Orange Business and Tech Mahindra are entering exclusive negotiations to form a non-equity global strategic partnership aimed at accelerating enterprise digital transformation—and reshaping how Orange Business delivers services outside France.

If finalized, the agreement would combine Orange Business’ secure connectivity platforms and global enterprise footprint with Tech Mahindra’s delivery scale and operational agility. The move signals a pragmatic pivot: deepen international reach while streamlining operations through selective outsourcing.

At stake is more than cost optimization. Orange Business is positioning the partnership as a growth lever in its ambition to become what it calls the “undisputed worldwide leader in secure connectivity for enterprises.”

A Go-to-Market Reset for Global Growth

The proposed collaboration centers on a joint go-to-market strategy focused on regional expansion, product innovation, and greater utilization of Orange Business’ existing platforms to deliver AI-powered, secure, and scalable solutions.

Enterprise demand for integrated connectivity, cybersecurity, cloud, and AI services is accelerating—particularly among multinationals seeking standardized services across regions. Yet scaling globally while maintaining margins has proven difficult for telecom-affiliated enterprise units.

By partnering with Tech Mahindra, Orange Business aims to expand faster in international markets while maintaining direct control over certain critical segments, including its French operations and regulated environments.

Notably, Orange Business would continue to ensure compliance with French and European regulations—a crucial point in a region where data sovereignty and labor frameworks are tightly regulated.

Outsourcing as Strategic Lever

A significant component of the proposal involves outsourcing parts of Orange Business’ global customer support, quote-to-bill operations, and post-sales teams outside France to Tech Mahindra.

In telecom and enterprise IT services, quote-to-bill workflows are complex and often slow, spanning proposal generation, pricing, contracting, provisioning, and invoicing. Improving speed and automation in this chain can directly affect customer satisfaction and revenue realization.

For Orange Business, shifting certain operational functions to Tech Mahindra could increase efficiency and scalability, particularly in price-sensitive global markets. For Tech Mahindra, the arrangement strengthens its footprint in managed services and telecom enterprise operations.

The companies emphasized that the partnership is non-equity—suggesting operational integration without ownership entanglements.

Complementary Strengths, Competitive Pressures

The partnership aligns two distinct but complementary capabilities.

Orange Business brings global network infrastructure, secure connectivity platforms, and an established enterprise brand. Tech Mahindra contributes large-scale delivery capabilities, automation expertise, and deep experience in telecom IT transformation.

The enterprise connectivity market is intensely competitive. Global systems integrators and hyperscalers increasingly encroach on telecom operators’ enterprise turf, offering cloud-native networking and AI-driven platforms.

To compete, telecom enterprise arms must move faster, innovate in AI-driven services, and operate with systems-integrator-like efficiency. The proposed partnership reflects that reality.

Aliette Mousnier-Lompré, CEO of Orange Business, described the collaboration as a growth catalyst designed to expand both market reach and operational excellence. Tech Mahindra CEO Mohit Joshi framed it as an opportunity to “shape the future of enterprise connectivity and digital experiences.”

Behind the rhetoric lies a clear strategic calculus: combine infrastructure ownership with scalable service delivery to stay competitive against global IT services giants.

Automation and AI at the Core

Both companies highlighted automation and AI as central pillars.

A comprehensive operational review is planned to identify areas where Tech Mahindra’s know-how can streamline processes and accelerate delivery. The emphasis is on speed, scalability, and customer experience—hallmarks of modern digital transformation mandates.

For enterprises, the promise is integrated connectivity and AI-enabled services delivered with greater agility. For Orange Business, it’s about maintaining relevance as enterprise customers increasingly expect software-defined, API-driven, and AI-orchestrated services.

Regulatory and Workforce Considerations

The project remains subject to consultation with relevant employee representative bodies—a standard but significant step in France and across Europe.

Labor considerations often influence the structure and timing of such partnerships. By retaining critical French operations while outsourcing select global functions, Orange Business appears to be balancing growth ambitions with domestic regulatory and workforce realities.

Strategic Context: Telecom Enterprise Units Under Pressure

Telecom operators’ enterprise divisions face a dual challenge: declining traditional connectivity margins and rising expectations for integrated digital solutions.

Some operators have spun off enterprise units. Others have leaned heavily into partnerships with IT services firms to extend capabilities without massive in-house expansion.

This proposed alliance places Orange Business firmly in the latter camp.

If completed, the partnership could serve as a template for telecom enterprise divisions seeking to scale globally without diluting regulatory control at home.

What Happens Next

The companies are currently in exclusive negotiations, meaning terms are being finalized and due diligence continues. The outcome depends on internal approvals and employee consultation processes.

Should the deal close, the partnership would mark a significant operational evolution for Orange Business—less a traditional telecom enterprise arm, more a hybrid infrastructure-and-delivery ecosystem.

In a market where secure connectivity increasingly converges with AI-driven services, scale and speed matter as much as network reach.

Orange Business is betting that together with Tech Mahindra, it can deliver both.

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Intuit Inc. (Nasdaq: INTU) delivered a strong second quarter for fiscal 2026, reporting revenue growth of 17% year-over-year to $4.7 billion for the period ending January 31, as the company continues expanding its AI-driven financial platform strategy.

CEO Sasan Goodarzi highlighted the company’s focus on what he described as a new category at the intersection of AI and human intelligence, emphasizing “autonomous, done-for-you experiences” across tax, small business, and mid-market enterprise solutions.

Q2 FY26 Financial Highlights

Revenue: $4.651 billion, up 17%
GAAP Operating Income: $855 million, up 44%
Non-GAAP Operating Income: $1.549 billion, up 23%
GAAP Diluted EPS: $2.48, up 49%
Non-GAAP Diluted EPS: $4.15, up 25%

The margin expansion reflects disciplined cost management alongside top-line growth.

CFO Sandeep Aujla said momentum across the company’s “big bets” gives management high confidence in delivering double-digit revenue growth and margin expansion for the full fiscal year.

Segment Performance

Global Business Solutions (GBS)

GBS revenue rose 18% to $3.2 billion.

• Online Ecosystem revenue: $2.5 billion, up 21%

• Excluding Mailchimp, GBS grew 21%

• Online Ecosystem revenue excluding Mailchimp grew 25%

Key drivers:

• QuickBooks Online Accounting revenue increased 24%, fueled by pricing, customer growth, and product mix shift.

• Online Services revenue grew 18%, driven by payroll and money offerings.

• International online revenue rose 9% on a constant currency basis.

Intuit noted that Mailchimp is expected to return to double-digit growth beyond fiscal 2026.

Consumer Segment

Consumer revenue increased 15% to $1.5 billion.

• Credit Karma revenue: $616 million, up 23%

• TurboTax revenue: $581 million, up 12%

• ProTax revenue: $290 million, up 7%

Credit Karma benefited from strong demand in personal loans, credit cards, and auto insurance, while TurboTax growth reflects ongoing digital tax adoption.

Capital Allocation & Balance Sheet

As of January 31, 2026:

• Cash and investments: ~$3.0 billion

• Debt: $6.2 billion

Key actions:

• Entered and subsequently terminated a $5.8 billion revolving credit facility tied to TurboTax early refund offerings.

• Replaced a prior credit agreement with a new $2.2 billion unsecured revolving credit facility maturing in 2031.

• Repurchased $961 million in shares during the quarter.

• $3.5 billion remains under the current repurchase authorization.

• Approved a quarterly dividend of $1.20 per share, payable April 17, 2026 — a 15% increase year-over-year.

Full-Year FY26 Guidance (Reiterated)

Intuit reaffirmed its fiscal 2026 outlook:

• Revenue: $20.997B–$21.186B (12–13% growth)

• GAAP Operating Income: $5.782B–$5.859B (17–19% growth)

• Non-GAAP Operating Income: $8.611B–$8.688B (14–15% growth)

• GAAP EPS: $15.49–$15.69 (13–15% growth)

• Non-GAAP EPS: $22.98–$23.18 (14–15% growth)

Segment Outlook

• Global Business Solutions: 14–15% growth

• Consumer: 8–9% growth

  • TurboTax: ~8%

  • Credit Karma: 10–13%

  • ProTax: 2–3%

Q3 FY26 Guidance

For the quarter ending April 30:

• Revenue growth of approximately 10%

• GAAP EPS: $10.56–$10.62

• Non-GAAP EPS: $12.45–$12.51

Strategic Context

Intuit’s results underscore continued execution across its AI-enabled platform strategy, integrating financial software, consumer finance, marketing automation, and enterprise solutions.

The company is increasingly positioning itself not just as a financial software provider, but as an AI-native financial technology ecosystem spanning consumers, small businesses, and mid-market enterprises.

With double-digit revenue growth, expanding margins, and reiterated guidance, Intuit enters the second half of fiscal 2026 with strong operational momentum.

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Melbourne-based AI agency Enterprise Monkey announced it will transition all internal AI operations, agents, and new product development to Claude, the flagship model developed by Anthropic.

The company said the move follows growing concerns around platform direction and governance in the AI sector, alongside what it described as a technical preference for Claude in agentic AI deployments.

Ethical Concerns and Platform Direction

Enterprise Monkey’s decision comes amid broader industry debate surrounding AI governance, commercialization models, and regulatory pressures.

CEO Aamir Qutub framed the shift as both a values-based and strategic move, stating that companies must take clear positions when governments or corporations push AI toward controversial use cases. He referenced recent geopolitical tensions involving AI providers and regulatory scrutiny impacting model deployment policies.

The announcement also coincides with the rise of the #QuitGPT movement on X, which has reportedly generated significant online engagement and user migration discussions.

Technical Rationale: “Purpose-Built for Agentic AI”

Qutub emphasized that the shift was not solely ideological.

According to the company, Claude offers stronger performance for:

• Autonomous AI agents

• Model Context Protocol (MCP) integrations

• Native tool use

• Structured reasoning workflows

Enterprise Monkey develops AI agents designed to autonomously manage business functions, including CRM, email workflows, media outreach, and content production.

Its proprietary agent, Zee, already operates entirely on Claude infrastructure.

“When your agents are making real business decisions, accuracy is everything,” Qutub said, citing concerns about hallucination rates and reasoning consistency in competing models.

Client Recommendations Remain Platform-Agnostic

Despite the internal transition, Enterprise Monkey clarified that it will continue recommending solutions based on client needs.

The agency stated it will:

• Continue building on OpenAI products where appropriate

• Advocate for Microsoft Copilot 365 in enterprise productivity environments

• Maintain platform independence in consulting engagements

“Our job is to give clients the best advice, full stop,” Qutub said. “We’re not in the business of pushing platforms — we’re in the business of solving problems.”

Book Revision Reflects Strategic Shift

Qutub, author of The CEO Who Mocked AI (Until It Made Him Millions), confirmed he is revising the book’s upcoming edition to reflect the agency’s platform shift. References to ChatGPT will be replaced with Claude, and the narrative will expand to include themes around ethical AI and sovereign alternatives.

Broader Industry Context

The move highlights a growing divide in the AI ecosystem:

• Some companies prioritize ecosystem scale, integrations, and commercial distribution.

• Others emphasize safety positioning, governance stance, and technical specialization in agentic AI.

As AI agencies increasingly build autonomous systems that execute business-critical tasks, model reliability, reasoning transparency, and governance philosophy are becoming strategic differentiators—not just technical specifications.

For Enterprise Monkey, the transition signals where it plans to concentrate its R&D investment and long-term intellectual property development.

Whether similar agencies follow suit may depend less on online movements and more on measurable performance in real-world, revenue-impacting AI systems.

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In the enterprise AI arms race, most organizations aren’t blocked by algorithms—they’re blocked by their own data.

That’s the premise behind a new strategic partnership between BonData, which bills itself as the creator of the first “Smart Harmonization Layer” for complex enterprise ecosystems, and Elad Systems (TASE: ELAD), a long-established digital transformation consultancy.

The companies say the collaboration will help enterprises across North America and EMEA bypass traditional integration bottlenecks by automating the correlation of fragmented data across legacy systems and SaaS applications—ultimately delivering what they describe as a unified “Golden Record” in days rather than months.

It’s a bold promise in a market where data unification projects are notorious for dragging on long after executive enthusiasm fades.

From “Data Debt” to Decision Latency

The partnership is anchored in a concept BonData calls “Data Debt”—the accumulation of fragmented, inconsistent, and poorly reconciled data across ERP systems, CRMs, data warehouses, and cloud apps.

While the term echoes technical debt in software engineering, its business consequence is what BonData labels “Decision Latency.” In plain terms: by the time leadership reconciles conflicting reports from finance, operations, and sales, the market opportunity has already shifted.

This isn’t theoretical. In volatile markets—whether driven by supply chain disruption, regulatory shifts, or AI-fueled competitive pressure—the speed of decision-making increasingly determines winners and losers.

Yet many enterprises still rely on brittle ETL pipelines, manual reconciliation, and spreadsheet-based workarounds to answer basic cross-functional questions.

BonData’s pitch is that harmonization—not just integration—is the missing layer.

IntelliBond and the “Surgical Wedge” Approach

At the center of the partnership is BonData’s IntelliBond engine, which automates the correlation of disparate data entities across systems. Rather than forcing organizations into massive rip-and-replace modernization efforts, the platform uses what the company calls a “Surgical Wedge” approach.

The idea: insert a harmonization layer that sits across existing infrastructure, mapping and reconciling entities without overhauling core systems.

For implementation partner Elad Systems, this changes the delivery model. Instead of spending months in what Brandes describes as the “Data Janitor” phase—cleaning, deduplicating, and reconciling records—teams can focus earlier on higher-value business use cases.

The outcome, according to the companies, is a unified, real-time “Golden Record” that aligns board-level reporting, operational dashboards, and AI models on the same verified source of truth.

In theory, that reduces internal friction as much as it improves analytics accuracy.

Why This Matters in the AI Era

The timing of the partnership is no accident.

As enterprises push to operationalize AI—particularly large language models and predictive analytics—data quality has emerged as the unglamorous bottleneck. “Garbage in, garbage out” remains stubbornly true, even in the age of generative AI.

BonData and Elad are positioning their joint offering as a foundation for what they call “High-Fidelity AI Readiness.” By automating entity correlation and building a contextual semantic map of enterprise data, organizations can move from experimental AI pilots to operational systems that act on harmonized context.

That’s a key distinction. Many AI initiatives stall after proof-of-concept because the underlying data is inconsistent or incomplete. A forecasting model trained on fragmented customer data won’t suddenly become strategic just because it’s powered by an LLM.

In this framing, harmonization becomes not just a data governance initiative, but a prerequisite for enterprise-grade AI.

Business Outcomes Over Buzzwords

The partnership outlines three primary business objectives:

Reduced Decision Latency: Compressing the time between raw data and executive action, particularly in fast-moving markets.

Elimination of “Data Doubt”: Ensuring that finance, operations, and the board are working from the same reconciled metrics rather than competing dashboards.

Accelerated ROI: By shortening integration cycles, clients can see value sooner—an increasingly important factor as IT budgets face scrutiny.

While the language leans aspirational, the underlying value proposition is straightforward: fewer months spent wrangling data, more time extracting insight.

That positioning puts BonData in a competitive landscape that includes data integration platforms, master data management (MDM) vendors, and newer semantic-layer startups. What differentiates it, the company argues, is automation at the entity-correlation level rather than rule-heavy data cleansing or manual mapping.

For Elad Systems, the partnership enhances its ability to deliver transformation programs with faster payback periods—an attractive pitch in an era when digital transformation fatigue is real.

The Broader Market Context

Enterprise ecosystems are more complex than ever. Organizations often operate dozens—or hundreds—of interconnected applications spanning on-premises systems and modern SaaS tools. Each generates its own data schema, definitions, and logic.

Historically, solving this required large-scale data warehouse projects or monolithic ERP consolidation. Today, many CIOs are reluctant to embark on multi-year overhauls with uncertain ROI.

A harmonization layer that overlays existing infrastructure may be more palatable—especially if it can deliver measurable improvements in days.

Still, execution will be critical. Automated entity matching across heterogeneous systems is notoriously difficult, particularly in regulated industries where precision matters.

If BonData’s IntelliBond engine can reliably reduce reconciliation time without introducing new inconsistencies, it could find traction among enterprises eager to accelerate AI adoption without another infrastructure rebuild.

Speed as a Competitive Advantage

Caroline Meidan, CEO of BonData, frames the partnership in stark terms: the most expensive asset in a modern enterprise is a slow, inaccurate decision.

In volatile markets, speed and accuracy are no longer trade-offs. Boards expect both.

By combining BonData’s harmonization technology with Elad Systems’ implementation expertise, the companies are betting that the fastest path to AI maturity isn’t another dashboard—it’s cleaner, unified context beneath it.

If they’re right, the next wave of enterprise AI won’t be defined by bigger models. It will be defined by better-aligned data.

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Digital marketing analytics firm iQuanti has been Certified™ by Great Place To Work for the second time, signaling sustained employee confidence in the company’s leadership and workplace culture.

The certification is based entirely on employee feedback, not executive submissions or external audits. This year’s survey shows that 82% of iQuanti’s US employees believe management trusts them to do their jobs without micromanagement, while 81% say they feel welcomed when they join the company.

In a services-driven industry where talent retention is increasingly tied to flexibility, autonomy, and psychological safety, those numbers matter.

Why This Certification Still Carries Weight

Unlike employer-voted awards or pay-to-play rankings, Great Place To Work certification relies on anonymized employee surveys measuring trust, fairness, camaraderie, and pride. The benchmark is widely recognized across technology, consulting, and enterprise services sectors.

For a mid-sized martech and analytics consultancy like iQuanti, repeat certification suggests cultural consistency—not just a one-year spike in morale.

That distinction is critical in today’s market. Marketing technology firms are navigating tighter budgets, AI-driven transformation, and rising client expectations for performance accountability. Culture can easily erode under delivery pressure. Maintaining employee trust while scaling client impact is not trivial.

Autonomy Over Oversight

The standout data point—82% of employees saying management trusts them without “watching over their shoulders”—reflects a leadership style that leans toward empowerment rather than control.

That’s especially relevant in hybrid and distributed work environments, where micromanagement can quietly undermine productivity. Trust-based models are increasingly becoming a competitive advantage in knowledge industries, particularly in analytics, SEO, paid media, and performance marketing—areas where iQuanti operates.

Arnab Sen, CEO of iQuanti, framed the certification as validation of a long-standing internal philosophy: that growth comes from empowering teams to lead and succeed.

While executive statements often echo similar sentiments across the industry, employee survey data provides a harder proof point. In this case, employees appear to back the narrative.

Onboarding and Belonging in a Competitive Talent Market

The second notable figure—81% of employees saying they felt welcome when joining—speaks to onboarding and inclusion practices.

In martech and analytics, where competition for data scientists, performance marketers, and AI specialists remains fierce, first impressions matter. Early engagement often determines long-term retention.

Ashish Goyal, VP of Human Resources at iQuanti, emphasized that cooperation and pride in shared accomplishments define the company’s internal culture. According to survey responses, employees describe colleagues as approachable and supportive—an environment that encourages psychological safety.

That concept has moved from HR buzzword to business necessity. Research consistently shows that teams with higher psychological safety are more innovative and more likely to surface problems early—both essential in performance-driven marketing engagements.

Context: Culture as a Strategic Lever in Martech

The certification arrives at a time when martech firms are recalibrating. AI integration, automation tools, and predictive analytics are reshaping how agencies deliver value. But technology alone isn’t enough; clients increasingly evaluate partners on stability, expertise continuity, and strategic thinking.

High employee trust and low attrition can translate into:

• More consistent client teams

• Deeper institutional knowledge

• Faster execution cycles

• Stronger long-term partnerships

In contrast, agencies struggling with burnout or turnover often face delivery disruptions.

By securing repeat recognition from Great Place To Work, iQuanti signals to clients and prospective hires that its internal culture is stable during broader industry shifts.

What This Means for the Market

Certifications like this do more than polish employer branding. They influence recruitment pipelines, client perception, and investor confidence.

In a consulting landscape crowded with performance marketing firms, workplace credibility becomes part of the value proposition. Companies that cultivate autonomy and mutual respect may be better positioned to attract senior-level strategists—talent that increasingly has options.

While Great Place To Work certification doesn’t measure revenue growth or client ROI, it does offer insight into the organizational health behind service delivery.

And in martech, the people behind the dashboards matter as much as the dashboards themselves.

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Opera Limited (NASDAQ: OPRA) closed out 2025 with a strong fourth quarter, capping a year that blended ad-driven revenue growth, expanding AI integration, and a sizable capital return plan.

For the full year ended December 31, 2025, Opera reported revenue of $614.8 million, up 28% year over year. Fourth-quarter revenue reached $177.2 million, a 22% increase compared to Q4 2024, exceeding the company’s own guidance.

The headline numbers are solid. But what’s more interesting is how Opera is positioning itself: not just as a browser company, but as an AI orchestration layer—and a fintech player in emerging markets.

Advertising and Query Revenue Drive Growth

Opera’s monetization engine remains firmly rooted in advertising and search partnerships.

• Advertising revenue climbed 25% year over year in Q4 to $114.4 million, representing 65% of total revenue.

• Query revenue rose 16% to $62.3 million, accounting for the remaining 35%.

• Non-search query revenue grew more than 200%, signaling diversification beyond traditional search deals.

E-commerce partnerships were the fastest-growing vertical, reflecting a broader trend: browsers are becoming commerce gateways, not just navigation tools. With intent-rich user data and integrated shopping experiences, Opera is clearly leaning into performance marketing economics rather than pure traffic arbitrage.

Opera’s annualized ARPU reached $2.49 in Q4, up 26% year over year, supported by 284 million average monthly active users (MAUs). Western markets added 2 million MAUs during the quarter, bringing that segment to 60 million users.

For context, while Opera remains smaller than dominant players like Google and Microsoft in the browser market, its strategy isn’t about share dominance. It’s about monetizing high-intent, niche audiences—especially gamers, crypto users, and power users seeking AI features.

Profitability Improves—Despite Higher Share-Based Compensation

Opera paired top-line growth with improved profitability:

• Net income nearly doubled in Q4 to $55.7 million, up 94% year over year.

• Full-year net income rose 34% to $108.3 million.

• Adjusted EBITDA for 2025 reached $142.5 million, up 24% year over year, with a 23% margin.

Diluted EPS for Q4 came in at $0.61, compared to $0.32 a year ago.

One notable swing factor: share-based compensation jumped sharply in 2025 following the granting of approximately 1.9 million RSUs earlier in the year, with front-loaded expense recognition driving a 603% year-over-year increase in Q4 share-based costs. Even so, operating profit remained stable at a 16% margin in the quarter.

Cash generation was strong. Q4 operating cash flow totaled $40.2 million—96% of adjusted EBITDA—while full-year free cash flow from operations reached $97.7 million.

Opera ended the year with $155.5 million in cash and cash equivalents.

$300 Million Share Buyback Signals Confidence

Perhaps the most market-moving announcement: Opera’s board authorized a $300 million share repurchase program over two years.

The size of the buyback exceeds all previous repurchases combined. It complements Opera’s semi-annual dividend program, including a recently paid $0.40 per share dividend.

Importantly, the buyback includes both open-market ADS repurchases and proportional purchases from Opera’s majority shareholder, maintaining the same public free float percentage.

In a market where many mid-cap tech firms are conserving cash amid AI infrastructure spending, Opera is signaling confidence in its operating model and balance sheet strength.

AI as the “Orchestration Layer”

Opera’s broader ambition is becoming clearer: position the browser as an AI command center.

In 2025, the company launched two new browsers—Opera Air and Opera Neon—expanding beyond its flagship Opera One and gaming-focused Opera GX. Each targets distinct user segments, a segmentation strategy reminiscent of how device makers differentiate product lines rather than pursuing a one-size-fits-all approach.

Opera integrated AI features powered by Google’s latest Gemini models across Opera One, Opera GX, and Opera Neon, bringing enhanced capabilities to more than 80 million PC users.

CEO Lin Song described the company’s vision as building the “best orchestration layer” for navigating AI platforms and services. Rather than building foundational models, Opera leverages third-party LLMs and layers its own agentic engine on top, aiming for contextual, privacy-aware AI experiences embedded directly in the browser.

This mirrors a growing industry shift: browsers are evolving into AI-native environments, not just rendering engines. Microsoft has Copilot embedded in Edge; Google is integrating Gemini across Chrome and Workspace. Opera’s differentiation lies in speed of iteration and targeting demanding user cohorts.

MiniPay and Stablecoin Expansion in Emerging Markets

Beyond browsing, Opera is pushing deeper into fintech via its MiniPay wallet.

MiniPay reached 13 million activated wallets and processed 360 million peer-to-peer transactions. During the quarter, Opera expanded USDT and Tether Gold support through its partnership with Tether.

The rollout of “Pay like a local” in Latin America enables real-time payments from stablecoin balances to platforms like Mercado Pago and Brazil’s PIX system, bridging digital assets and everyday commerce.

In emerging markets where currency volatility and limited banking access remain structural challenges, stablecoin-backed wallets offer practical utility—not just speculative use. Opera appears to be leveraging its browser distribution footprint to seed fintech adoption.

2026 Outlook: Slower Growth, Higher Absolute Dollars

For Q1 2026, Opera expects revenue between $169 million and $172 million, representing 18%–21% year-over-year growth. Full-year 2026 guidance calls for revenue between $720 million and $735 million, or 17%–20% growth.

Adjusted EBITDA for 2026 is projected at $167 million to $172 million, maintaining a 23% margin.

While that implies some deceleration from 2025’s 28% growth, it still represents strong double-digit expansion at scale—particularly for a company balancing dividends, buybacks, and AI investments.

The Bigger Picture

Opera’s 2025 results show a company successfully straddling three domains:

1. Performance-driven digital advertising

2. AI-enhanced browsing experiences

3. Stablecoin-enabled fintech in emerging markets

That combination is unusual—and potentially resilient. Advertising remains cyclical, but fintech and AI-driven engagement offer alternative growth levers.

For martech and adtech watchers, Opera’s results reinforce a key insight: distribution is power. A browser with nearly 300 million users is more than a utility—it’s a monetization platform, a commerce gateway, and increasingly, an AI interface.

With strong cash flow and a $300 million buyback underway, Opera is betting that its hybrid browser-AI-fintech model can keep delivering.

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After a year of aggressive AI rollouts, expanding martech stacks, and rising acquisition costs, 2025 delivered a sobering lesson for ecommerce leaders: more tools didn’t guarantee more profit.

According to the newly released Agentic Commerce Shift Report 2026 from Netcore, the brands that pulled ahead weren’t the ones that spent the most. They were the ones that fixed execution.

The report argues that ecommerce is entering a structural reset. Campaign-led growth and channel-first planning are giving way to agentic, always-on systems built around profit accountability and shared AI context.

In short: AI experimentation is over. Operational AI is in.

The 2025 Reality Check: AI Scaled, Conversion Didn’t

Throughout 2025, ecommerce teams layered copilots, recommendation engines, personalization widgets, and predictive models onto already complex stacks. Funnels multiplied. Journeys expanded. Budgets grew.

But conversion didn’t scale proportionally.

Netcore’s report frames the gap clearly: intelligence wasn’t the constraint—execution was.

Fragmented tools, siloed data, and unclear ownership meant that even powerful AI systems operated in isolation. The result? Incremental lift instead of structural improvement.

The brands that improved profit didn’t simply deploy more AI. They reorganized around it.

From Campaigns to Agentic Systems

The central thesis of the report is that ecommerce growth is shifting from episodic campaigns to governed AI agents operating continuously on shared data.

Rather than asking, “Which channel should we push this week?” high-performing teams are asking, “Which profit outcome are we solving for—and which agents own it?”

That shift reframes digital commerce from a marketing calendar to an execution architecture.

Discovery Is the New Battleground

One of the report’s strongest findings challenges conventional CRO thinking: most ecommerce leakage happens before checkout.

Optimization efforts have traditionally focused on cart abandonment, checkout UX, and payment friction. But Netcore’s analysis suggests that discovery—the moment a shopper tries to find what they need—is where intent often dies.

For example, Restaurant Equippers transformed search into a guided, conversational layer capable of understanding intent quickly. Instead of static filters and keyword search, discovery became an adaptive experience. The result: measurable lifts in add-to-cart and conversion, without higher media spend.

The implication is clear: traffic isn’t the bottleneck. Translation of intent is.

Six Structural Shifts Defining 2026

Netcore outlines six execution shifts that separate leaders from laggards heading into 2026.

1. Loss-Making Ecommerce Is a Systems Problem

Retailers like Walmart experimented heavily with AI copilots. But profitability didn’t improve simply by layering features.

It improved when companies collapsed fragmented tools into a small set of governed AI agents with shared data and clearly defined ownership tied to profit outcomes.

AI moved from experimentation to accountability.

2. Discovery Outweighs Checkout Optimization

Brands that invested in intelligent discovery saw stronger ROI than those endlessly refining checkout flows.

Restaurant Equippers demonstrated that guided search and real-time understanding of shopper intent can unlock conversion gains without incremental ad spend—a meaningful insight as CAC continues rising across retail.

3. Markdown AI Turned Margin Drain Into Profit Lever

In perishable and short-shelf-life categories, pricing inefficiencies often stem from manual guesswork.

Retailers such as Morrisons replaced human-led markdown cycles with store-level AI decision loops. Pricing shifted from reactive clearance tactics to governed, predictive margin control.

In an inflation-sensitive market, that distinction matters.

4. Language Became Infrastructure

In high-growth markets, localization moved beyond translation.

Meesho embedded vernacular and voice-first journeys across browsing, payments, and support. The result: higher conversion among first-time and Tier II+ shoppers, alongside lower cost-to-serve.

Language, in this context, isn’t UX polish. It’s operational leverage.

5. Shopping Missions Beat Channel Metrics

Channel-based dashboards often obscure why customers buy.

Netcore’s report argues that organizing around shopping missions—big shop, top-up, urgent purchase—provides better clarity for assortment, pricing, and journey design.

Channels then become execution layers, not strategic anchors.

That shift challenges how many teams still structure reporting and performance incentives.

6. Always-On Journeys Quietly Outperform Campaign Calendars

Campaigns remain visible and measurable. But incremental profit increasingly comes from responding to live intent between campaigns.

Brands such as Fabindia, Crocs, and Andamen leaned into AI-driven triggered journeys that treated every browse, cart drop-off, and interaction as recoverable value.

These always-on journeys out-earned calendar-driven pushes—without increasing messaging volume.

In a world fatigued by promotional noise, responsiveness may outperform frequency.

What “Agentic” Really Means

The term “agentic commerce” risks sounding like the next buzzword. But in practice, the report defines it narrowly:

• AI agents operate on shared context.

• Ownership is clearly tied to measurable profit metrics.

• Execution runs continuously, not episodically.

• Systems are designed around outcomes, not tools.

This model contrasts with 2025’s fragmented deployments, where AI assistants often operated independently across search, CRM, pricing, and merchandising.

The difference isn’t intelligence. It’s orchestration.

Why This Matters for 2026

Ecommerce growth isn’t slowing—but margin pressure is intensifying. Paid acquisition costs remain volatile. Customer journeys are fragmented across marketplaces, social commerce, DTC storefronts, and messaging platforms.

In that environment, scaling spend without fixing execution becomes expensive quickly.

Netcore’s report suggests that competitive advantage will come from:

• Collapsing martech sprawl into governed agent systems

• Shifting from channel reporting to mission-level strategy

• Treating discovery as a revenue function

• Embedding AI accountability into profit metrics

For CXOs and growth leaders, the takeaway is pragmatic rather than philosophical. AI adoption alone won’t differentiate brands in 2026. Execution architecture will.

The ecommerce leaders next year won’t necessarily have more tools. They’ll have fewer—but smarter, shared, and accountable ones.

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In a market where AI-driven discovery increasingly shapes B2B buying decisions, third-party validation carries new weight.

Blackpearl Group’s B2B Rocket has been named a Top 1% Sales Software Product in the 2026 Best Software Awards by G2—a distinction based entirely on verified customer reviews.

For Blackpearl, the recognition does more than add a badge to its website. It reinforces the company’s broader pitch: democratizing access to data and AI for US sales and marketing teams, particularly small and mid-sized businesses (SMEs) competing against larger, better-resourced rivals.

Why This Ranking Matters in 2026

G2’s Best Software Awards are determined using a proprietary algorithm that blends verified user reviews with market presence data. To qualify, products must have received at least 10 approved reviews during the 2025 calendar year, and only reviews from that evaluation window count toward scoring.

In other words, this isn’t a legacy reputation award. It reflects recent, active user sentiment.

That distinction matters more than ever. As buyers increasingly rely on AI search engines and conversational assistants to evaluate software vendors, platforms like G2 often supply the “answer layer” data that informs recommendations. Credible, review-backed performance signals now influence not only human buyers but also AI-generated shortlists.

Godard Abel, co-founder and CEO of G2, underscored this dynamic, noting that products must earn recommendation in what he called the “answer moment”—when AI platforms surface solutions based on trusted data.

In practical terms, strong review performance now feeds discoverability in AI-assisted buying journeys.

From Acquisition to Acceleration

Since joining Blackpearl Group in July 2025, B2B Rocket has been integrated into the company’s Pearl Engine, Blackpearl’s core data and AI orchestration layer.

According to CEO Nick Lissette, the platform has gained traction by helping US sales and marketing teams move from raw data to action more quickly. Rather than acting as a standalone prospecting tool, B2B Rocket operates within a broader AI system designed to identify “next best customers” and prioritize engagement.

That positioning taps into a broader B2B trend: sales teams are under pressure to increase pipeline efficiency without increasing headcount. Generic lead lists and spray-and-pray outreach models are losing ground to precision targeting powered by AI.

The value proposition is straightforward:

• Surface high-intent prospects faster

• Automate prioritization

• Convert insight into workflow-ready action

For SMEs, this shift can narrow the competitive gap with enterprise players that historically had deeper data resources and advanced analytics teams.

The Rise of Review-Driven Visibility

Recognition in G2’s Top 1% is also strategically timed. B2B software discovery is undergoing structural change:

• Buyers are conducting more self-guided research.

• AI assistants are summarizing and recommending vendors.

• Review platforms are becoming primary trust signals.

Being visible in these ecosystems isn’t optional. It’s table stakes.

Awards grounded in verified user reviews carry greater credibility than vendor-submitted case studies or analyst briefings. They also influence algorithmic visibility, shaping how products appear in AI-curated responses.

In that sense, B2B Rocket’s placement isn’t just reputational—it’s distribution leverage.

Democratizing AI for Sales Teams

Blackpearl’s broader mission centers on making AI-driven sales intelligence accessible to smaller organizations. While large enterprises have long invested in advanced CRM customization and predictive analytics, SMEs often struggle with fragmented tools and limited internal data science expertise.

By embedding B2B Rocket into its Pearl Engine, Blackpearl aims to:

• Reduce tool sprawl

• Centralize customer intelligence

• Tie AI recommendations directly to measurable sales outcomes

This aligns with a wider industry shift away from standalone AI “assistants” toward orchestrated systems with shared context and outcome accountability.

The question heading into 2026 isn’t whether AI belongs in sales workflows. It’s whether it can produce measurable revenue impact at scale.

G2’s Top 1% recognition suggests customers believe B2B Rocket is delivering on that promise.

What It Signals for the Market

For competitors in the sales tech and revenue intelligence space, the message is clear: verified customer performance is becoming a competitive moat.

In the AI-driven answer economy, visibility must be earned with proof—not positioning.

For Blackpearl Group, the award strengthens its credibility in the US market and supports its strategy of pairing AI orchestration with actionable sales execution.

As buying journeys grow more autonomous and AI-mediated, products that win both customer trust and algorithmic validation will likely pull ahead.

B2B Rocket’s Top 1% ranking positions it squarely in that conversation.

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