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.

Get in touch with our MarTech Experts.

As financial advisors double down on in-person seminars to drive client acquisition, one marketing tech firm is betting big on infrastructure to make those events more predictable—and more profitable.

AcquireUp, a technology-first seminar marketing company serving financial professionals, has appointed Jim Parkinson as its new Chief Technology and Information Officer. The move signals a deeper push into AI, platform scalability, and data-driven performance measurement for advisors relying on educational seminars as a primary growth channel.

Parkinson will oversee product development, data sciences, IT engineering, and infrastructure, with a mandate to strengthen AcquireUp’s managed marketing services and its proprietary LeadJig platform.

Why This Hire Matters

Seminar marketing isn’t new. What’s changing is how it’s measured, optimized, and automated.

Financial advisors have long used live educational events to build trust and convert attendees into clients. But the operational side—lead tracking, follow-ups, conversion analytics, compliance guardrails—has often lagged behind the sophistication seen in digital marketing stacks.

AcquireUp is positioning LeadJig as a modernized answer to that gap: a platform that brings structured data, workflow automation, and increasingly AI-driven insights to what has historically been a manual and relationship-heavy process.

Parkinson’s appointment suggests the company is serious about transforming that stack into something more scalable—and more defensible.

“Jim’s depth of experience building scalable platforms and leading complex technology organizations makes him a tremendous addition,” said CEO Greg Bogich, noting that Parkinson will help advisors more predictably convert seminars into net new asset growth.

Predictability is the keyword. In wealth management, growth strategies that can’t be measured precisely don’t scale well—and they don’t inspire confidence from compliance teams or enterprise RIAs.

A Cloud and Platform Veteran Steps In

Parkinson isn’t a niche martech hire. His résumé reads more like that of a Silicon Valley infrastructure architect.

He previously spent more than two decades at Sun Microsystems, where he held multiple senior leadership roles, including Senior Vice President of Software Products and Cloud Computing Engineering. During that time, he led the team that built what the company described as the world’s first utility computing platform—a precursor to modern cloud computing models.

Sun Microsystems’ early work in distributed systems and cloud-style infrastructure laid groundwork that would later influence enterprise cloud adoption. That background matters as AcquireUp looks to scale a platform used by advisors across geographies, regulatory environments, and business models.

More recently, Parkinson served as Chief Digital Officer and Executive Vice President of Digital Advertising at Valassis, where he oversaw enterprise technology strategy and digital media initiatives. He also held the role of Chief Technology and Information Officer in the credit card processing industry, leading product and engineering for processing and acquiring platforms.

That combination—cloud infrastructure, digital advertising, and payments—points to a leader comfortable with high-volume systems, compliance-heavy environments, and performance-based business models.

In other words: exactly the type of background needed to evolve a marketing platform serving financial advisors.

AI and the Rise of “Agentic” Marketing Infrastructure

AcquireUp has been vocal about incorporating AI across its operations. Parkinson emphasized plans to enhance both employee workflows and customer experiences using AI, including what the company refers to as an “Agentic AI approach.”

Agentic AI—systems capable of executing multi-step tasks autonomously within defined guardrails—is increasingly becoming a buzzword across enterprise tech. In martech and fintech, its appeal lies in automating complex workflows while maintaining auditability and compliance.

For financial advisors, that could mean:

• Smarter segmentation of seminar invite lists

• AI-assisted follow-up sequences tailored to attendee behavior

• Predictive models for seminar-to-client conversion rates

• Performance dashboards that surface anomalies or compliance risks

If executed well, these capabilities could transform seminars from a relationship-first, data-second tactic into a tightly optimized acquisition engine.

And that’s where Parkinson’s platform experience becomes critical. Agentic systems are only as strong as the infrastructure supporting them—data pipelines, security controls, uptime guarantees, and governance frameworks.

The Broader MarTech Context

AcquireUp operates at the intersection of martech and wealth management—a space that’s heating up as advisors face rising acquisition costs and increasing competition from robo-advisors and digital-first firms.

Unlike pure-play digital lead generation companies, AcquireUp blends managed services with proprietary technology. That hybrid model mirrors broader trends in B2B tech, where software-plus-services offerings are becoming common in vertical markets that require regulatory sensitivity and high-touch engagement.

Competitors in financial advisor marketing have invested heavily in digital funnels, social advertising, and automated nurturing campaigns. What differentiates AcquireUp’s approach is its continued focus on in-person educational seminars, combined with a tech backbone designed to quantify and optimize the entire lifecycle.

By strengthening LeadJig’s engineering foundation, AcquireUp appears to be betting that analog trust-building experiences can coexist with digital-grade analytics and automation.

Infrastructure as Competitive Advantage

The announcement also reflects a wider industry shift: marketing companies are increasingly judged not just by creative output but by technical depth.

Financial services firms, in particular, demand:

• Data security and compliance controls

• Transparent performance attribution

• Integration with CRM and portfolio management systems

• Scalable infrastructure for multi-office enterprises

Parkinson’s background in large-scale systems suggests AcquireUp intends to compete less like an agency and more like a SaaS platform provider with managed services layered on top.

That positioning could make the company more attractive to larger RIAs and enterprise advisory networks that require robust IT governance.

What Comes Next

AcquireUp says it will continue investing in its technology and operational infrastructure to support advisors who rely on seminars as a core growth strategy. Parkinson’s mandate spans product, data science, engineering, and IT—effectively giving him control over the entire technical backbone of the business.

If LeadJig evolves into a more intelligent, AI-assisted operating system for seminar marketing, the company could carve out a defensible niche in a fragmented market.

The bigger question is whether advisors—often cautious adopters of emerging tech—will embrace agentic AI tools in a heavily regulated industry. That adoption curve will likely hinge on one factor: measurable, compliant results.

With a veteran cloud and digital infrastructure executive now at the helm of its technology strategy, AcquireUp is clearly preparing for that next phase.

Get in touch with our MarTech Experts.

Social media marketing in 2026 isn’t about being everywhere. It’s about knowing which platform deserves your budget—and your patience.

That’s the headline takeaway from the latest benchmark data released by Emplifi, whose 2026 Social Media Benchmark Report analyzes performance data from tens of thousands of global brands using its CX and social media marketing platform.

The results? A widening performance gap between platforms—and a clear winner.

TikTok Is No Longer Experimental. It’s Essential.

According to Emplifi’s data, TikTok saw median follower counts for brands jump 200% year-over-year in 2025. That’s not incremental growth. That’s acceleration.

Even more striking:

• TikTok delivered a median engagement rate of 27.6% in Q4 2025, the highest across major platforms.

• It generated twice the median interactions of Instagram.

• It produced 20 times the median interactions of Facebook.

For brands still treating TikTok as a secondary test channel, the data suggests they’re leaving engagement—and likely revenue—on the table.

Susan Ganeshan, CMO at Emplifi, put it bluntly: platforms are rewarding different behaviors, and performance is becoming increasingly platform-specific. Translation: one-size-fits-all content strategies are officially obsolete.

Instagram: Still Strong, But Slipping

Instagram remains a core brand-building channel, but engagement momentum has cooled.

Median engagement rates fell from 16.9% in Q1 2024 to 9.7% in Q4 2025—a significant drop in under two years. Follower growth remained steady, but only in the mid-single digits.

However, not all formats are struggling:

• Carousels and Reels generated 44% more engagement than image posts.

• Video content on Instagram produced 30 times more engagement than Facebook video, making it the second-strongest environment for video performance after TikTok.

• Instagram Reels ad spend tripled between Q1 2024 and Q4 2025.

The signal here isn’t that Instagram is fading. It’s that brands must align tightly with format trends. Static image grids won’t cut it anymore.

Facebook: Predictable Reach, Limited Growth

Facebook continues to offer steady, if unremarkable, performance.

• Median engagement rates ranged between 1.4% and 2.5% across 2024–2025.

• Follower growth remained flat.

• Median ad spend per account stayed relatively stable, ranging from $8.5K to $11.2K.

But there’s a twist: format still matters.

Facebook Live videos generated a median of 37.5 interactions per post, outperforming link posts by four times and image posts by six times. Meanwhile, Feed Ads accounted for 70% to 80% of total Facebook ad spend every quarter.

In other words, Facebook may not be the growth engine—but it remains a reliable reach channel, particularly for advertisers seeking scale and consistency.

LinkedIn and X: Niche Gains, Targeted Impact

LinkedIn posted double-digit median follower growth, particularly tied to employer branding, professional positioning, and thought leadership content. For B2B marketers, that’s a strong indicator that strategic investment here still pays dividends.

Meanwhile, on X, lightweight formats ruled. GIFs generated a median of seven interactions per post, reinforcing the platform’s preference for fast, scroll-friendly content.

Neither platform matches TikTok’s explosive engagement rates, but both show that focused use cases can still drive results.

The Ad Spend Reality Check

One of the most telling data points in Emplifi’s report isn’t about engagement—it’s about budget allocation.

• TikTok commanded the highest median ad spend per account, reaching $14.9K in Q4 2025.

• Facebook followed with stable investment levels.

• Instagram posted the lowest overall spend per account at $5.1K in Q4 2025, despite rising investment in Reels.

This suggests marketers are voting with their budgets—and increasingly treating TikTok as a primary performance channel rather than an experimental add-on.

That shift aligns with broader industry sentiment. According to EMARKETER, social media marketers cited “the ability to reach their target audience” as their top challenge last year—ranking above content trends, ROAS calculation, or cross-channel management.

In a fragmented landscape, reach isn’t guaranteed. Platform alignment is.

Diverging Performance Curves

Perhaps the most important takeaway from the report is structural: performance trends across platforms are diverging faster than ever.

TikTok rewards commitment and content-native creativity. Instagram demands format optimization. Facebook offers consistency but limited upside. LinkedIn thrives on professional authority. X prioritizes brevity.

Brands that adapt to those distinctions are outperforming those that recycle the same creative across channels.

Ganeshan summed it up clearly: the brands seeing the biggest gains on TikTok treat it as a core channel, not a side experiment. But Facebook and Instagram remain essential for steady reach—creating a multi-platform balancing act for marketers.

What This Means for 2026 Strategy

For CMOs and performance marketers, Emplifi’s data reinforces three strategic imperatives:

1. Platform-specific optimization is no longer optional. Algorithms are rewarding native behaviors, not cross-posting shortcuts.

2. Video dominance continues. TikTok leads, Instagram follows, Facebook lags.

3. Budget follows engagement. TikTok’s rising ad spend mirrors its performance gains.

The era of treating social media as a monolithic channel is over. The 2026 playbook demands specialization, commitment, and ongoing recalibration.

For brands willing to adapt, the upside is clear. For those still spreading effort evenly across platforms without strategy, the engagement gap will only widen.

Get in touch with our MarTech Experts.

RegTech consolidation is accelerating—and this one lands squarely in the AI fast lane.

CUBE, a global provider of Automated Regulatory Intelligence (ARI) and Regulatory Change Management (RCM), has acquired 4CRisk.ai, a Silicon Valley-based compliance technology firm known for its agentic AI-driven policy mapping platform.

The deal strengthens CUBE’s position in automated regulatory compliance, extending its capabilities beyond tracking regulatory change into fully automated internal policy and control mapping. In practical terms: identifying new regulations is no longer enough. Enterprises now want AI to tell them exactly what to update, where, and why—without weeks of manual review.

From Regulatory Change to Enterprise-Wide Action

CUBE has built its reputation on regulatory intelligence—monitoring global regulatory developments and helping financial institutions stay ahead of change. With the addition of 4CRisk, the company is pushing deeper into enterprise risk automation.

Founded in 2019, 4CRisk developed a purpose-built compliance and risk platform designed to break down corporate policies and procedures and map them directly to regulatory obligations, controls, and risks. The system operates at granular levels, connecting abstract regulatory language to specific governance artifacts inside an organization.

At the core of its platform are proprietary Specialized Language Models (SLMs) trained on authoritative regulatory compliance and risk data sources. Combined with its AI compliance co-pilot, Ask ARIA, the technology reportedly produces results up to 50 times faster than equivalent manual compliance processes.

That speed differential matters. In large financial institutions and multinational enterprises, updating policy frameworks after regulatory change can involve months of cross-functional analysis. Automation at this layer could dramatically compress response times.

Why This Deal Matters in 2026

Compliance is becoming more complex, not less. Regulatory domains are expanding beyond traditional financial oversight into areas like:

• Cybersecurity

• Artificial intelligence governance

• Data privacy

• Labor laws

• Environmental, Social, and Governance (ESG) mandates

4CRisk already provides specialized compliance solutions across those domains. By integrating it into CUBE’s broader RegPlatform, customers can now move from detecting regulatory changes to automatically assessing their downstream impact on policies and controls across the enterprise.

Ben Richmond, Founder and CEO of CUBE, described the acquisition as a “natural extension” of the company’s capabilities—one that enables customers to move from understanding regulatory changes to automating governance mapping at scale.

The strategic shift is clear: regulatory intelligence alone is no longer sufficient. Enterprises want closed-loop automation.

Agentic AI Moves Into RegTech

The acquisition also underscores the growing influence of agentic AI in highly regulated industries.

Agentic systems—AI models capable of executing multi-step tasks autonomously within defined parameters—are moving from experimental pilots into production compliance environments. In this context, agentic AI doesn’t just summarize regulations; it maps them, cross-references internal frameworks, flags control gaps, and suggests remediation paths.

Silicon Valley has been a hotbed for this kind of applied AI infrastructure, and Richmond specifically cited the pace of innovation emerging from the region as a factor in the acquisition.

Venky Yerrapotu, Founder and CEO of 4CRisk, emphasized explainability and trust as central pillars of the platform. That’s critical in compliance settings, where AI outputs must be auditable and defensible under regulatory scrutiny.

In heavily regulated sectors, black-box automation is a non-starter. Explainable AI isn’t a feature—it’s a requirement.

Backed by Private Equity, Built for Scale

CUBE’s expansion is supported by investor Hg, which backed the company in 2024 with a strategic focus on building an end-to-end AI-powered compliance platform.

Joshua Gielessen, investor at Hg, framed the acquisition as a key step in executing that strategy—bringing together regulatory intelligence and purpose-built regulatory AI to create a stronger, unified offering.

CUBE now serves more than 1,000 customers globally across financial services and adjacent industries. Its platform spans every regulated country, positioning it as one of the more comprehensive players in the RegTech space.

Notably, both CUBE and 4CRisk were recently named in the RegTech100 for 2026, signaling peer and industry recognition of their innovation in compliance technology.

Competitive Landscape: The Race to Unified Compliance

The RegTech market has seen increasing consolidation as vendors attempt to offer end-to-end solutions rather than point tools. Enterprises are fatigued by fragmented compliance stacks that require manual integration across:

• Regulatory monitoring systems

• Policy management tools

• Risk and control frameworks

• Audit and reporting platforms

By combining regulatory change intelligence with automated policy mapping, CUBE is moving toward a unified compliance operating system.

Rivals in the space have focused on workflow automation or regulatory content aggregation. What differentiates this deal is the deep integration of AI-driven mapping capabilities—particularly with models trained specifically on regulatory and risk corpora.

If successfully integrated, the combined platform could reduce reliance on consulting-heavy compliance processes, a shift that may resonate strongly with global financial institutions facing mounting regulatory pressure.

The Bigger Picture

AI in compliance is shifting from experimentation to infrastructure. Financial institutions and multinational enterprises are demanding faster turnaround, lower operational risk, and greater transparency in how regulations are interpreted and implemented internally.

This acquisition suggests CUBE sees the future of compliance not as advisory support, but as automated orchestration—where AI continuously aligns external regulatory change with internal governance frameworks.

For compliance leaders navigating increasingly complex regulatory landscapes, the promise is clear: fewer manual reviews, faster impact analysis, and stronger audit trails.

Whether CUBE can fully deliver on that vision will depend on seamless integration and continued AI refinement. But the direction is unmistakable.

RegTech’s next phase isn’t just smarter alerts. It’s autonomous compliance mapping.

Get in touch with our MarTech Experts.

When a reality TV institution hits its 50th season, you expect fireworks. What you don’t always expect is a mobile gaming takeover. That’s exactly what Zynga Inc. is delivering with a sweeping, season-long collaboration tied to the milestone return of Survivor.

In partnership with CBS, the Take-Two-owned publisher is rolling out themed integrations across five of its biggest franchises—transforming passive viewers into active participants. The event aligns with Survivor 50’s February 25 premiere, airing on CBS and streaming via Paramount+.

This isn’t a simple cosmetic reskin. It’s a coordinated cross-title strategy designed to capitalize on live TV momentum, player retention loops, and the growing overlap between entertainment IP and mobile gaming.

A Cross-Game Strategy, Not a One-Off Event

Zynga is activating Survivor-themed experiences across:

• Words With Friends

• Zynga Poker

• Two Dots

• FarmVille 3

• Dragon City

Each title adapts Survivor’s competitive DNA—strategy, elimination, teamwork, endurance—to its core mechanics.

Words With Friends integrates Survivor-themed Word of the Day challenges and custom word searches tied to premiere week. Zynga Poker leans into high-stakes drama with six weeks of Survivor Watch Events, offering limited-edition rewards and a sweepstakes trip to the live finale in Los Angeles. Two Dots introduces time-limited puzzle challenges with collectible rewards.

Meanwhile, FarmVille 3 and Dragon City bring the island competition into simulation territory, with themed events, tribe-inspired activities, and competitive races layered into gameplay loops.

In short: this is not a cameo. It’s a season-long live-ops program engineered to sustain engagement across multiple audiences.

Why This Matters for MarTech

From a marketing technology lens, this partnership is a case study in transmedia engagement.

Instead of running standalone promotional ads for Survivor 50, CBS is embedding the brand directly inside daily-use mobile ecosystems. That shifts the marketing play from awareness to participation. Viewers aren’t just reminded the show exists—they’re reenacting it.

For Zynga, the benefits are equally strategic:

• Cross-title retention: Players hopping between games encounter unified thematic content.

• Live-ops amplification: TV airtime fuels recurring in-game events.

• Data capture: Themed challenges provide behavioral insights tied to event-based engagement spikes.

• Monetization lift: Limited-edition rewards and sweepstakes mechanics encourage higher session frequency.

This mirrors broader industry trends where entertainment IP increasingly functions as a live-service engine. Think Fortnite’s concerts or Call of Duty’s crossover events—but tailored for casual and midcore mobile audiences.

“In the Hands of the Fans”

Survivor 50 itself introduces a viewer-driven mechanic—marketed as “In the Hands of the Fans”—where audience decisions influence gameplay outcomes. By syncing mobile integrations with that participatory theme, Zynga reinforces a consistent brand narrative: fans shape the experience.

It’s a smart alignment. Reality TV thrives on community debate and tribal loyalty. Mobile games thrive on daily engagement and progression loops. Combine them, and you create a feedback cycle between broadcast and gameplay.

Competitive Context

Cross-media integrations are hardly new, but few span five titles simultaneously. For Zynga—now operating as a publishing label under Take-Two Interactive—this signals a mature live-ops infrastructure capable of coordinated deployment at scale.

It also reflects the increasingly blurred lines between gaming and traditional entertainment marketing. As user acquisition costs rise and organic discovery declines, leveraging tentpole IP moments becomes a cost-efficient way to spike attention without starting from zero.

The Bigger Picture

Survivor 50 brings back 24 legendary contestants from across 49 seasons, chasing the franchise’s familiar $1 million prize. But this collaboration suggests the bigger prize may be sustained cross-platform engagement.

If successful, expect more networks to treat mobile games not as peripheral licensing deals, but as integrated marketing channels with measurable ROI.

For players, it’s simple: solve the puzzle, bluff the hand, farm the crops, race the dragon. For marketers, it’s something else entirely—a live demonstration of how broadcast television can still move the needle in a mobile-first world.

Get in touch with our MarTech Experts.

Consumer goods giant Unilever is betting big on AI—and on Google Cloud—to reshape how its brands are discovered, marketed, and sold in a world increasingly shaped by conversational search and autonomous agents.

The companies today announced a five-year strategic partnership aimed at accelerating Unilever’s business transformation through Google Cloud’s AI, data infrastructure, and next-generation marketing tools. At the center of the effort: enterprise-scale AI, agentic workflows, and a complete rethink of how CPG brands win attention in the age of intelligent systems.

For an industry often criticized for slow digital evolution, this is a decisive move.

AI Moves From Experiment to Operating System

Unilever’s portfolio spans global heavyweights like Dove, Vaseline, and Hellmann's. Traditionally, growth for these brands has relied on mass media, retail dominance, and increasingly, e-commerce optimization. But the ground is shifting.

Consumers are no longer just browsing search results—they’re asking AI assistants what to buy. Discovery is becoming conversational. Shopping journeys are becoming agentic. And brand influence is increasingly mediated by algorithms rather than shelf placement.

Unilever’s answer? Build what it calls an “AI-first digital backbone” by migrating its integrated data and cloud platforms onto Google Cloud.

That backbone will power:

• Faster demand generation

• Real-time data-to-insight pipelines

• AI-augmented marketing workflows

• Agentic systems capable of executing multi-step business processes

In short, AI won’t sit on top of operations. It becomes the operating layer.

Vertex AI, Gemini, and the Rise of Agentic Commerce

The technical foundation rests on Google Cloud’s enterprise AI platform, Vertex AI, along with advanced models like Gemini.

Vertex AI gives enterprises the tooling to build, deploy, and scale machine learning and generative AI models. But this partnership goes beyond standard AI deployment. It focuses on enabling what both companies describe as “agentic workflows.”

In practical terms, that means intelligent systems that don’t just analyze data—they take action. These agents could:

• Optimize media spend dynamically

• Adjust pricing or promotions based on predictive demand

• Generate and test creative variations at scale

• Automate supply chain responses to real-time signals

For marketers, this marks a shift from dashboard-driven decision-making to semi-autonomous execution systems.

It also reflects a broader trend across the enterprise software landscape: generative AI is evolving from content co-pilot to decision-making infrastructure.

Marketing in the Age of AI Discovery

Perhaps the most strategically important pillar of the partnership is what the companies call “agentic commerce and marketing intelligence.”

As AI assistants increasingly influence product discovery, brands must ensure they’re visible not just in search results, but in AI-generated answers.

That’s a subtle but massive shift.

Instead of optimizing solely for keywords and ad placements, brands must now consider:

• How AI models interpret product attributes

• How brand data feeds into conversational systems

• How performance is measured in AI-mediated journeys

Measurement itself is changing. Traditional attribution models—already strained in a privacy-first world—face new complexity when AI agents act as intermediaries between consumer intent and purchase.

By combining Unilever’s first-party data and Google Cloud’s AI capabilities, the companies aim to build new models for brand discovery, conversion, and performance tracking in these conversational environments.

For CPG, that’s uncharted territory.

Rebuilding the Enterprise Core

The second major pillar is less flashy but arguably more important: migrating key enterprise applications and data platforms to Google Cloud.

For a company of Unilever’s scale, this isn’t a lift-and-shift IT project. It’s structural surgery.

The move is designed to create a unified data environment capable of:

• Scalable AI deployment across supply chain and marketing

• Faster cross-functional decision-making

• Real-time responsiveness to market shifts

Willem Uijen, Unilever’s chief supply chain and operations officer, framed the shift bluntly: technology has moved “to the core of value creation.”

That language signals something critical. This isn’t about digital optimization at the margins. It’s about embedding AI into every layer of operations—from manufacturing forecasts to campaign activation.

Competitive Context: Why This Matters Now

Unilever’s partnership comes amid a broader wave of enterprise AI alliances. Major CPG and retail players are racing to modernize their stacks as cloud hyperscalers aggressively position themselves as transformation partners rather than infrastructure vendors.

Google Cloud, in particular, has been pushing hard into vertical-specific AI solutions to compete with rivals. Strategic, long-term enterprise deals are key to that effort.

For Unilever, the stakes are equally high. The CPG sector faces:

• Margin pressure from inflation and supply chain volatility

• Fragmented consumer attention across digital channels

• Rising customer acquisition costs

• Intensifying private-label competition

In this environment, speed and intelligence become differentiators.

If AI can shorten the loop between insight and action—even by days—that translates directly into competitive advantage.

From Automation to Autonomy

There’s also a philosophical shift embedded in this deal.

Previous waves of digital transformation centered on automation—making processes faster and cheaper. The current wave aims for autonomy—systems that reason, learn, and act.

Google Cloud’s EMEA President Tara Brady emphasized this transition, describing the deployment of advanced models as building a “system of intelligence” rather than merely modernizing legacy systems.

That distinction matters.

Automation reduces friction. Intelligence changes behavior.

For marketers, that could mean AI systems continuously refining messaging based on live performance data. For supply chain teams, it could mean predictive systems that preempt disruptions before they escalate.

The Big Picture: CPG’s AI Inflection Point

CPG has historically lagged sectors like financial services and technology in advanced data integration. Complex distribution networks and reliance on third-party retailers have slowed unified data strategies.

But as retail media networks expand and direct-to-consumer models mature, CPG brands are regaining access to richer consumer data.

Pair that data with scalable generative AI, and you get something new: intelligent commerce ecosystems.

Unilever’s five-year commitment suggests it sees this as a once-in-a-decade inflection point. By locking in a long-term AI and cloud strategy now, it positions itself ahead of what may soon become table stakes.

What to Watch

Over the next 12 to 24 months, key signals will determine whether this partnership delivers on its promise:

• Are agentic marketing systems deployed at scale—or stuck in pilots?

• Does AI measurably improve media efficiency and ROI?

• Can integrated data platforms meaningfully accelerate decision cycles?

• Do competitors announce similar hyperscaler alliances?

If successful, this deal could serve as a blueprint for how CPG companies adapt to AI-native commerce.

If not, it risks becoming another ambitious transformation story swallowed by enterprise complexity.

For now, one thing is clear: in the AI era, brand equity alone isn’t enough. The companies that win will be those whose infrastructure thinks as fast as their consumers do.

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In a market flooded with AI claims and cloud slogans, Tata Communications is pressing reset on how it wants to be seen.

The global communications technology provider—serving 300 of the Fortune 500—has introduced a new brand identity and positioning: “Together, limitless.” The move marks a strategic milestone in the company’s 24-year evolution and signals a sharpened focus on integration, long-term value, and what it calls leadership in the “intelligent age.”

Brand refreshes are common. But timing is everything—and this one arrives as enterprises are rearchitecting their tech stacks amid escalating complexity.

Why This Rebrand Matters Now

Enterprises today aren’t just adopting new tools; they’re rewiring their operating models. Hybrid work, distributed cloud, cybersecurity pressures, AI deployment, and compliance demands have created what many CIOs describe as “stack sprawl.”

The result? More vendors, more dashboards, more noise.

Tata Communications says its research and customer listening surfaced a consistent tension: organizations don’t need more technology—they need clarity, integration, and trusted orchestration.

That message is at the core of “Together, limitless.” It’s less about flashy innovation and more about unifying platforms, expertise, and partnerships to deliver measurable outcomes.

For a company historically known for global connectivity and network infrastructure, this signals an ambition to move higher up the value chain.

From Connectivity Provider to Integration Partner

Managing Director and CEO A.S. Lakshminarayanan framed the moment as a shift toward becoming a “more integrated, future-ready company.”

The emphasis isn’t just semantic.

Tata Communications has been steadily expanding beyond core network services into cloud, security, collaboration, IoT, and managed services. The brand repositioning aligns these capabilities under a single narrative: simplification in a hyperconnected world.

At the heart of that strategy is its “Digital Fabric”—a platform approach designed to integrate networking, cloud, security, and edge capabilities into a cohesive environment. The pitch is straightforward: help enterprises simplify complexity and accelerate innovation without stitching together a dozen separate vendors.

In today’s environment, that integration story resonates. Enterprises are increasingly fatigued by multi-vendor fragmentation, especially as AI workloads demand tighter interoperability across infrastructure layers.

The Competitive Landscape: Integration Is the New Differentiator

Tata Communications’ repositioning reflects a broader industry shift. Major players across telecom and cloud are repositioning themselves as transformation partners rather than infrastructure providers.

Enterprises are asking harder questions:

• Can this vendor unify my stack?

• Will they reduce operational complexity?

• Can they scale securely across geographies?

• Are they accountable beyond deployment?

In that context, “Together, limitless” is less marketing flourish and more strategic posture. It underscores partnership, co-creation, and shared outcomes—language increasingly central to enterprise buying decisions.

Stephen Meade, EVP — Corporate and B2B at McCann, which developed the new campaign, distilled it neatly: companies don’t need more technology; they need better integration.

That sentiment captures the mood of a market moving from experimentation to consolidation.

A Campaign Built Around Clarity

The brand launch is backed by Tata Communications’ first major television and digital campaign, also created with McCann. The creative concept mirrors today’s tech landscape—crowded, busy, overwhelming—before pivoting to the calm and clarity enabled by thoughtful orchestration.

It’s a subtle but pointed commentary on the state of enterprise IT.

Rather than leading with product features or AI buzzwords, the campaign leans into emotional reassurance: trust, partnership, stability.

That’s notable. As generative AI dominates headlines, some enterprise buyers are prioritizing resilience and integration over novelty.

Strategic Signals Beneath the Surface

Beyond the tagline, the repositioning signals several deeper shifts:

1. Integrated Go-to-Market Alignment
The company is strengthening capabilities across products, sales, marketing, and operations. That internal alignment is critical if the “integration” promise is to hold externally.

2. Value Creation Over Volume
The emphasis on long-term momentum and differentiated competitiveness suggests a focus on higher-value enterprise engagements rather than commoditized connectivity deals.

3. Global Expansion with Local Relevance
Serving 300 Fortune 500 companies gives Tata Communications scale credibility. The new positioning reinforces its ambition to deepen those relationships rather than simply maintain them.

The Bigger Picture: The Intelligent Age and Enterprise Expectations

We’re entering what many analysts call the “intelligent enterprise” era—where AI, automation, and distributed infrastructure converge.

But intelligence without orchestration leads to chaos.

Enterprises now demand:

• Speed without sacrificing security

• Innovation without operational fragility

• Scalability without runaway complexity

Tata Communications’ rebrand is effectively a bet that integration—not invention—will define the next decade of enterprise technology leadership.

It’s a calculated pivot. In crowded markets, clarity can be a competitive advantage.

Will the Message Stick?

Brand positioning alone doesn’t transform a company. Execution does.

The real test will be whether Tata Communications can consistently demonstrate:

• Tangible simplification of complex environments

• Measurable acceleration of digital initiatives

• Deep, trust-based partnerships across global markets

If “Together, limitless” translates into operational excellence and platform cohesion, it could strengthen the company’s standing in an increasingly integration-driven market.

If not, it risks blending into the sea of aspirational enterprise taglines.

For now, the message is clear: in a noisy, hyperconnected world, Tata Communications wants to be the steady orchestrator.

And in today’s enterprise landscape, that might be exactly what customers are looking for.

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Market research is getting its copilots—and now, its architects.

quantilope has rolled out a major update to its AI Research Partner, quinn, completing what it calls a fully integrated, end-to-end AI research workflow. The headline feature: quinn can now create and review comprehensive, methodologically sound research studies from scratch.

For a sector long dominated by manual setup, logic checks, and spreadsheet wrangling, that’s a bold claim.

From DIY to “Do-It-With-AI”

quantilope is positioning this release as more than incremental AI enhancement. The company says it marks a formal transition from traditional DIY research to what it calls “Do-It-With-AI” (DIA)—or, in branded shorthand, “Do-it-with-quinn.”

The shift reflects a broader trend across enterprise software: AI is no longer just summarizing outputs. It’s designing workflows.

With the update, quinn now supports the entire research lifecycle:

• Drafting studies from high-level objectives

• Structuring questionnaires using advanced methodologies

• Automatically validating logic and setup

• Conducting AI-powered analysis

• Generating automated reports

In other words, quinn moves from assistant to orchestrator.

AI as the Research “Nervous System”

At the core of the upgrade is what quantilope describes as advanced end-to-end AI integration. Quinn now maintains persistent context across the research journey—from initial study design through analysis and reporting.

That continuity is crucial.

Many AI tools in research today operate in silos: one for survey drafting, another for analysis, another for visualization. Context gets lost between steps. Errors creep in. Researchers spend time re-explaining objectives.

Quinn’s updated architecture aims to eliminate that fragmentation by acting as the platform’s “nervous system,” carrying intent and logic across stages.

The update also includes:

• Strengthened AI model performance

• Saved chat histories for contextual continuity

• Expanded dashboarding capabilities

• Direct integration within quantilope’s Editor

That Editor integration is particularly significant. Researchers can now convert high-level business objectives into structured questionnaires within minutes—using advanced methods—while quinn automatically reviews configurations to catch logic mistakes before launch.

For teams under tight timelines, that automation could cut hours—or days—of back-and-forth.

Real-Time Refinement Inside the Workflow

Beyond study creation, the update introduces real-time refinement tools.

New “quinn Action Buttons” allow one-click improvements to question phrasing, helping researchers fine-tune clarity and reduce bias. Meanwhile, persistent chat functionality lets users interrogate survey logic or request technical clarifications without leaving the build environment.

That conversational layer reflects a larger UX shift happening in enterprise platforms. Instead of navigating complex menus, users increasingly interact through dialogue—asking systems to explain, adjust, or optimize on demand.

In practical terms, it lowers the barrier to advanced methodologies. Researchers don’t need to manually configure every detail—they can collaborate with the AI to get there faster.

Productivity Gains—With a Human in Control

quantilope is careful to emphasize that quinn is “Human-Led, AI-Powered.” The positioning mirrors broader AI adoption narratives across enterprise software: augmentation over automation.

The company frames quinn as a master architect—handling structural rigor and execution—while researchers provide strategic context, brand nuance, and stakeholder considerations.

That balance matters in research, where methodological integrity and contextual understanding are critical.

According to quantilope’s leadership, the productivity shift is substantial. Instead of spending time on manual configuration and error-checking, researchers can focus on higher-level insight generation and strategic interpretation.

In a market where insights teams are often asked to do more with fewer resources, that productivity narrative is compelling.

Competitive Context: AI Arms Race in Insights

The consumer insights space has seen an AI surge over the past two years. Survey platforms, analytics vendors, and full-stack research solutions are racing to embed generative AI across their offerings.

But many tools still function as bolt-ons—AI summarizing findings after the fact, or suggesting edits without owning the process.

quantilope’s bet is that full lifecycle integration is the differentiator.

If quinn can reliably draft, validate, analyze, and report within one cohesive workflow, it could reduce the need for external scripting, manual QA, and third-party analysis tools.

The real test will be methodological depth. Enterprise research buyers won’t trade rigor for speed. If quinn consistently produces statistically sound studies while maintaining flexibility for customization, it could raise expectations for the category.

What This Means for Research Teams

For insights professionals, the implications are clear:

• Faster time from brief to field

• Fewer manual logic errors

• More iterative experimentation

• Greater focus on strategic storytelling

It also signals a philosophical shift. Research platforms are evolving from execution tools to collaborative intelligence systems.

If AI can shoulder structural complexity, researchers can concentrate on the harder part: asking better questions.

The Bottom Line

With this update, quantilope is aiming to redefine how enterprise research gets done. Quinn’s evolution from support tool to end-to-end workflow engine reflects a broader transformation across B2B tech—where AI is embedded deeply, not sprinkled on top.

The promise is ambitious: compress the research lifecycle without compromising methodological integrity.

If delivered consistently, “Do-It-With-AI” may not just be a slogan—it could become the default operating model for modern insights teams.

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