Enterprise video platform provider Kaltura has announced a strategic partnership with Descript to deliver integrated AI-powered video creation and editing capabilities for organizations across multiple industries.

The collaboration combines Kaltura’s AI-driven video production tools and avatar technology with Descript’s script-based video editing platform. Together, the companies aim to help enterprises scale video content production while maintaining governance, accuracy, and compliance—particularly in highly regulated sectors such as healthcare.

The partnership has already resulted in a commercial deployment at a major medical center, where the integrated solution is being used to produce training and communication content across departments.

Growing Enterprise Demand for AI Video Workflows

Organizations worldwide are increasingly adopting AI-powered video tools to improve internal communications, employee training, and customer engagement.

Video has become a critical medium for enterprise knowledge sharing, but traditional production workflows can be time-consuming and resource-intensive.

By integrating AI-powered creation and editing tools, the partnership between Kaltura and Descript aims to simplify the process of producing professional-grade content.

Enterprises can use the combined platform to automate repetitive production tasks while ensuring subject-matter experts retain control over messaging and content accuracy.

This approach allows organizations to increase production speed without sacrificing governance or oversight.

AI Avatars and Script-Based Editing Streamline Production

The integrated platform brings together two complementary capabilities.

Kaltura provides AI-powered video creation tools and digital avatars that can automatically generate professional video presentations from scripts or structured inputs.

Meanwhile, Descript offers a unique editing interface that allows users to edit video by editing text—making video production accessible even for non-technical teams.

Together, these capabilities enable enterprise teams to:

• produce video content faster
• edit and refine messaging using text-based workflows
• generate AI-driven visual presentations
• distribute content through enterprise video platforms

This integration allows organizations to manage the entire video lifecycle—from production and editing to publishing and distribution—within a unified workflow.

Healthcare Deployment Demonstrates Compliance Capabilities

One of the first joint implementations of the solution is taking place within a major healthcare organization.

Healthcare institutions often face strict compliance requirements regarding training materials, patient communications, and internal documentation.

The integrated platform enables healthcare teams to scale video production across departments while maintaining the governance standards required in regulated environments.

For the medical center deploying the technology, ease of use and human oversight were essential criteria.

By combining automated production tools with human review processes, the system allows experts to maintain control over messaging while leveraging AI to streamline technical production steps.

Supporting the Future of Enterprise Video

Executives from both companies say the partnership reflects broader enterprise demand for integrated AI solutions.

Lior Bukshpan, Head of Strategic Partnerships at Kaltura, described the collaboration as part of a broader shift toward practical AI adoption in enterprise environments.

Organizations increasingly want AI tools that improve productivity without adding complexity to existing workflows.

At the same time, enterprises are facing rising operational pressures, including cost management, workforce burnout, and the need to accelerate digital transformation initiatives.

Integrated AI platforms that simplify content production while maintaining compliance and oversight are becoming essential infrastructure for modern enterprises.

AI Video Platforms Expand Enterprise Content Capabilities

The partnership between Kaltura and Descript reflects a broader transformation in enterprise content creation.

As organizations increase their reliance on video for communication and training, AI-powered platforms are helping scale production in ways that were previously impractical.

By automating technical production tasks and integrating intuitive editing tools, enterprises can produce high-quality content faster and more efficiently.

For industries operating under strict regulatory oversight—such as healthcare, finance, and government—these technologies must also support strong governance frameworks.

The combined capabilities of Kaltura and Descript aim to meet both needs: rapid content production and enterprise-grade compliance.

Key Insights

• Kaltura and Descript formed a strategic partnership to deliver AI-powered enterprise video production tools.
• The integration combines AI video creation, avatars, and script-based editing workflows.
• A major healthcare organization has already adopted the solution for regulated video content production.
• The platform helps enterprises scale video creation for training, communications, and customer engagement.
• AI automation accelerates production while maintaining human oversight and governance. 

Get in touch with our MarTech Experts.

AI-powered advertising platform StackAdapt has been recognized as a Strong Performer in The Forrester Wave™: Omnichannel Advertising Platforms, Q1 2026, an industry evaluation conducted by Forrester.

The report assessed leading omnichannel advertising platforms based on a range of criteria, including current capabilities, strategic vision, and customer feedback.

StackAdapt achieved the highest possible scores in several categories, including self-service capabilities, onboarding and training support, and pricing transparency.

Forrester Evaluation Highlights Platform Strengths

The The Forrester Wave™: Omnichannel Advertising Platforms, Q1 2026 evaluated vendors across two primary dimensions: current offering and long-term strategy.

In addition to strong performance scores, StackAdapt received above-average feedback from customers participating in the evaluation.

According to the report, users praised the platform for its usability, cost-effectiveness, and responsive customer support.

The evaluation also marked StackAdapt’s participation in the first edition of Forrester’s Wave report focused specifically on omnichannel advertising platforms.

For the company, the recognition reflects its growing presence in the rapidly evolving adtech landscape.

AI-Driven Platform Supports Omnichannel Campaigns

The platform from StackAdapt enables marketers to manage advertising campaigns across multiple digital channels through a unified interface.

These channels include:

• connected TV (CTV)
• native advertising
• display advertising
• video advertising
• digital audio
• in-game advertising
• digital out-of-home (DOOH)

By consolidating campaign planning, activation, and optimization into a single environment, the platform aims to eliminate the fragmented workflows commonly associated with legacy advertising systems.

AI and automation capabilities within the platform analyze campaign performance data and help marketers optimize targeting, bidding strategies, and creative delivery.

Bridging MarTech and AdTech

According to Forrester, advertisers increasingly seek comprehensive platforms that reduce operational complexity while providing advanced capabilities.

The report notes that modern advertisers want solutions capable of supporting multiple forms of artificial intelligence—including predictive, generative, and agentic AI—within unified advertising workflows.

This shift reflects the broader convergence of marketing technology and advertising technology (AdTech).

Platforms that integrate audience intelligence, campaign orchestration, and cross-channel optimization are becoming central components of digital marketing infrastructure.

Executives at StackAdapt say the company’s vision aligns with this trend by helping marketers understand audiences more effectively while simplifying campaign management.

Expanding Capabilities for Global Advertising

Over the past year, StackAdapt has continued to expand its platform capabilities.

Recent developments include tools designed to help brands and agencies:

• consolidate media buying workflows
• streamline omnichannel campaign execution
• improve campaign performance through AI insights
• access premium global advertising inventory

These enhancements aim to support enterprise-scale advertising strategies while maintaining ease of use for marketers.

Industry Recognition Reflects Platform Momentum

Recognition in The Forrester Wave™: Omnichannel Advertising Platforms, Q1 2026 places StackAdapt among leading vendors in the omnichannel advertising market.

Industry analysts note that demand for integrated advertising platforms continues to grow as brands seek more efficient ways to manage campaigns across increasingly complex digital ecosystems.

By combining AI-driven automation, cross-channel campaign management, and strong customer support, the platform aims to help marketers deliver measurable outcomes while reducing operational complexity.

Key Insights

• StackAdapt was named a Strong Performer in The Forrester Wave™: Omnichannel Advertising Platforms, Q1 2026.
• The company received top scores for self-service capabilities, onboarding and training, and pricing transparency.
• The platform supports campaigns across CTV, native, display, video, audio, in-game, and DOOH channels.
• AI-driven automation helps marketers optimize campaigns and improve performance across channels.
• The recognition reflects growing demand for integrated omnichannel advertising platforms.

Get in touch with our MarTech Experts.

As AI-powered search rapidly reshapes how companies evaluate software, G2 is rolling out a set of new product innovations aimed at reinforcing something many algorithms still struggle with: trust.

The B2B software marketplace and review platform announced a series of updates designed to help vendors increase visibility in AI-driven discovery while providing buyers with more credible signals during software evaluations. The new capabilities include richer buyer-generated content, LinkedIn-based identity verification for reviewers, an AI integration powered by Anthropic’s Claude, and expanded market intelligence tools for go-to-market teams.

Together, these updates reflect a larger shift in the B2B buying journey. As organizations increasingly rely on AI assistants and conversational search to shortlist vendors, the quality of the underlying data—reviews, buyer behavior, and market signals—becomes a decisive factor in which products get surfaced.

According to G2, its platform already processes signals from more than 200 million annual buyers and hosts over six million verified reviews. The company now wants those signals to become foundational inputs for AI-driven decision-making.

“AI is transforming how companies analyze markets and make decisions, but those systems need trusted data signals to produce meaningful insights,” said Alexis Zheng, Chief Product and Technology Officer at G2. Zheng described the new releases as part of the company’s effort to position G2 as the “trust layer” for the software ecosystem in the AI era.

New Buyer Content Designed for Humans—and AI

One of the biggest changes comes in how G2 structures user-generated feedback. The company is introducing several new formats aimed at extracting richer context from reviewers while making the information easier for AI engines to interpret.

The first is structured category FAQs. These provide authoritative answers to common questions across software categories, helping potential buyers quickly understand key features, limitations, and use cases. At the same time, the standardized format makes it easier for AI systems to ingest and reference the information when generating answers.

G2 is also introducing guided discussion prompts within reviews. Rather than leaving feedback entirely open-ended, these prompts encourage users to discuss implementation experiences, real-world use cases, and product trade-offs. The result is deeper contextual insight into how software performs beyond marketing claims.

Another update focuses on feature comparisons. Using signals from review data, G2 now automatically identifies how users naturally describe product capabilities and converts those insights into structured feature lists. This allows buyers to compare tools more quickly without manually scanning dozens of reviews.

Collectively, the goal is to make the buying journey more grounded in real user experiences—while simultaneously improving how AI systems interpret that information.

LinkedIn Verification Adds Identity to Reviews

In an era where AI-generated content is becoming increasingly difficult to distinguish from authentic feedback, G2 is also doubling down on reviewer credibility.

The company has expanded its partnership with LinkedIn by integrating LinkedIn’s identity verification directly into G2’s moderation workflow. Reviews can now display verification signals tied to a user’s professional identity, including their employer or educational background.

The move is intended to ensure that reviews reflect genuine professional experiences rather than anonymous commentary or automated submissions.

Early results from the integration suggest measurable improvements. Since launching the verification process, G2 reports collecting more than 100,000 reviews from LinkedIn-verified users. The platform has also seen a 40 percent drop in review rejection rates and a 13-point increase in approval rates, while moderation efficiency improved by roughly 25 percent.

“Trust in B2B buying starts with credibility,” said Adam Kahn, Senior Manager on LinkedIn’s Trust Team. “As AI-generated content becomes more prevalent, visible verification signals matter more than ever.”

Claude Integration Connects AI Assistants to G2 Data

Perhaps the most technically ambitious announcement is G2’s new Model Context Protocol (MCP) architecture, which allows AI assistants to directly access G2’s structured buyer intelligence.

The first integration connects the platform to Claude, the AI assistant developed by Anthropic.

Rather than relying solely on general web content or scraped information, the integration enables AI tools to reference verified buyer reviews, competitive research signals, and marketplace data from G2 in real time.

In practice, that means teams can ask AI systems questions like which competitors buyers are evaluating, which product strengths appear most frequently in reviews, or whether customers might be considering alternatives.

These insights could help sales, product, and customer success teams spot potential churn risks earlier—or identify accounts actively researching competing platforms.

The approach reflects a broader industry trend: enterprises increasingly expect AI tools not just to summarize public information but to integrate proprietary and high-quality datasets into workflows.

New Market Intelligence for Go-to-Market Teams

Alongside its AI integrations, G2 is expanding its analytics capabilities with new intelligence features aimed at helping vendors understand shifts in buyer behavior and competitive dynamics.

One of the key additions is Competitive Pulse, a dashboard that combines CRM opportunity data with G2 buyer intent signals and competitor research activity. The feature highlights deals that may be at risk and identifies areas where rival vendors are gaining traction.

Another addition is Churn Threat detection, which surfaces signals when existing customers begin researching competing products on G2. For customer success teams, that early warning could provide a crucial window to intervene.

G2 is also introducing analytics focused on Answer Engine Optimization (AEO), a growing discipline focused on how brands appear within AI-generated answers. The new AEO traffic insights show how often buyers discover products through conversational AI responses or AI-driven search results.

Meanwhile, expanded buyer intent data reveals which categories and vendors companies are actively researching across the G2 marketplace.

For investors and market analysts, the company is also introducing spend and contract intelligence based on more than $100 billion in SaaS purchasing agreements. By linking purchasing activity with research behavior, the dataset aims to provide a more accurate picture of category momentum and vendor growth.

The Bigger Picture: Trust as the New SEO

The timing of these announcements reflects a broader shift across the B2B technology landscape.

For years, software discovery has largely been driven by traditional search engines, vendor websites, and analyst reports. But as generative AI tools increasingly answer research questions directly, the sources those systems rely on are becoming strategic assets.

Platforms that host credible, structured, and verified data—like G2—are positioning themselves as the underlying knowledge layers for AI-driven buying decisions.

That dynamic could fundamentally reshape how vendors approach visibility. Instead of focusing solely on ranking in search results, companies may increasingly compete to appear in AI-generated answers backed by trusted third-party signals.

G2 unveiled the new capabilities during its latest quarterly Innovation Event, titled “Winning with Trust in AEO,” where executives emphasized that verified identity, authentic buyer voice, and real behavioral data will become essential inputs for AI-driven discovery.

If that vision holds, the future of software marketing may depend less on who shouts the loudest—and more on whose customers speak most credibly.

Get in touch with our MarTech Experts.

Moments captured on smartphones rarely come out perfectly. Someone blinks, the lighting is off, or the camera angle misses the mark. Instead of settling for those imperfect memories, Wondershare believes AI can give users a second chance.

The creativity and productivity software company has introduced Relumi, a new AI-powered mobile application designed to improve flawed photos and effectively “retake” images after the moment has passed. The app combines advanced AI models with image reconstruction capabilities to fix common photo issues, adjust expressions, and even modify camera angles—while preserving the original context of the scene.

The launch reflects a broader shift in consumer imaging software, where artificial intelligence is increasingly moving beyond traditional editing tools toward generative enhancement. Instead of simply tweaking brightness or cropping frames, modern AI apps are beginning to rebuild visual elements to create more polished versions of real-world moments.

AI Moves Beyond Photo Editing

Smartphone photography has exploded over the past decade, fueled by social media sharing and increasingly powerful camera hardware. But despite better sensors and computational photography, real-life conditions—lighting, timing, and positioning—still lead to imperfect captures.

Wondershare positions Relumi as a solution to that gap. Rather than requiring users to retake photos in the moment, the app uses AI to reconstruct and refine images afterward.

According to the company, the technology is powered by advanced AI models, including an integration with Nano Banana Pro, which enables the app to analyze visual elements in a photo and intelligently modify them while maintaining realistic results.

“Photos are more than images—they are memories,” said Dirk, Head of Product at Wondershare. “With Relumi, we are redefining what it means to ‘retake’ a photo. Instead of missing the moment, users can now go back and take it better.”

Fixing Faces, Poses, and Group Photos

One of Relumi’s primary capabilities focuses on repairing portrait flaws. The Photo Flaw Repair feature automatically detects issues such as closed eyes, awkward facial expressions, or minor pose inconsistencies. Once identified, the system adjusts the affected elements while preserving the original lighting and background details.

For group photos—a notoriously difficult category—Relumi introduces Multi-Person Photo Repair. The feature isolates individual subjects within a group image and corrects problems like blinking or unflattering expressions independently.

That capability could prove particularly useful for event photography, where retakes are often impossible. A single group shot from a wedding, conference, or family gathering can now be refined without altering the rest of the scene.

AI-Driven Lighting and Atmosphere

Beyond facial adjustments, Relumi also focuses on environmental improvements. The Smart Environment Preset Retake feature analyzes the scene’s lighting conditions and recommends optimized mood presets.

Users can apply cinematic-style enhancements that modify lighting, tone, and atmosphere without manual editing. The system attempts to maintain natural realism while giving photos a more polished aesthetic—something typically reserved for professional editing workflows.

Changing Camera Angles After the Fact

Perhaps the most technically ambitious feature is 3D Angle Adjustment Retake, which uses AI-based modeling to reconstruct images from alternative perspectives.

By generating a three-dimensional understanding of the scene, the tool can correct distorted selfies or shift the apparent camera viewpoint. For example, a selfie taken from an awkward angle could be rebalanced to appear more natural.

This type of AI-driven perspective correction is emerging as a new frontier in computational photography, combining elements of image generation, depth estimation, and 3D reconstruction.

Turning Photos into Animated Memories

Relumi also expands beyond still images with its Photo-to-Video with Sound feature.

The tool animates static photos by generating subtle movements such as facial micro-expressions or environmental motion. It can also add audio-enhanced elements to create short video clips suitable for social media sharing.

As platforms like TikTok and Instagram increasingly favor video content, this functionality allows users to transform older photos into dynamic posts without recording new footage.

Targeting Creators, Families, and Memory Keepers

Wondershare says Relumi is designed for a wide range of users—from casual smartphone photographers to social media creators.

Families preserving milestone moments, travelers capturing scenic experiences, and digital creators looking for more engaging visual content could all benefit from the app’s AI capabilities. By automating complex editing processes, the platform aims to make high-quality image enhancement accessible without professional design skills.

This approach mirrors a broader industry trend. AI-driven creative tools are rapidly lowering the technical barrier for photo and video production, enabling everyday users to achieve results that previously required advanced editing software.

Availability

Relumi is available as a mobile application for both iOS and Android devices. Users can download the app and explore its features directly on their smartphones, with additional details available through Wondershare’s official website and social media channels.

As generative AI continues to reshape creative software, tools like Relumi highlight how the next phase of photo editing may focus less on correction—and more on reconstruction.

Get in touch with our MarTech Experts.

Artificial intelligence is rapidly redefining how digital marketing strategies are built, executed, and optimized. As organizations manage larger datasets and increasingly complex consumer behavior patterns, AI-powered systems are becoming central to marketing operations across industries.

From audience targeting to campaign optimization, AI-driven platforms are helping marketing teams analyze data faster and adapt strategies in near real time. The shift marks a departure from traditional marketing workflows that relied on static datasets and scheduled adjustments, replacing them with dynamic systems capable of responding continuously to changing performance signals.

From Static Campaigns to Adaptive Marketing

Historically, digital marketing campaigns were planned around fixed timelines and periodic optimization cycles. Campaign managers would analyze performance reports, make adjustments, and relaunch initiatives based on historical results.

AI-driven systems are changing that model. Modern marketing platforms now process real-time engagement signals and automatically refine campaign parameters such as messaging, timing, and distribution channels.

This adaptive approach allows campaigns to evolve as consumer behavior shifts. For example, if engagement trends change mid-campaign, AI tools can modify audience targeting or ad placement without waiting for manual intervention.

The result is a more responsive marketing environment where performance improvements can occur continuously rather than through scheduled optimization cycles.

Audience Targeting Becomes More Behavioral

Audience segmentation is also undergoing a transformation as AI systems analyze behavioral signals at scale.

Instead of relying solely on demographic attributes, AI-powered platforms increasingly evaluate interaction history, browsing behavior, and engagement patterns to identify audience intent. These insights enable marketers to build highly granular segments and tailor campaigns to users whose behavior indicates a higher likelihood of interest or conversion.

This shift reflects a broader trend toward individualized digital experiences. As personalization becomes an expectation rather than a novelty, AI systems provide the analytical backbone that enables marketers to deliver more relevant messaging.

AI Influences Content Strategy

Content marketing workflows are also being influenced by AI-assisted analysis.

Marketing teams are increasingly using predictive tools that evaluate search behavior, keyword trends, and audience interest patterns. These insights help guide editorial planning, allowing brands to develop content aligned with current demand.

Rather than relying entirely on manual forecasting, marketers can now incorporate predictive signals into content calendars. This approach improves consistency across channels while helping teams respond more quickly to emerging topics and shifting search trends.

For organizations managing large content ecosystems, these tools can significantly streamline planning processes while supporting more data-driven decision-making.

Advertising Automation Expands

AI is also playing a growing role in paid advertising management.

Many advertising platforms now incorporate automation features capable of adjusting campaign parameters in real time. Budget allocation, bidding strategies, and audience targeting can be optimized algorithmically based on engagement and conversion data.

These automated adjustments are designed to improve campaign efficiency and maximize return on ad spend while reducing the need for constant manual oversight.

For marketers, the shift means that strategic planning increasingly focuses on campaign objectives and creative direction, while optimization tasks are handled by automated systems.

Email Marketing Adopts AI Optimization

Email marketing platforms are adopting similar AI-driven capabilities.

Automation tools are being used to determine optimal send times, personalize messaging, and refine customer journey workflows. By analyzing recipient behavior—including open rates, click patterns, and past engagement—AI systems can continuously refine email campaigns to improve performance.

These systems also enable marketers to create more responsive automation sequences that adjust based on subscriber behavior, helping maintain relevance throughout the customer lifecycle.

Search Strategies Adjust to AI Algorithms

The influence of artificial intelligence extends beyond marketing tools themselves. Search engines and digital platforms are increasingly relying on AI-driven algorithms to determine content visibility.

Search ranking systems now place greater emphasis on user intent, contextual relevance, and overall experience. As a result, digital marketing strategies are evolving to prioritize structured content, technical site performance, and accessibility.

Automated SEO monitoring tools are also becoming more common, helping marketing teams track site health and identify performance issues that could affect search visibility.

Social Platforms Lean on AI Distribution

Social media platforms have also embedded AI deeply into their content distribution models.

Feed algorithms analyze engagement patterns and interaction history to determine which content appears in front of users. For marketers, this means that engagement signals—such as comments, shares, and watch time—play a significant role in determining content reach.

As a result, social media strategies increasingly emphasize content designed to drive meaningful interaction rather than simply maximizing posting frequency.

Data Consolidation Becomes Essential

The growing role of AI in marketing is also changing how organizations approach data analysis.

Modern marketing operations generate large datasets across websites, advertising platforms, social media channels, and email campaigns. AI systems are increasingly used to consolidate and interpret these datasets, enabling organizations to identify trends and uncover insights that would be difficult to detect manually.

This centralized analysis supports more informed strategic decisions and helps marketers respond quickly to shifts in consumer behavior.

Balancing Automation With Human Strategy

Despite the expanding capabilities of AI systems, human expertise remains a critical component of digital marketing.

Strategic planning, brand positioning, and creative storytelling continue to rely heavily on human insight. AI tools can process data and optimize execution, but defining campaign objectives and crafting compelling narratives still requires human direction.

Brett Thomas, owner of the New Orleans-based firm Jambalaya Marketing, emphasized the evolving relationship between automation and strategy.

“Marketing strategies are becoming more dynamic as AI systems process data and adjust campaigns in real time,” Thomas said. “The focus is shifting toward systems that respond to behavior rather than relying on static planning.”

Governance and Data Integrity Challenges

As organizations integrate AI technologies into marketing workflows, operational considerations are also emerging.

Maintaining data accuracy and consistency is essential for ensuring reliable AI outputs. Inaccurate or incomplete data can lead to flawed insights and ineffective campaign decisions.

Companies are also evaluating governance frameworks to ensure that data usage aligns with privacy regulations and transparency standards. These considerations are becoming increasingly important as AI-driven marketing systems rely on large volumes of behavioral and engagement data.

The Next Phase of AI-Driven Marketing

The role of artificial intelligence in digital marketing is expected to expand as platforms continue to evolve and data availability increases.

Future developments will likely focus on deeper automation, predictive intelligence, and cross-channel integration. Marketing ecosystems are moving toward systems capable of responding to consumer behavior in real time while continuously optimizing performance.

For organizations navigating competitive digital environments, AI is increasingly becoming not just a tool—but a core operational capability shaping how marketing strategies are developed and executed

Get in touch with our MarTech Experts.

Enterprise analytics provider SplashBI is strengthening its North American sales leadership as demand for AI-powered data intelligence continues to grow.

The company announced that Brian Morgan has joined the organization as Regional Sales Director for North America, bringing more than 15 years of enterprise technology sales experience and a background in workforce analytics and talent intelligence.

The appointment signals SplashBI’s continued push to expand its footprint among enterprise organizations seeking to transform fragmented data into actionable business insights.

Expanding Enterprise Analytics Adoption

SplashBI has been positioning its platform as a unified environment for enterprise reporting, analytics, and decision intelligence. The company’s AI-powered system integrates data from across organizational systems—finance, HR, and operational platforms—to surface insights automatically and deliver real-time answers to business teams.

For many organizations, the challenge is not collecting data but translating it into decisions. As enterprises generate massive volumes of workforce and operational information, analytics platforms are increasingly expected to provide predictive insights rather than static reports.

Morgan’s role will focus on accelerating adoption of SplashBI’s platform among North American enterprises navigating that shift.

“Organizations today are under increasing pressure to get more from their data—faster and with greater confidence,” the company noted in announcing the appointment. Morgan’s experience working with finance and workforce leaders positions him to guide those conversations as companies evaluate AI-driven analytics platforms.

Deep Experience in Workforce and People Analytics

Morgan joins SplashBI from Crunchr, where he served as Director of Sales for North America. At Crunchr, he focused on enterprise workforce analytics solutions that help organizations interpret employee data to support planning and performance decisions.

His broader career includes leadership and strategic sales roles at companies focused on HR technology and workforce intelligence, including Workhuman, Gloat, and SkyHive.

That background reflects a growing area of enterprise analytics: translating workforce data into operational insights. HR and talent systems generate significant datasets—from performance metrics to workforce planning signals—but many organizations still struggle to integrate and interpret that information at scale.

Morgan’s expertise in this space could help SplashBI connect its analytics platform to one of the fastest-growing data categories inside large enterprises.

Strengthening SplashBI’s North American Strategy

In his new role, Morgan will focus on expanding SplashBI’s enterprise presence across North America. The position involves working closely with the company’s marketing, customer success, and product teams to introduce organizations to the platform’s analytics capabilities.

The strategy reflects a broader trend in enterprise software: platforms that combine reporting, analytics, and AI-powered insights into unified environments rather than isolated tools.

Organizations increasingly want analytics systems that can serve multiple departments—from HR and finance to operations—while maintaining consistent data governance and trusted insights.

SplashBI’s platform aims to address that need by connecting data sources across the enterprise and automatically surfacing insights that teams can act on in real time.

AI-Driven Decision Intelligence Gains Momentum

The appointment also comes as enterprise analytics platforms evolve from traditional reporting tools into decision intelligence systems.

Rather than simply presenting dashboards or historical reports, modern analytics solutions incorporate machine learning models that identify patterns, flag anomalies, and recommend actions based on current data signals.

As companies seek faster and more confident decision-making, platforms capable of combining automation, analytics, and AI are becoming central to enterprise technology stacks.

Morgan’s experience selling workforce intelligence and AI-driven analytics solutions aligns with that direction, positioning him to help organizations adopt tools that transform raw data into strategic insights.

Academic Background

Morgan holds a Bachelor of Business Administration in Marketing from the Isenberg School of Management at University of Massachusetts Amherst.

Get in touch with our MarTech Experts.

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.

Get in touch with our MarTech Experts.

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.

Get in touch with our MarTech Experts.