A new challenger in the CRM market is making a bold pitch to startups frustrated with legacy platforms: moving your entire CRM stack should take about an hour.

Lightfield, an AI-native customer relationship management platform built for high-growth companies, has launched an automated migration agent designed to transfer data from platforms such as HubSpot with minimal manual work. The system processes exported CSV files and automatically maps contacts, companies, deals, custom fields, and pipeline stages—eliminating the manual data cleaning and field mapping typically required during CRM migrations.

The company says the tool can process up to 90,000 records per hour while preserving relationships across records, a step often cited as one of the most complex aspects of CRM switching.

A Fast-Growing AI-Native CRM

Lightfield emerged from stealth in November 2025 and has already gained traction among startups. According to the company, more than 2,500 organizations have created workspaces on the platform, with hundreds migrating directly from HubSpot.

The rapid growth highlights a broader trend in enterprise software: startups increasingly want AI-native tools that automate routine workflows rather than simply storing data.

Traditional CRM platforms were built for manual input. Sales teams log calls, update pipeline stages, and write notes after every customer interaction. AI-driven systems like Lightfield aim to replace that model by automatically capturing and analyzing communication data.

A Data Ownership Debate

The launch arrives at a moment when data ownership inside CRM platforms is becoming a sensitive topic.

During an investor call discussing HubSpot’s fourth-quarter 2025 results, CEO Yamini Rangan indicated that the company plans to “monitor, meter, and monetize” third-party agent access to customer data on its platform.

That stance suggests that as AI-powered development tools become more common, software vendors may increasingly control how external applications interact with platform data.

Lightfield CEO Keith Peiris argues for the opposite approach.

“Your data is yours—and you should be able to use it, unencumbered, with any agentic tool you choose,” Peiris said in announcing the migration agent. He added that all objects and attributes inside Lightfield are accessible through its API without egress fees.

Peiris frames the strategy as preparation for a future in which AI agents interact fluidly with enterprise systems rather than operating within tightly controlled software ecosystems.

“The future of work will be far more fluid than the last generation of SaaS,” he said.

The CRM Problem for Startups

For many startups, CRM systems are essential but frustrating infrastructure.

Sales teams often spend hours each week updating records—logging calls, entering meeting notes, and updating deal stages. Even with consistent effort, CRM databases frequently remain incomplete because information depends on manual entry.

As companies scale, the problem compounds. New hires inherit CRM records that may lack historical context, forcing founders and senior sales leaders to remain heavily involved in deals simply because institutional knowledge isn’t captured consistently.

The friction associated with switching CRM platforms has historically reinforced this dynamic. Migrating from systems like HubSpot often requires weeks of consulting work, extensive field mapping, and careful data cleaning to avoid losing critical information.

Lightfield’s migration agent aims to eliminate that barrier by automating the entire process.

How the Migration Agent Works

The system follows a structured, multi-step workflow designed to make CRM switching mechanical rather than manual.

First, users export their CRM data—typically contacts, companies, deals, and custom fields—as CSV files. The migration agent analyzes the structure of those files and confirms mapping before importing any data.

Next, the system configures the Lightfield workspace to mirror the original CRM structure, including pipeline stages and custom properties. Once configured, records are imported and linked automatically so relationships between contacts, accounts, and deals remain intact.

After migration, teams can connect their email and calendar accounts. Lightfield then ingests communication data to build contextual histories for every contact and opportunity.

Companies can also upload transcripts from recorded sales calls. The platform associates those conversations with the relevant contacts and deals, creating searchable context across the CRM.

An AI-Driven CRM Workflow

Once the migration is complete, Lightfield’s AI layer begins automating many of the tasks traditionally handled manually by sales teams.

The platform continuously logs calls, emails, and meetings, automatically generating summaries and suggested follow-up actions. Pipeline analytics are also generated directly from conversation data rather than relying on manually updated fields.

For founders and sales leaders, the shift could significantly reduce time spent on CRM maintenance.

Tyler Postle, co-founder of Y Combinator-backed startup Voker, described the difference after switching platforms.

“Using HubSpot, I was a data hygienist,” Postle said. “Using Lightfield, I’m a closer.”

The Rise of AI-Native Business Software

Lightfield’s launch reflects a broader movement across enterprise technology.

AI-native tools are emerging across categories—from productivity software to analytics platforms—designed to automate data capture and decision-making rather than simply organizing information.

In the CRM category, that shift could reshape long-standing incumbents whose platforms were built around manual workflows.

For startups adopting AI-driven development environments and automation tools, the ability to integrate CRM data seamlessly with external agents may become an increasingly important differentiator.

Lightfield is betting that reducing migration friction—and offering open access to business data—will accelerate that transition.

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AI assistants are increasingly becoming the interface for business software. Now, one e-signature platform wants to make sending legally binding documents as easy as asking an AI to do it.

Firma.dev has launched Firma 12, a major update to its developer-focused e-signature API that introduces dual Model Context Protocol (MCP) servers. The integration allows users to send, track, and manage document signatures directly through AI tools such as ChatGPT, Claude AI, Cursor, GitHub Copilot, Visual Studio Code, and OpenAI Codex.

The update positions Firma.dev among a growing group of platforms building AI-native integrations that allow conversational interfaces to perform real operational tasks—rather than simply generating content or summaries.

AI as the New Interface for E-Signatures

Firma 12 introduces two MCP servers designed to connect AI tools directly with the platform.

The Docs MCP server, released earlier in 2026, enables developers to query live API documentation inside coding environments. The newly released Data MCP server goes further by allowing AI tools to perform real actions inside a Firma.dev account.

The system exposes 84 AI-ready tools across 10 operational categories, enabling users to create signing requests, manage templates, track envelope statuses, configure webhooks, and query usage data.

According to Derick Dorner, co-founder of Firma.dev, the goal is to remove traditional barriers between users and digital document workflows.

“You can literally say ‘send the standard NDA to Sarah’ inside Claude, and it happens,” Dorner said. “You don’t need to be a developer. You don’t need to learn an API. You just talk to your AI.”

Authentication is handled through OAuth, meaning users can connect their AI tools simply by adding the MCP server URL and signing into their Firma.dev account.

Competing on Price in the E-Signature Market

Alongside its AI integrations, Firma.dev is also competing aggressively on cost.

The company charges €0.029 per envelope, roughly three U.S. cents, using a pure pay-as-you-go model without contracts, per-seat fees, or monthly minimums.

That pricing stands in contrast to legacy e-signature platforms such as DocuSign, where enterprise customers can pay anywhere from $1 to more than $5 per envelope depending on plan structure and volume.

The pricing difference has already attracted customers processing large document volumes.

Paul Jolley, CEO of Hawaiian property management platform Clear, said switching platforms could reduce his company’s annual costs by thousands of dollars.

Jolley’s team processes roughly 20,000 envelopes per month, and he estimates the move to Firma.dev could save between $5,000 and $10,000 over the next year.

“The billing model is like Twilio,” Jolley said. “You just use it and pay for what you send.”

AI-Native Integration Speeds Implementation

The platform’s AI-driven approach also appears to simplify integrations.

Yavuz Selim Mert, founder of Splendid Consulting in Toronto, said he completed a full implementation in roughly a day despite having no prior coding experience.

Using ChatGPT as a guide during setup, Mert reduced his monthly e-signature costs from about $230 with his previous provider to roughly $14 using Firma.dev—an estimated 94 percent cost reduction.

Another customer, Ghali Bennani, co-founder of London fintech startup Ralio, specifically chose the platform for its MCP integration and completed setup in under five minutes.

These examples highlight a growing trend in enterprise software: AI tools increasingly serve as both development assistants and operational interfaces.

Built for AI-First Workflows

Firma.dev’s MCP architecture reflects a broader shift toward AI-driven workflows, where conversational interfaces trigger real business processes across software systems.

Instead of navigating dashboards or writing custom integrations, users can instruct AI agents to perform tasks such as sending contracts, checking document status, or retrieving usage metrics.

This model mirrors developments across the SaaS ecosystem as platforms integrate with AI development tools and agent frameworks.

As AI assistants become more deeply embedded in developer environments and business operations, APIs designed for conversational control may become a defining feature of modern enterprise platforms.

Legal Compliance and Global Availability

Firma.dev’s e-signatures are legally valid in 54 countries and support SES (Simple Electronic Signatures) and AdES (Advanced Electronic Signatures) under the European Union’s eIDAS regulatory framework.

The platform also complies with U.S. electronic signature laws, including the ESIGN Act and UETA, along with equivalent regulations in other supported jurisdictions.

Infrastructure is hosted on European cloud servers, including AWS infrastructure in Paris with content delivery via Stockholm. The system is designed to meet regulatory requirements such as GDPR, HIPAA, SOC 2, and ISO/IEC 27001.

Availability

Firma 12 is available immediately through the company’s website. New users can create an account and begin sending documents without providing a credit card.

Developers and AI tool users can connect to the Docs MCP server or Data MCP server directly, enabling AI-driven document workflows across supported platforms.

As AI interfaces increasingly move beyond chat and into operational control, Firma.dev is betting that e-signatures—one of the most common digital business processes—are ready for a conversational future.

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Artificial intelligence has become a staple in modern marketing stacks—but most teams still struggle to turn AI-generated insights into real operational impact.

That’s the central takeaway from a new industry survey released by NinjaCat, an AI-enabled marketing data and analytics platform. The report, titled “The Next Phase of Marketing Intelligence: AI Maturity Across the Analyze–Optimize–Act Cycle,” surveyed more than 500 marketing and advertising leaders and found a significant gap between AI experimentation and true AI-driven operations.

While many marketing teams report confidence in their AI capabilities, the infrastructure required to convert insights into consistent action remains largely missing.

AI Adoption Is High—But Operational Maturity Is Low

The survey highlights a paradox familiar to many marketing leaders: widespread AI usage paired with persistent operational inefficiencies.

For example, 85% of respondents say they are satisfied with their data visibility, suggesting marketers feel they have access to the information they need. Yet 78% report that performance data remains fragmented across platforms and spreadsheets, making it difficult to translate insights into coordinated action.

Reporting workflows reveal a similar disconnect. While 91% of respondents say AI has streamlined parts of their workflows, 72% still describe reporting as highly manual, indicating that many organizations rely on AI for analysis but not execution.

Perhaps the most revealing statistic involves workflow orchestration. Only 8% of marketing teams report running multi-step AI workflows across tools and departments, suggesting that true AI operational maturity remains rare across the industry.

According to Paul Deraval, CEO of NinjaCat, the gap reflects a deeper structural challenge rather than a lack of enthusiasm for AI.

“AI can be an extremely powerful amplifier, but you need to know what it is amplifying,” Deraval said. “AI is not a band-aid you can slap on a problem; it needs to be properly integrated.”

Evaluating AI Across the Marketing Lifecycle

The report evaluates AI adoption across three stages of the marketing lifecycle: Analyze, Optimize, and Act. Across each phase, organizations appear to encounter barriers that prevent insights from turning into measurable outcomes.

In the Analyze phase, data fragmentation remains a major obstacle. Although 83% of marketing leaders feel confident in their ability to analyze performance data, 70% report that reconciling data from multiple sources consumes significant time. Only 37% of organizations maintain a unified source of truth, limiting the reliability of insights.

The Optimize stage shows similar friction. Roughly 80% of marketers say they are comfortable with AI automating campaign optimization, yet the lack of integrated workflows limits how those insights are applied. With only 8% of organizations orchestrating multi-step AI processes, many teams identify opportunities but struggle to implement them effectively.

The Act phase—where insights become execution—remains the weakest link. Two-thirds of respondents (66%) rely on generic AI tools, while just 16% report using AI systems connected directly to their proprietary data.

This reliance on off-the-shelf AI tools often limits personalization, automation depth, and operational integration.

The Small Group Achieving AI Maturity

Despite the widespread challenges, the report identifies a small segment of organizations that have successfully operationalized AI across the marketing cycle.

These advanced teams share several common characteristics. They consolidate marketing data into centralized intelligence layers, connect AI-generated insights directly to operational workflows, and automate execution across platforms without relying on manual handoffs between teams.

One example cited in the report is Seer Interactive, a digital marketing agency that integrated an AI agent into its existing marketing processes.

According to Alisa Scharf, Vice President of AI and Innovation at Seer Interactive, the integration significantly accelerated insight generation.

“Now in marketing, timing is the asset,” Scharf said. “A great insight that shows up three weeks late isn’t an insight—it’s a recap.”

By combining unified data infrastructure with automated AI workflows, the organization reportedly achieved a 30× reduction in time-to-insight, dramatically improving responsiveness to performance signals.

The Next Phase of Marketing Intelligence

The survey suggests that the marketing industry may be entering a new phase of AI adoption.

During the initial wave of AI integration, many organizations experimented with tools for reporting automation, predictive analysis, or content generation. The next phase, according to NinjaCat, will focus on operationalizing AI across the full marketing lifecycle.

That means integrating AI not just into analysis or optimization, but also into execution systems that manage campaigns, reporting, and performance adjustments in real time.

As marketing ecosystems become more complex—spanning advertising platforms, analytics tools, CRM systems, and social media channels—the ability to orchestrate AI-driven workflows across those environments could become a critical competitive advantage.

Organizations that succeed in building that infrastructure may gain faster insight cycles, improved campaign agility, and more efficient data-driven decision-making.

For marketing leaders, the challenge ahead may not be adopting AI—but ensuring it actually changes how work gets done.

The full report is available from NinjaCat for organizations interested in benchmarking their own AI maturity across marketing operations.

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LearnUpon, a global provider of learning management systems (LMS), has launched Create+, an AI-native content authoring capability designed to dramatically accelerate course creation for learning and development teams.

The launch represents the first major product milestone following LearnUpon’s acquisition of AI learning creation platform Courseau in November 2025. By integrating Courseau’s AI capabilities directly into its LMS platform, LearnUpon aims to eliminate one of the most persistent challenges in corporate training: the content creation bottleneck.

Tackling the Learning Content Bottleneck

For many organizations, building digital training courses can take weeks or even months. Instructional designers often rely on multiple tools, external vendors, and lengthy production cycles to develop structured learning materials.

Create+ aims to change that workflow.

The new AI-powered authoring system allows organizations to convert existing materials—including PDFs, documents, videos, and links—into structured, interactive learning modules in minutes. The generated courses can then be reviewed, edited, and customized directly within the platform.

By reducing reliance on complex development processes and external production resources, Create+ helps organizations accelerate training delivery while lowering costs.

Making Course Creation Accessible to Subject Matter Experts

A key focus of Create+ is expanding who can build learning content. Traditionally, course creation has required instructional design expertise or specialized software skills.

With AI-native authoring integrated into the LMS, internal Subject Matter Experts (SMEs) can now transform their expertise into structured learning experiences without needing formal training design backgrounds.

According to Louise Jackson, Learning and Development Business Partner at Ebor Academy Trust, the technology simplifies internal knowledge sharing.

“By using Create+ as part of our LearnUpon portal, Ebor Academy Trust can instantly transform our internal expertise and existing resources into engaging, high-quality learning modules for our staff,” Jackson said.

She added that simply uploading a document or link enables the AI system to generate a structured course within minutes, with content remaining fully editable afterward.

The organization expects the platform to reduce reliance on expensive external content while supporting a more sustainable internal training model aligned with its educational standards.

Consolidating the Learning Technology Stack

Another major advantage of the integration is operational consolidation.

By embedding AI-powered authoring directly into the LMS platform, administrators can manage the entire learning lifecycle—from course creation to distribution and tracking—within a single system.

This approach helps organizations streamline their learning technology stack and reduce spending on separate authoring tools.

Brendan Noud, CEO of LearnUpon, said the launch reflects the company’s broader mission of helping organizations unlock the potential of their workforce and communities.

“It’s great to see this come together so quickly,” Noud said. “We’re incredibly keen to save our customers both time and money while supporting their evolving course creation needs.”

Strengthening the LearnUpon–Courseau Partnership

The release of Create+ also highlights the rapid integration of Courseau’s technology into LearnUpon’s platform following the 2025 acquisition.

According to Ro Ren, co-founder of Courseau, the collaboration between the two teams enabled the product to move from acquisition to launch in a relatively short time.

“This launch is a clear testament to the great fit between LearnUpon and Courseau,” Ren said, adding that the teams are now focused on shaping the future of workplace learning together.

The Growing Role of AI in Workplace Learning

The launch of Create+ reflects a broader shift toward AI-driven learning technologies. As organizations face increasing pressure to train employees, partners, and customers quickly, AI-powered authoring tools are emerging as a critical solution.

By reducing development time and enabling experts across organizations to create learning materials directly, platforms like LearnUpon aim to make workplace learning more agile and scalable.

Organizations interested in the new capability can request a 14-day Create+ trial or schedule a platform demonstration through LearnUpon.

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Customer relationship management platforms are rapidly evolving into strategic enterprise systems as artificial intelligence reshapes how companies manage customer engagement, revenue operations and business growth.

That is the key conclusion of new research released by Information Services Group (ISG), a global AI-centered technology research and advisory firm. The firm’s 2026 ISG Buyers Guides™ for Customer Relationship Management analyze CRM platforms across the market and highlight how AI-powered capabilities are transforming CRM from a data repository into an operational intelligence hub.

CRM Expands Beyond Sales Automation

Historically, CRM platforms primarily served as tools for contact management and sales force automation. However, ISG’s research shows the technology has evolved significantly, becoming a foundation for revenue operations, customer experience strategy and performance management.

According to Barika Pace, director of research for revenue operations at ISG, enterprises increasingly depend on CRM platforms to unify organizational insights.

“Companies need a shared, trusted view of customer interactions to create long-term value,” Pace said. “They rely on CRM to align marketing, sales and service teams and inform strategy and execution throughout the organization.”

The report evaluated 52 software providers and their CRM-related products, examining platforms designed to manage customer and prospect data, sales engagement, digital commerce and partner relationship management.

Multi-Channel Customer Engagement Drives CRM Demand

ISG found that enterprise requirements for CRM platforms have expanded as companies adopt digital commerce models and engage customers across more channels than ever before.

Organizations now expect CRM systems to support:

• Cross-channel customer engagement
• Revenue forecasting and pipeline visibility
• Workflow automation
• Customer lifetime value analysis
• Integration across marketing, sales and service platforms

Despite this demand, many enterprises still rely on legacy CRM systems dependent on spreadsheets and manual data entry, limiting their ability to automate processes and generate actionable insights.

AI Is Transforming CRM Platforms

Artificial intelligence is playing a central role in the next generation of CRM platforms.

The research highlights several AI-driven capabilities already embedded in modern CRM systems, including:

• Predictive lead scoring
• Customer segmentation optimization
• Intelligent service routing
• Forecasting and analytics automation

However, most AI implementations currently augment human decision-making rather than operate autonomously.

ISG notes that the rise of agentic AI—systems capable of planning and executing actions within defined parameters—represents the next stage of CRM evolution. These capabilities are shifting CRM systems from passive data repositories to active orchestration engines for revenue and customer engagement workflows.

Legacy Architectures Remain a Barrier

Despite the growing importance of AI in CRM platforms, many enterprises are not yet prepared to deploy advanced capabilities.

ISG predicts that more than half of enterprises will be unable to implement the latest AI-driven CRM technologies through 2027 because their internal processes and system architectures remain outdated.

These limitations could significantly affect revenue growth, as organizations without modern CRM infrastructure may struggle to deploy AI-powered sales automation, service optimization and partner management tools.

To prepare for the next generation of CRM systems, ISG advises organizations to prioritize:

• AI strategy alignment with business goals
• Flexible system architecture
• Strong integration ecosystems
• Governance frameworks that limit technical debt

Top CRM Providers Identified

The ISG Buyers Guides evaluate vendors based on five categories: Overall performance, Product Experience, Capability, Platform strength and Customer Experience.

Among major CRM vendors, Salesforce and Oracle ranked as the top two Overall Leaders across all platform categories, reinforcing their dominant positions in the global CRM market.

Other leading providers highlighted in the research include:

• HubSpot, which ranked as the third Overall Leader across six categories
• ServiceNow and Microsoft, which each ranked third in specific platform categories

The research also evaluated emerging CRM providers, with Creatio ranking as the top Overall Provider, followed by SuperOffice and Pipedrive.

CRM Platforms Now Span Multiple Enterprise Functions

The 2026 Buyers Guides cover several CRM-related categories reflecting the expanding role of the technology ecosystem:

• Customer Relationship Management platforms
• CRM marketing platforms
• CRM sales platforms
• CRM service platforms
• Digital commerce solutions
• Partner relationship management software
• Sales engagement tools

Providers evaluated across these segments include major technology companies such as Adobe, SAP, Zendesk, Zoho and ZoomInfo.

CRM Strategy Will Shape Enterprise Competitiveness

According to David Menninger, executive director of software research at ISG, the CRM platform an organization selects can significantly influence its ability to adopt new technologies and scale revenue.

“The CRM software an enterprise chooses can significantly influence its ability to adapt to new technologies and increase revenue and profit over time,” Menninger said.

ISG notes that the Buyers Guides are based on more than a year of independent market research and are not sponsored by software vendors, providing enterprises with an objective framework for evaluating CRM technology investments.

As CRM platforms continue evolving into AI-driven operational hubs, the research suggests that organizations investing in flexible, AI-ready architectures today will be best positioned to compete in the next generation of digital customer engagement.

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Rising customer expectations for seamless, personalized interactions are pushing enterprises to adopt advanced journey analytics and orchestration platforms that turn real-time data into meaningful engagement. Reflecting this shift, CSG Systems International has been named a Leader in the 2026 Gartner Magic Quadrant for Customer Journey Analytics and Orchestration, recognizing the company’s ability to help organizations deliver more connected customer experiences.

The evaluation assessed vendors based on two key criteria: Completeness of Vision and Ability to Execute, positioning CSG among the top technology providers in the customer experience orchestration space.

Customer Experience Now Requires Real-Time Decisioning

As enterprises manage increasingly complex digital ecosystems, simply understanding customer behavior is no longer enough. Organizations must act on insights immediately to ensure every interaction contributes to long-term loyalty and business value.

According to Katie Costanzo, President of Customer Experience at CSG, modern customer engagement demands real-time operational intelligence.

“It’s not enough to understand the customer—businesses must act on that knowledge in real time and prove the value of every customer interaction,” Costanzo said.

She noted that organizations increasingly need unified systems capable of transforming customer data into clear decisions, measurable outcomes and trusted customer experiences.

Xponent Platform Enables Real-Time Customer Journey Orchestration

At the center of CSG’s recognition is CSG Xponent, the company’s customer engagement and journey orchestration platform.

The platform is designed to help organizations bridge the gap between customer expectations and operational execution by integrating real-time data across multiple touchpoints. This allows brands to deliver contextual interactions precisely when they matter most.

Key capabilities of the platform include:

• Real-time journey analytics
• Cross-channel personalization
• Customer experience monitoring
• Automated decisioning and orchestration
• Real-time issue detection within customer journeys

By enabling businesses to identify and resolve friction points as they occur, the platform aims to improve both customer satisfaction and operational performance.

Measurable Business Impact

CSG reports that organizations using the Xponent platform have already achieved measurable results.

One example cited involves a U.S.-based bank that implemented the platform to optimize customer engagement across digital channels. The deployment reportedly delivered several improvements:

• 12% reduction in inbound customer calls
• Threefold increase in SMS response rates
• 25% decrease in fraud cases
• $30 million in financial impact

These results highlight how journey analytics platforms are evolving beyond reporting tools into systems that directly influence business outcomes.

Journey Analytics Emerging as a Strategic Technology Category

The Gartner Magic Quadrant for Customer Journey Analytics and Orchestration represents one of the first comprehensive evaluations of this rapidly growing technology segment.

The research provides enterprises with a comparative view of vendors operating in the space, helping organizations assess which platforms can best support customer experience transformation initiatives.

According to Gartner’s methodology, Magic Quadrant research evaluates vendors based on market understanding, product strategy, innovation, and execution capabilities across real-world customer deployments.

The Growing Importance of Journey Orchestration

Customer journey orchestration platforms are gaining traction as organizations seek to unify interactions across marketing, service, digital commerce and support channels.

As digital engagement continues to expand, businesses increasingly need systems that can analyze customer signals in real time and respond with contextual actions—whether through messaging, offers, service interventions or fraud prevention.

For enterprises investing in customer experience transformation, journey analytics and orchestration technologies are quickly becoming essential infrastructure for delivering the connected, personalized experiences customers now expect.

Organizations interested in exploring the full research findings can access the 2026 Gartner Magic Quadrant for Customer Journey Analytics and Orchestration through CSG’s resource center.

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Contract review has long been the silent productivity killer inside enterprises—slow, manual, and deeply dependent on overworked legal teams. Now, Docusign is stepping in with a fix it hopes will finally move the needle.

The company has introduced a new AI-powered contract review assistant, built on its Intelligent Agreement Management (IAM) platform and powered by its Iris AI engine. The goal is straightforward: help legal, sales, and procurement teams review agreements faster without sacrificing oversight.

AI Takes on Contract Review’s Most Tedious Work

At its core, the assistant tackles the grunt work that typically bogs down legal teams. Instead of manually scanning dense documents, users get AI-generated highlights of key terms, risks, and deviations from company standards.

Think of it as a copiloted legal review: teams can query contracts in plain language—like asking whether a deal auto-renews—and get precise answers linked directly to relevant clauses. It’s a notable shift from static document review to interactive analysis.

The assistant also generates redlines, suggests edits, and drafts new clauses. That puts it squarely in competition with a growing wave of AI legal tech vendors aiming to automate early-stage contract review.

Playbooks, But Without the Pain

Legal playbooks—those internal guides that dictate how contracts should be reviewed—are essential but notoriously hard to maintain. Docusign is trying to change that dynamic by letting teams auto-generate playbooks from templates or past agreements.

More importantly, contracts can be automatically checked against those playbooks, flagging non-compliant terms in real time. That’s a big deal for enterprises juggling high volumes of vendor and customer agreements.

The implication: less time spent enforcing policy manually, and fewer risky clauses slipping through the cracks.

Built Into the Workflow, Not Bolted On

Unlike standalone AI tools, Docusign’s assistant is embedded directly into its IAM platform. That means contract review isn’t an isolated step—it’s part of a continuous workflow spanning creation, negotiation, signing, and lifecycle management.

This integration matters. Legal teams rarely operate in isolation, and delays often come from misalignment between departments. By keeping review connected to sales, procurement, and HR workflows, Docusign is betting it can reduce friction across the entire agreement lifecycle.

Why This Matters Now

The timing isn’t accidental. Agreement management is quickly becoming a strategic priority, not just a back-office function.

According to Deloitte, more than 70% of legal leaders say agreement management tools improve outcomes—from dispute resolution to sales satisfaction. That aligns with Docusign’s own internal metrics, which show AI-assisted reviews saving up to 15 minutes per NDA and cutting master service agreement (MSA) negotiations by up to an hour.

In a high-volume enterprise environment, those time savings compound quickly.

The Bigger Picture: AI’s Expanding Role in Legal Tech

Docusign’s move reflects a broader trend: AI is rapidly reshaping legal operations, especially in contract lifecycle management (CLM). Competitors and startups alike are racing to automate everything from clause extraction to negotiation insights.

What sets Docusign apart—for now—is its scale and its ability to embed AI directly into an end-to-end agreement platform. If execution holds up, that could give it an edge over point solutions that require additional integrations.

Still, the real test will be adoption. Legal teams tend to be cautious, especially when AI is involved in risk-sensitive processes. Transparency, accuracy, and auditability will be key factors in determining whether tools like this become indispensable—or just another experiment.

Availability

The contract review assistant is available globally for Docusign CLM and select IAM customers, with support for multiple languages including English, French, German, Spanish, and Brazilian Portuguese.

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The next insider threat might not be an employee—it could be your AI.

That’s the premise behind BigID’s latest move: extending its Data Access Governance (DAG) platform to cover AI agents, the increasingly autonomous systems operating across enterprise environments with minimal oversight.

As enterprises deploy agentic AI tools that can access databases, retrieve sensitive information, and even take actions on behalf of users, governance frameworks built for humans are starting to crack. BigID is betting that the future of data security lies in treating these agents as first-class identities.

AI Agents: The New Insider Risk

Unlike human users, AI agents don’t log off, take breaks, or question unusual activity. They operate continuously, often with permissions granted months earlier and rarely revisited.

That creates a perfect storm: persistent access, broad permissions, and little visibility.

BigID’s expansion addresses this gap by applying the same data-centric governance model used for human users directly to non-human identities. The shift is subtle but significant—security teams now need to track not just who accesses data, but what autonomous systems are doing behind the scenes.

What’s New in BigID’s DAG for AI

The update introduces three core capabilities aimed squarely at enterprise AI risk:

Agent Discovery and Mapping
BigID automatically identifies AI agents operating across systems, mapping what data they access, which permissions they hold, and how they interact with enterprise environments. In short, if an agent is touching your data, it’s now visible.

Access Right-Sizing for AI
Borrowing from least-privilege principles, the platform analyzes actual agent behavior versus granted permissions. Over-permissioned agents are flagged, with remediation paths suggested before misconfigurations turn into incidents.

Real-Time Activity Monitoring
Security teams can track agent behavior as it happens—reads, writes, and cross-system data movement—along with context about data sensitivity and policy compliance. That’s a step beyond traditional logs, offering actionable insight instead of raw activity trails.

Why This Matters Now

The rise of agentic AI is forcing a rethink of identity and access management. Traditional IAM tools—designed for employees and contractors—struggle to keep up with autonomous systems that operate at machine speed and across distributed environments.

BigID’s approach stands out by focusing on the data layer rather than just identity controls. Instead of simply tracking access, it evaluates the sensitivity of the data being accessed and whether that interaction should occur at all.

That’s increasingly critical as enterprises adopt AI copilots, automation agents, and orchestration tools that blur the line between user and system.

A Shift in the Competitive Landscape

Most vendors in the identity governance space are retrofitting existing human-centric IAM frameworks to accommodate AI. BigID, by contrast, is positioning itself as a data-first governance platform—arguably a better fit for environments where risk is tied more to data exposure than login credentials.

This aligns with a broader industry trend: security is moving closer to the data itself, especially as AI systems bypass traditional perimeters.

Still, adoption will hinge on how well these tools integrate with existing security stacks—and whether organizations are ready to treat AI agents with the same scrutiny as human insiders.

The Bottom Line

BigID’s expansion underscores a growing reality: AI agents aren’t just tools—they’re active participants in enterprise workflows, with real access to sensitive data.

And like any insider, they need governance.

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