As search engines morph into answer engines, one Hyderabad-based agency is retooling for the shift.

BeTopSEO has officially launched AI-Powered SEO and Generative Engine Optimization (GEO) services, positioning itself for a world where ranking on page one is no longer the only goal. Instead, the agency is targeting visibility inside AI summaries, featured snippets, local map packs, and conversational search results—places where traditional keyword tactics often fall short.

The move reflects a broader industry pivot. With platforms like Google AI Overviews reshaping how information is surfaced, brands are discovering that organic blue links don’t guarantee attention. Increasingly, search queries are answered directly by AI-generated summaries. If your business isn’t embedded in that answer layer, you risk becoming invisible.

From Keywords to Entities

BeTopSEO’s new offering blends AI SEO Services, Answer Engine Optimization (AEO), Local SEO, and advanced technical SEO into a unified strategy. The emphasis: structured data, entity building, and authoritative content designed for machine interpretation—not just human readers.

Founder and SEO Strategist Sandeep describes the shift as “seismic,” and the language isn’t hyperbole. The rise of AI summaries and conversational interfaces is forcing agencies to rethink core optimization principles. Instead of chasing high-volume keywords alone, brands now need to establish structured credibility signals that AI systems can trust and cite.

That means schema markup, entity consistency across the web, knowledge graph alignment, and topical authority clusters—areas where many traditional SEO campaigns have lagged.

GEO: The Next Acronym in Search Marketing

Generative Engine Optimization (GEO) is the agency’s headline addition. While the term is still gaining traction, the concept is straightforward: optimize content and digital assets so they are referenced, summarized, or surfaced within generative AI responses.

If SEO was about ranking pages and AEO is about earning featured snippets, GEO is about becoming part of the AI-generated narrative.

For startups, healthcare providers, real estate firms, and e-commerce brands—the verticals BeTopSEO primarily targets—this can translate into higher visibility in AI-powered summaries and voice or chat-driven search experiences.

The company also integrates Google and Meta Ads services into its strategy, aiming for a full-funnel approach that combines paid amplification with organic authority. In an era where organic click-through rates may decline due to AI summaries, blending performance marketing with AI-focused optimization could be less optional and more essential.

Why This Matters Now

Search behavior is fragmenting. Users are asking longer, more conversational queries. AI tools are synthesizing results rather than listing them. And local intent is often resolved before a user ever clicks a website.

For agencies and brands, that creates both risk and opportunity:

• Risk: Reduced website traffic if AI answers replace clicks.

• Opportunity: Increased brand authority and visibility if cited within AI responses.

BeTopSEO’s bet is that businesses in Hyderabad—and across India—need to act now rather than react later. Early adopters of AI-aware optimization strategies could secure entity authority before competition intensifies.

The agency emphasizes measurable outcomes: increased organic traffic, higher-quality leads, and stronger digital authority. But in the AI era, authority may matter more than raw traffic. If a brand becomes a trusted source in AI summaries, it earns positioning that competitors can’t easily buy.

The Competitive Landscape

Globally, major SEO platforms and agencies are beginning to incorporate AI-focused frameworks into their services. However, regional firms that tailor these strategies to local markets may have an advantage. Local SEO—especially in competitive urban hubs like Hyderabad—remains critical for healthcare clinics, real estate developers, and service-based businesses.

Appearing in AI-generated summaries for “best cardiologist near me” or “top real estate projects in Hyderabad” could dramatically impact lead generation.

By layering GEO and AEO onto traditional technical and local SEO, BeTopSEO is aligning with the next iteration of search marketing rather than defending the last one.

A Shift From Visibility to Trust

If SEO 1.0 was about ranking and SEO 2.0 was about user experience, the emerging phase is about trust signals interpretable by machines.

Structured data, entity reinforcement, and authoritative content ecosystems are becoming prerequisites for visibility in AI ecosystems. Agencies that fail to evolve risk delivering diminishing returns as search interfaces change.

BeTopSEO’s launch underscores a larger reality: the future of search optimization isn’t just about being found. It’s about being understood—and selected—by AI.

Whether GEO becomes a mainstream discipline or another industry buzzword remains to be seen. But one thing is clear: businesses that ignore the AI layer of search may soon find themselves optimized for a landscape that no longer exists.

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As AI assistants increasingly replace traditional search results, brands are discovering a harsh reality: measuring AI visibility is easy. Influencing it is not.

Mersel AI, Inc. this week launched its Generative Engine Optimization (GEO) execution platform, aimed squarely at helping companies improve how they appear inside AI-generated answers and recommendations across major assistants.

That includes platforms like ChatGPT, Perplexity AI, Gemini, and Claude—tools that are rapidly becoming the first stop for product research, vendor comparisons, and category discovery.

The pitch is straightforward: visibility dashboards don’t fix invisibility. Execution does.

From AI Visibility Metrics to AI Eligibility

Over the past year, a wave of AI visibility tools has emerged, promising to track brand mentions, prompt-level position, and share of voice inside generative AI answers. For marketing and growth teams, that data can be illuminating—and occasionally alarming.

But as Mersel AI points out, simply knowing you’re absent from AI responses doesn’t mean you know how to change it.

Large language models cite and summarize sources based on structured clarity, semantic consistency, and credibility signals. If your product data is ambiguous, inconsistently presented, or thinly supported off-site, measurement alone won’t move the needle.

Mersel AI’s solution is an “agent-as-a-service” model designed to operationalize GEO. Instead of licensing a tool and assigning another dashboard to an already overloaded team, the company positions itself as an execution layer that ships changes continuously.

Founder Joseph Wu frames the issue bluntly: many teams can measure where they’re missing in AI answers, but they lack the infrastructure to implement the fixes at scale.

A Four-Layer Approach to AI Citation

The GEO execution platform focuses on four operational pillars that influence how AI systems interpret and recommend brands.

1. A Machine-Readable Layer for Websites

Rather than requiring a full website rebuild, Mersel AI adds a structured, machine-readable layer over existing sites. This includes schema markup, structured data, and semantic signals designed to clarify product attributes, pricing context, policies, and positioning.

The goal is to reduce ambiguity. AI systems favor content that is easier to parse and less prone to misinterpretation. If a product’s specifications or policies are inconsistently formatted across pages, models may hesitate to summarize or cite them confidently.

2. Content Structured for AI Summarization

Traditional SEO content often prioritizes keyword density and long-form coverage. GEO content, by contrast, must be extractable.

Mersel AI supports recurring publication of prompt-aligned content built around real AI query patterns—comparisons, category overviews, use cases, and decision-stage questions. The structure is engineered for summarization, enabling language models to lift key points with minimal friction.

In practice, that means clear fact blocks, consistent terminology, and tightly scoped explanations that map cleanly to how AI assistants generate responses.

3. Off-Site Trust Signals

AI systems don’t rely solely on on-page content. They cross-reference review sites, social platforms, and editorial sources to validate claims and establish credibility.

Mersel AI says it strengthens third-party presence through internal agentic tools that reinforce brand signals across relevant external platforms. In crowded categories where messaging converges, these signals may influence whether a brand is cited as a recommendation or omitted altogether.

4. Measurement Tied Directly to Iteration

Unlike standalone monitoring tools, Mersel AI connects cross-platform AI visibility tracking to shipped updates. It measures brand-mention rates, prompt-level positioning, and competitive share of voice—then uses those insights to guide subsequent changes.

This creates a feedback loop: measure, implement, reassess, repeat.

Why GEO Is Gaining Momentum

Generative Engine Optimization is emerging as a parallel discipline to traditional SEO and Answer Engine Optimization (AEO). While SEO targets ranking positions in search results, GEO targets presence within AI-generated narratives.

The stakes are rising quickly. As conversational interfaces become default research tools, fewer users may scroll through multiple links. Instead, they rely on summarized answers and curated recommendations.

For brands, that means the battle for visibility is shifting from page rankings to citation eligibility.

The challenge is that AI ecosystems evolve constantly. Model updates, prompt trends, and citation behaviors can change without notice. For many companies, building an internal GEO team to track and respond to these shifts may be impractical.

Mersel AI is betting that outsourcing execution—rather than just analytics—will resonate with organizations that need continuous adaptation without expanding headcount.

The Bigger Shift: Tools vs. Outcomes

The broader marketing technology landscape is moving from software licensing to outcome-based services. AI tooling has lowered the barrier to insight, but not necessarily to impact.

Mersel AI’s agent-as-a-service positioning reflects that shift. Instead of adding another interface to the stack, it aims to deliver iterative implementation tied directly to AI platform behavior.

If AI assistants continue to displace traditional search journeys, GEO may become less of a niche experiment and more of a baseline requirement.

For now, Mersel AI is staking its claim early in what could become a highly competitive segment: helping brands not just be visible to AI—but be chosen by it.

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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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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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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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Customer experience platforms are under pressure to do more than promise transformation. They have to show measurable gains—faster resolutions, tighter forecasting, and smoother cross-channel journeys.

8x8, Inc. is positioning its latest updates as exactly that: practical AI enhancements embedded directly into the 8x8 Platform for CX. Rather than bolting on generative features, the company says it has woven AI into the operational core of its contact center, workforce management, and collaboration stack.

The result, according to 8x8, is lower handle times, improved forecast accuracy, and more seamless customer engagement across channels.

AI at the Point of Conversation

The headline update centers on speed and context. With Customer 360, 8x8 turns its Agent Workspace into a unified customer hub, pulling together cross-channel history, profile data, and AI-driven insights such as sentiment analysis and top discussion topics.

Instead of toggling between tools, agents see everything in one interface. That consolidated view aims to shorten resolution cycles and make personalization less dependent on memory and more dependent on data.

Hunter Middleton, Chief Product Officer at 8x8, was explicit about the company’s positioning: this isn’t “AI-washing.” The focus, he says, is on reducing operational friction and improving customer outcomes at scale.

In a market where AI features are proliferating across CX vendors, embedding automation directly into workflows—not just dashboards—has become the new battleground.

Workforce Management, No Add-On Required

Another notable move: 8x8 Workforce Management is now included in every 8x8 Contact Center package.

That means forecasting, scheduling, and shift management are no longer optional extras. By bundling WFM capabilities into the base offering, 8x8 is responding to a key enterprise pain point—tool sprawl.

Accurate forecasting isn’t just a back-office metric. It determines staffing levels, wait times, and ultimately customer satisfaction. By tightening forecast accuracy and streamlining shift management, 8x8 aims to reduce the gap between predicted and actual service demand.

In an environment of tightening margins, operational precision matters as much as customer delight.

Collaboration Meets Compliance

The updates also extend beyond the contact center. 8x8 Work, the company’s unified communications layer, now includes enhanced meeting scalability controls and navigation improvements aligned with WCAG accessibility standards.

There’s also stronger real-time visibility into staff coverage, along with self-service controls that help teams adjust quickly to spikes in demand.

This reflects a broader industry trend: the line between contact center and internal collaboration is dissolving. Customers don’t care whether their issue spans support, billing, or sales—they expect continuity. Platforms that unify communications and CX infrastructure have a structural advantage in delivering that consistency.

WhatsApp Gets a Bigger Role

Customer engagement doesn’t stop at voice or web chat. Businesses can now engage customers via interactive flows and one-tap voice calling through WhatsApp.

Interactive messaging on WhatsApp can reduce friction in common workflows—appointment confirmations, service updates, order tracking—while escalating seamlessly to voice when needed.

8x8 also introduced automated MM Lite onboarding and WhatsApp Business App plus Cloud API co-existence. In practical terms, this allows organizations to scale messaging campaigns and automation without disrupting existing setups or compromising data protection.

As messaging platforms continue to dominate global customer interactions, tighter integration with WhatsApp is less a feature and more a necessity.

One Platform Strategy

The strategic thread tying these updates together is consolidation. The 8x8 Platform for CX unifies:

• Contact center

• Unified communications

• Communication APIs

All on a single AI-powered foundation.

That unified architecture is increasingly important as enterprises look to simplify vendor stacks. Multiple disconnected systems may offer best-of-breed capabilities, but they often create fragmented data and inconsistent workflows.

By embedding AI across a single platform, 8x8 is betting that integrated intelligence delivers stronger business momentum than isolated automation.

Competitive Context

The CX market is crowded with vendors layering generative AI onto legacy systems. The differentiator now is execution—how deeply AI is integrated and how directly it impacts measurable KPIs.

Reducing average handle time. Improving forecast accuracy. Increasing first-contact resolution. Those are metrics that CFOs and COOs track, not just CX leaders.

If 8x8 can demonstrate sustained improvements in those areas, it strengthens its case against competitors offering either standalone contact center solutions or communications platforms without deep CX integration.

The Bottom Line

AI in customer experience has moved beyond novelty. Enterprises want operational leverage.

With its latest updates, 8x8 is positioning AI as a built-in force multiplier across conversation context, workforce management, collaboration, and messaging. The message is clear: AI shouldn’t just sound intelligent—it should shorten queues, tighten forecasts, and accelerate outcomes.

In a climate where customer expectations are rising and budgets are under scrutiny, that practical focus may be exactly what the market demands.

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AI experimentation is easy. AI you can trust at scale? That’s harder.

As enterprises move from pilot projects to business-critical AI deployments, the central question is no longer access to models. It’s oversight. Today, Dataiku is tackling that problem head-on with the launch of the 575 Lab, its new Open Source Office focused on building trust infrastructure for modern AI systems.

The initiative debuts with two open-source toolkits aimed at making enterprise AI more transparent, governable, and secure—particularly in the emerging world of agentic AI systems.

From AI Access to AI Accountability

For the past two years, enterprises have raced to integrate large language models and AI agents into workflows. But as these systems take on more autonomous roles—triggering actions, making recommendations, and orchestrating multi-step processes—the governance challenge has intensified.

Open source, Dataiku argues, offers a structural advantage.

Hannes Hapke, Director of the 575 Lab, frames it succinctly: open source isn’t just a distribution model—it’s a trust model. When core components are inspectable and standardized, enterprises can verify how systems operate rather than relying on opaque assurances.

That philosophy underpins the lab’s first two projects.

Agent Explainability Tools: Opening the Black Box

The first toolkit focuses on agent explainability.

Modern AI agents often execute multi-step workflows—pulling data, reasoning over it, calling tools, and making decisions. While impressive, these layered actions can be difficult to trace.

Dataiku’s Agent Explainability Tools are designed to help teams:

• Trace decision-making across multi-step agent workflows

• Understand how conclusions were reached

• Provide visibility for data scientists, compliance teams, and end users

In regulated industries, that traceability isn’t optional. Whether it’s financial services evaluating risk decisions or healthcare systems managing patient workflows, the ability to explain “why” is as important as the output itself.

As agentic ecosystems grow more complex, explainability tools could become foundational rather than supplementary.

Privacy-Preserving Proxies: Safer Use of Closed Models

The second project tackles another enterprise tension: leveraging powerful closed-source models while protecting sensitive data.

Privacy-Preserving Proxies are designed to:

• Protect sensitive data end-to-end

• Enable safer interaction with closed-source models

• Run locally within enterprise environments

Many organizations hesitate to send proprietary or regulated data into external AI APIs. By introducing proxy layers that sanitize and manage data flows, Dataiku aims to reduce that risk without sacrificing access to high-performing models.

This reflects a broader industry shift. Enterprises increasingly want hybrid AI stacks—combining open and closed models, internal tools, and external APIs. Governance layers that mediate those interactions are becoming critical infrastructure.

Open Standards for Agentic AI

The 575 Lab builds on Dataiku’s decade of enterprise AI experience and extends its involvement in the open-source ecosystem. The company is a member of the Linux Foundation and the Agentic AI Foundation, signaling an intent to collaborate rather than operate in isolation.

Florian Douetteau, CEO and co-founder of Dataiku, emphasizes reusable building blocks as the goal. As enterprises construct increasingly complex agentic ecosystems, standardized control and inspection mechanisms will likely emerge as industry norms. By contributing these tools in the open, Dataiku hopes to help shape those standards.

The timing is strategic. As regulatory scrutiny intensifies globally, enterprises are under pressure to demonstrate responsible AI practices. Toolkits that support explainability, privacy, and governance may soon be prerequisites for large-scale deployments.

Competitive and Market Context

Enterprise AI platforms are rapidly adding governance features—model monitoring, bias detection, compliance reporting. What differentiates 575 Lab is its open-source orientation.

Rather than locking governance capabilities inside proprietary systems, Dataiku is pushing foundational components into the open. That approach may appeal to large enterprises wary of vendor lock-in and eager to align with emerging community standards.

At the same time, open-source governance tools can accelerate adoption by enabling cross-platform compatibility. In agentic AI environments where multiple vendors’ systems interact, interoperability matters.

If successful, 575 Lab could position Dataiku not just as an AI platform provider, but as a contributor to the trust infrastructure underpinning enterprise AI at large.

Availability and Community Involvement

The 575 Lab is now open to AI specialists, data scientists, developers, and enterprise partners. Community members can follow the projects, contribute, and help shape what Dataiku describes as “open trust infrastructure” for AI at scale.

That community-driven approach aligns with the broader open-source ethos: transparency, collaboration, and shared accountability.

The Bottom Line

As AI systems become more autonomous and more consequential, enterprises need more than model access. They need visibility, control, and standards they can rely on.

With 575 Lab, Dataiku is betting that trust in AI will be built not just through performance benchmarks, but through open, inspectable foundations. In the race toward agentic enterprise systems, governance may prove to be the most valuable innovation of all.

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Design software darling Figma just delivered its strongest quarter on record—and it’s doing so while reshaping itself into a broader AI-powered product development platform.

For the fourth quarter of 2025, Figma reported $303.8 million in revenue, up 40% year-over-year and above guidance. For the full year, revenue crossed the billion-dollar mark for the first time, reaching $1.056 billion, a 41% annual increase.

If 2024 was about IPO headlines and post-merger drama, 2025 was about operational momentum.

The Financials: Growth With Discipline

Q4 marked Figma’s best quarter for net new revenue on record. Key highlights include:

• Revenue: $303.8 million (up 40% YoY)

• Non-GAAP operating income: $44.0 million (14% margin)

• Operating cash flow: $39.9 million (13% margin)

• Cash and marketable securities: $1.7 billion

For the full fiscal year:

• Revenue: $1.056 billion (up 41% YoY)

• International revenue growth: 45%

• Operating cash flow: $250.7 million (24% margin)

• Adjusted free cash flow margin: 23%

On a GAAP basis, the company posted a $1.3 billion net loss for the year, largely driven by a one-time $975.7 million stock-based compensation expense tied to its IPO. Strip that out, and Figma reported $166.8 million in non-GAAP net income.

In other words: headline losses, but underlying profitability and cash generation look healthy.

Enterprise Expansion and Retention

Figma’s enterprise penetration continues to deepen:

• Net Dollar Retention Rate: 136%

• 13,861 customers with more than $10,000 in ARR

• 1,405 customers above $100,000 in ARR

• 67 customers exceeding $1 million in ARR

A 136% retention rate signals strong expansion within existing accounts—an indicator that Figma isn’t just landing teams; it’s embedding itself across organizations.

CFO Praveer Melwani emphasized platform-led adoption across enterprise and international markets as a key growth driver. The company enters 2026 with projected first-quarter revenue between $315 million and $317 million, implying 38% growth. Full-year 2026 guidance points to roughly 30% growth.

That’s slower than 2025, but still elite territory for a company of this scale.

AI Is Now the Growth Engine

While financials tell one story, product evolution tells another.

Figma is no longer “just” a design tool. Its AI initiatives are expanding how teams ideate, prototype, and ship.

Weekly active users of Figma Make—its AI-powered app-building and prototyping tool—grew over 70% quarter-over-quarter. Notably, more than half of customers generating over $100,000 in ARR are now building in Figma Make weekly.

Even more telling: over 80% of Figma Make’s weekly active users on Full seats also used Figma Design during the quarter. That cross-product usage suggests AI features are enhancing, not cannibalizing, core workflows.

Deepening AI Integrations

Figma expanded its AI ecosystem aggressively in Q4:

• Support for experimental models Gemini 3 Pro and Claude Opus 4.6 within Figma Make

• “Claude Code to Figma,” allowing UIs generated in Claude Code to import directly into Figma’s canvas as editable layers

• Launch of Figma MCP app inside Claude, enabling diagram and Gantt chart creation via chat

• Expanded integration with ChatGPT to generate FigJam diagrams, Buzz marketing assets, and Slides presentations

The partnership with Anthropic reflects a broader AI ecosystem strategy. Rather than building a closed system, Figma is integrating deeply with leading AI platforms.

In a world where prompts increasingly initiate product workflows, Figma wants to be the canvas where those outputs are refined, iterated, and shipped.

Image Editing and the Weavy Acquisition

Figma also launched three AI-powered image editing tools directly inside its canvas. Complementing that move, it acquired Weavy—now rebranded as Figma Weave—which combines leading AI models with professional editing tools in a browser-based environment.

That acquisition signals Figma’s intent to expand beyond UI design into broader creative workflows, potentially competing more directly with creative tool incumbents.

Global Expansion: India in Focus

Figma opened a new office in Bengaluru and announced local data hosting and governance support for enterprise customers in India, now its second-largest market by monthly active users.

With international revenue growing 45% year-over-year, global expansion is no longer a side story—it’s central to the company’s growth thesis.

Strategic Positioning in the Product Stack

CEO Dylan Field framed Figma’s role as central to the product development stack—whether work begins in a terminal, a prompt box, or a hand-drawn sketch.

That positioning matters. As AI blurs the lines between design and development, Figma is aiming to remain the connective layer between ideation and execution.

Its abandoned merger with Adobe is now history. What remains is a publicly traded company with strong cash reserves, accelerating AI integration, and expanding enterprise adoption.

The Outlook

For 2026, Figma projects:

• Q1 revenue: $315–$317 million

• Full-year revenue: $1.366–$1.374 billion

• Non-GAAP operating income: $100–$110 million

Growth is expected to moderate from 41% to roughly 30%, but with sustained profitability and expanding platform adoption, that slowdown appears more like normalization than weakness.

The Bottom Line

Figma’s latest earnings show a company scaling rapidly while evolving into an AI-powered collaboration platform. Revenue growth remains strong, enterprise retention is high, and AI adoption is accelerating across its ecosystem.

As product teams increasingly begin their workflows in AI tools like Claude and ChatGPT, Figma is positioning itself not as a replacement—but as the creative control center where AI outputs become polished products.

If 2025 proved it could grow post-IPO, 2026 will test whether AI-driven platform expansion can sustain that momentum.

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