAP Cloud ALM, extending observability to automated jobs and workflows that underpin critical business processes.

That includes workflows spanning both SAP and non-SAP systems—a critical distinction. Modern enterprises rarely operate in single-vendor environments.

By synchronizing execution data into SAP’s observability layer, organizations gain centralized transparency without switching platforms. It’s a practical move for SAP-centric operations that want tighter orchestration visibility without tool sprawl.

Redwood Insights Premium: No-Code, Long-Term Intelligence

Redwood also introduced Redwood Insights Premium, which extends analytics capabilities with:

• A no-code custom dashboard builder

• 15 months of historical data retention

The longer retention window enables trend analysis, executive reporting, and automation ROI measurement over time.

In many enterprises, automation ROI is assumed rather than proven. With immutable, long-term execution data, teams can demonstrate cost savings, SLA compliance, and efficiency improvements—useful for audits and budget reviews alike.

Crucially, IT teams can securely create dashboards tailored to different audiences. A data management team might require granular execution metrics, while executives may want high-level SLA risk indicators.

That flexibility supports what Redwood describes as democratized automation intelligence.

Why Observability Is Becoming Automation’s Next Frontier

Automation has matured quickly over the past decade, evolving from task schedulers to enterprise-wide orchestration platforms. But visibility hasn’t always kept pace.

As companies pursue autonomous enterprise strategies, blind spots become expensive.

• Missed SLAs can trigger contractual penalties

• Manual reporting creates bottlenecks

• Lack of telemetry correlation increases MTTR

• Compliance gaps introduce risk

Redwood’s strategy aligns with a broader industry shift: automation platforms are no longer judged solely by what they execute, but by how transparently and predictably they operate.

Observability is becoming a core differentiator.

Business Outcomes, Not Just Dashboards

Redwood frames the update around measurable impact. Organizations leveraging the expanded observability ecosystem can:

• Reduce MTTR through cross-platform telemetry correlation

• Eliminate manual reporting and “IT-as-translator” bottlenecks

• Monitor SLA risks in real time

• Demonstrate automation ROI with long-term execution data

For enterprises struggling with underutilized automation investments, better visibility may be the missing link between deployment and value realization.

The Bigger Picture: Automation Grows Up

The autonomous enterprise vision depends on more than scripts and schedulers. It requires trust, predictability, and shared visibility.

By embedding analytics natively, integrating deeply with SAP environments, and connecting to broader observability ecosystems, Redwood is positioning RunMyJobs as both an execution engine and an intelligence layer.

If automation is the nervous system of modern operations, observability is the feedback loop that keeps it healthy.

And as 2026 unfolds, enterprises may find that the real competitive edge isn’t how much they automate—but how clearly they can see it.

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Leadership reshuffles often signal something bigger than a new title. At Quad/Graphics, Inc., the promotion of Dave Honan to President—while retaining his Chief Operating Officer role—appears designed to reinforce execution as the company deepens its evolution into a marketing experience powerhouse.

Honan, who has served as COO since 2022, will now take on expanded responsibilities overseeing day-to-day operational leadership across Quad’s business units. He continues to report directly to Chairman and CEO Joel Quadracci, who remains focused on long-term strategy, innovation, and stakeholder relationships.

For a company navigating the intersection of legacy print manufacturing and modern marketing services, clarity at the top matters.

A Structural Shift Focused on Execution

Quadracci has led Quad as President and CEO since 2006 and as Chairman, President and CEO since 2010. By elevating Honan to President, the company is formalizing a leadership structure that separates long-term strategic direction from daily operational management.

In practice, this means:

• Honan drives operational discipline and growth execution

• Quadracci focuses on strategic transformation and external relationships

• The executive team aligns around scaling Quad’s marketing services vision

That alignment is particularly relevant as Quad continues repositioning itself beyond its roots as a large-scale printing company.

From Print Giant to Marketing Experience Company

Quad has spent the past decade transforming into what it calls a “marketing experience company”—expanding beyond manufacturing into integrated marketing services, data-driven solutions, and omnichannel execution.

The shift mirrors broader industry trends. As brands consolidate agency relationships and demand measurable ROI across channels, service providers are under pressure to deliver both creative and operational scale.

Quad’s hybrid model—combining manufacturing infrastructure with marketing services—requires tight operational control. Margin management in print remains critical, even as higher-growth marketing services expand.

Honan’s background positions him well for that balancing act.

A Finance Leader Turned Operations Chief

Honan joined Quad in 2009 and has held multiple executive roles, including Chief Accounting Officer and Chief Financial Officer before becoming COO.

He’s credited with:

• Strengthening Quad’s public-company finance and accounting functions

• Refining its capital structure

• Improving manufacturing efficiency

• Driving margin expansion

• Supporting innovation as marketing services scaled

That operational and financial rigor has been central to Quad’s ability to fund its transformation while maintaining competitiveness in a mature print market.

By elevating Honan, Quad is effectively doubling down on disciplined execution as it accelerates growth initiatives.

Why This Matters in Today’s Marketing Landscape

The marketing services sector is undergoing rapid change. Brands face:

• Fragmented media ecosystems

• Pressure for measurable performance

• Rising production and distribution costs

• Increased demand for omnichannel consistency

Providers that can integrate production, data, logistics, and strategy under one roof may hold an advantage.

Quad’s leadership update suggests confidence in its operational engine at a time when efficiency and scalability are key differentiators.

It also signals continuity rather than disruption. Honan’s 17-year tenure offers institutional knowledge, while Quadracci’s continued role ensures strategic consistency.

The Road Ahead

The promotion isn’t a dramatic pivot—it’s a structural refinement.

Honan’s expanded role formalizes his responsibility for driving day-to-day execution across Quad’s evolving business model. Quadracci remains the strategic architect.

For investors and clients, the move reinforces stability as Quad continues its transition from print-centric roots to a diversified marketing experience platform.

If the company’s next phase hinges on operational precision meeting strategic ambition, this leadership adjustment appears designed to keep both in sync.

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