Stensul, the campaign creation platform used by enterprise marketing teams, is starting 2026 with more than product momentum. The company has been named one of Built In’s Best Places to Work in 2026, earning a spot among the top midsize employers in New York City and ranking within Built In’s top 100 companies overall.

The recognition places Stensul alongside organizations setting the pace for how modern tech companies attract, retain, and support talent—at a time when both marketing and work itself are being reshaped by AI.

Culture as a Competitive Advantage

Built In’s annual Best Places to Work awards spotlight companies whose compensation, benefits, and workplace programs stand out in an increasingly competitive labor market. Now in its eighth year, the program uses a data-driven methodology that evaluates employers on pay transparency, benefits offerings, and company-wide cultural initiatives.

For Stensul, the honor reinforces a strategy that treats culture as more than an HR initiative. Rachel Meranus, Stensul’s Chief Revenue and Marketing Officer, described the recognition as validation of a people-first philosophy that directly supports innovation and customer impact.

As marketing teams face mounting pressure to move faster and do more with fewer resources, Stensul’s internal culture mirrors the value proposition of its platform: empower teams, remove friction, and let creativity scale.

Standing Out in an AI-First Job Market

The award also reflects a broader shift in how companies are evaluated by talent. With AI-driven search and discovery increasingly influencing how candidates research employers, workplace recognition now carries added weight beyond traditional employer branding.

Maria Christopoulos Katris, Founder and CEO of Built In, emphasized that Best Places to Work recognition has become a signal not just to people, but to algorithms. In an era where AI tools help candidates compare roles, culture, and growth opportunities, awards like these shape how a company’s story is interpreted and surfaced.

For tech and MarTech companies competing for specialized talent, that visibility matters. Culture, benefits, and values are no longer soft differentiators—they are discoverable signals that influence hiring outcomes.

Momentum Heading Into 2026

Stensul enters 2026 with growing relevance in enterprise marketing, where speed-to-market and operational efficiency are top priorities. Being recognized as a Best Place to Work strengthens the company’s position as it scales—helping attract the kind of talent needed to support innovation without sacrificing cohesion.

The recognition also underscores a trend across B2B tech: high-performing platforms increasingly depend on high-trust cultures. As automation and AI absorb more routine work, companies that invest in people, autonomy, and clarity are better positioned to compete.

For Stensul, the Built In award isn’t just a badge—it’s a signal that the company’s internal operating model is keeping pace with the future of work it helps marketers navigate every day.

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Awards season has always been fueled by speculation—who will win, who will be snubbed, and which moments will dominate the conversation the next day. Now, that speculation is getting quantified. Polymarket, the world’s largest prediction market, has announced a first-of-its-kind partnership with the Golden Globes®, becoming the awards show’s exclusive prediction market partner.

The collaboration introduces real-time, market-driven insights into one of entertainment’s most closely watched events, blending cultural debate with live probability data across Golden Globes events, digital platforms, and editorial coverage.

Turning Awards Buzz Into Live Market Signals

At its core, the partnership formalizes something fans already do instinctively: predict outcomes. Polymarket translates those collective expectations into live markets, offering a constantly updating snapshot of what audiences believe will happen—before and during the show.

As part of the deal, Polymarket branding and live market insights will be integrated into the official 2026 Golden Globes viewing party, giving attendees and viewers a new layer of context as categories unfold. Rather than relying solely on pundits or social chatter, fans can track real-time probabilities shaped by thousands of participants.

Shayne Coplan, Founder and CEO of Polymarket, positioned the partnership as a natural extension of awards culture. The Golden Globes, he noted, have always been about anticipation and debate. Prediction markets simply formalize that energy, turning opinions into data.

A New Engagement Model for Live Entertainment

For the Golden Globes and its owner, Penske Media Corporation, the move reflects a broader push to deepen fan engagement across entertainment, fashion, and pop culture. Craig Perreault, President of Penske Media Corporation, described the partnership as opening a “new frontier” in how audiences interact with content—one that strengthens emotional investment in films, shows, and creators.

This approach mirrors trends already playing out in sports and finance, where live data overlays and real-time analytics have reshaped how audiences experience events. Applying that model to awards shows signals an evolution in entertainment media, where passive viewing gives way to participatory experiences.

Why This Matters Beyond Awards Season

The partnership also highlights the growing mainstream appeal of prediction markets. Once considered niche tools for political forecasting or crypto-native audiences, platforms like Polymarket are increasingly intersecting with culture, media, and marketing.

For brands and publishers, real-time prediction data offers a new way to capture attention during live events—especially as second-screen behavior becomes the norm. Instead of scrolling aimlessly during commercial breaks, viewers can engage with live probabilities that evolve moment by moment.

It’s also a sign of how entertainment properties are experimenting with interactive formats to stay relevant in an on-demand world. Live events remain one of the few moments that reliably command collective attention, and adding data-driven interactivity raises the stakes for keeping audiences locked in.

What to Expect at the 2026 Golden Globes

The 83rd Annual Golden Globes will air on Sunday, January 11, 2026, broadcast live on the CBS Television Network and streaming on Paramount+ in the U.S. Polymarket insights will appear not only at live events but across the broader Golden Globes digital and editorial ecosystem.

As “Hollywood’s Party of the Year®” continues to expand its scope—now celebrating film, television, and podcasting—the addition of prediction markets signals a shift toward more dynamic, participatory storytelling.

For viewers, it means awards night won’t just be about watching winners announced. It will be about seeing expectations rise and fall in real time—turning collective opinion into a live narrative that unfolds alongside the show itself.

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Brick Marketing is doubling down on something it’s been doing longer than most agencies have existed: teaching marketers how to do the work themselves.

The Boston-based digital marketing firm has announced an expanded suite of marketing education resources, aimed at helping businesses, in-house teams, and individual professionals sharpen their skills and improve real-world performance. The update builds on more than two decades of education-led consulting, now tailored to a market reshaped by generative AI, evolving search behavior, and rising expectations for measurable ROI.

At a time when “AI-powered marketing” often means black-box tools and vague promises, Brick Marketing is taking a more grounded approach—focusing on practical instruction, clear frameworks, and hands-on learning that teams can apply immediately.

Education Built for How Marketing Actually Works

Brick Marketing’s expanded offering combines full-day training classes, free educational webinars, and its long-running Wear Em Down Marketing strategy eBook. Together, the resources are designed to support organizations at different stages of growth—from companies building their first SEO foundation to mature teams adapting to Generative Engine Optimization (GEO) and AI-influenced discovery.

What stands out is the emphasis on applicability. Rather than abstract theory, the programs are structured around real business needs: how search visibility is earned today, how content authority compounds over time, and how AI is changing—not replacing—the fundamentals of marketing strategy.

This focus reflects a broader industry shift. As search engines increasingly blend traditional rankings with AI-generated answers, marketers are being pushed to rethink how visibility is achieved. Education, not just tooling, is becoming a competitive advantage.

Full-Day SEO, GEO, and Content Marketing Training

At the center of the expansion are full-day training classes covering SEO, Generative Engine Optimization, and content marketing. These sessions are available both in person at Brick Marketing’s Boston office and virtually across the U.S., making them accessible to distributed teams and remote-first organizations.

The curriculum blends foundational concepts with advanced execution, using live walkthroughs, real examples, and clear explanations of how each tactic contributes to long-term brand authority and discoverability. Participants are guided through frameworks they can implement immediately—without needing enterprise-level tools or outside agencies to get started.

Class sizes are intentionally limited, a notable contrast to large-scale online courses that prioritize reach over engagement. Attendees are encouraged to ask questions, interact with examples, and work through real scenarios that mirror their own marketing challenges.

Registration and scheduling for the SEO, GEO, and content marketing classes are now open.

Preparing Marketers for the Generative Search Shift

Brick Marketing’s inclusion of Generative Engine Optimization reflects how quickly the search landscape is changing. As platforms like Google integrate AI-generated summaries and conversational results, marketers can no longer rely solely on traditional ranking signals.

Instead, visibility increasingly depends on content clarity, topical authority, and how well information can be interpreted and surfaced by generative systems. Brick Marketing’s training addresses this shift directly, helping teams understand how SEO and GEO work together rather than treating AI as a separate channel.

This educational angle positions Brick Marketing alongside a growing group of consultancies focused on knowledge transfer, not dependency. For organizations wary of outsourcing strategy they don’t fully understand, that approach offers both transparency and resilience.

A Long-Term View on Marketing Capability

While many agencies emphasize short-term performance gains, Brick Marketing’s education-first model is rooted in long-term capability building. The idea is simple: companies that understand why something works are better equipped to adapt when platforms change—as they inevitably do.

That philosophy feels particularly relevant in 2026, as marketers navigate AI disruption, tighter budgets, and increased pressure to prove impact. Training teams to think critically about SEO, content, and generative visibility may not be flashy—but it’s increasingly essential.

For businesses looking to future-proof their marketing skills rather than chase the next tool, Brick Marketing’s expanded education suite is a reminder that fundamentals, when taught well, still scale.

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Retail marketing has a data problem. Campaigns live in one system, transactions in another, loyalty data in a third—leaving retailers with fragmented insights and fuzzy ROI. At NRF 2026: Retail’s BIG Show, Celerant Technology is positioning itself as the antidote.

The retail commerce platform provider will showcase an expanded Customer Engagement Suite (CES), a tightly integrated marketing hub designed to let retailers manage campaigns, messaging, and automation from a single interface—powered directly by real in-store and online sales data.

The pitch is straightforward but increasingly urgent: stop stitching together marketing tools, and start running engagement from the same system that runs the business.

Marketing Meets the Core Retail Engine

Unlike standalone marketing platforms that rely on APIs and periodic data syncing, Celerant’s Customer Engagement Suite is built directly into its core commerce platform. That means email, SMS, live chat, social media management, online reviews, and customer communications all draw from a single, centralized retail database.

Every campaign is tied to actual transactions—point of sale, eCommerce, mobile apps, loyalty programs, and customer profiles—without manual exports or reconciliation.

“Retailers don’t need more marketing tools; they need one connected system,” said Michele Salerno, Celerant’s Chief Growth Officer. “CES becomes the central command center for marketing, powered by the same data that runs the business both in-store and online.”

It’s a clear response to a broader industry shift: retailers are under pressure to personalize outreach, prove ROI, and coordinate experiences across physical and digital channels—all while dealing with tighter budgets and leaner teams.

From Behavior-Based Campaigns to Built-In Automation

At the heart of CES is behavior-driven marketing. Retailers can build campaigns based on what customers actually do—what they buy, browse, return, or ignore—rather than relying on static lists or third-party data pipelines.

Because CES is natively connected to the product catalog, marketers can pull SKUs, images, and inventory data directly into campaigns, reducing friction between merchandising and marketing teams. Automation rules ensure messages are timely and relevant, triggered by real shopping activity instead of guesswork.

The benefit isn’t just personalization—it’s operational simplicity. By collapsing marketing and commerce into one system, retailers gain cleaner data, faster execution, and fewer points of failure as campaigns scale.

Closing the Online-to-Offline Gap With Geo-Fencing

Celerant is also using NRF to spotlight its geo-fencing capabilities, which push CES beyond digital channels and into physical retail environments.

Through branded mobile apps, retailers can trigger automated push notifications when customers enter predefined geographic zones near stores or key locations. The goal: influence purchasing decisions at the exact moment customers are close enough to act.

Once configured, these proximity-based campaigns run continuously, driving foot traffic and incremental sales without daily intervention from store teams. In an era where physical retail must justify every visit, location-aware engagement is becoming less of a novelty and more of a necessity.

Why This Matters Now

Celerant’s expanded CES lands at a time when retailers are rethinking their MarTech stacks. Best-of-breed tools once promised flexibility, but often delivered data silos, rising costs, and limited visibility into performance.

The alternative—unified commerce platforms with embedded marketing—is gaining traction. By tying engagement directly to transactions, platforms like Celerant aim to give retailers something many still lack: a single source of truth for customer behavior and campaign impact.

Competitors across the retail tech landscape are moving in a similar direction, but Celerant is leaning heavily on its single-database architecture as a differentiator—especially for mid-market and specialty retailers that don’t want enterprise-level complexity.

See It in Action at NRF 2026

Celerant will demo the Customer Engagement Suite at booth #4232, positioning it as a core pillar of its broader all-in-one retail platform. That platform already spans point of sale, inventory, eCommerce, mobile apps, fulfillment—and now centralized marketing.

For NRF attendees, the message is clear: the future of retail marketing isn’t another dashboard. It’s fewer systems, tighter data, and marketing that’s inseparable from how—and where—customers actually buy.

Get in touch with our MarTech Experts.

Artificial intelligence is rapidly becoming the new interface between sports organizations and their fans—and the ATP is leaning into that shift. Infosys has announced the launch of Ally, an AI-powered chatbot designed to deepen fan engagement across men’s professional tennis, while also extending its long-standing partnership with the ATP through 2028.

The move underscores how digital platforms, data, and generative AI are reshaping the sports experience, turning passive spectators into active participants. For Infosys, it also reinforces a strategy it has been executing for over a decade: using enterprise-grade AI and analytics to power fan-facing experiences at global sporting institutions.

Ally: A New Front Door to the ATP’s Data Universe

Ally is built on Infosys Topaz, the company’s AI-first offering that leverages generative AI technologies. At its core, the chatbot acts as a conversational gateway to the ATP’s vast data ecosystem, providing real-time answers to questions around match statistics, tournament draws, schedules, head-to-head records, and historical performance.

Unlike basic sports chatbots that recycle surface-level facts, Ally is tightly integrated with the ATP Stats Centre, ensuring responses are grounded in verified, up-to-date data. Fans, journalists, players, and coaches can explore player comparisons, track performance trends, and surface context-rich insights through natural language conversations.

The experience is designed to be concise and intuitive, lowering the barrier for casual fans while still delivering depth for analysts and professionals who rely on accurate, granular data.

Built for Trust, Safety, and Scale

One of the growing concerns around generative AI—particularly in live sports contexts—is accuracy. Infosys and the ATP are positioning Ally as a controlled, enterprise-grade deployment rather than an open-ended experiment.

The chatbot includes multiple guardrails, such as content filtering, removal of personally identifiable information (PII), and contextual validation to detect and prevent misinformation or AI hallucinations. Continuous learning mechanisms allow Ally to refine its responses over time, adapting as new data, players, and tournaments enter the ecosystem.

This focus on governance reflects a broader trend in sports and media organizations: AI is no longer just about novelty, but about reliability, compliance, and brand trust.

A Decade-Long Digital Partnership Deepens

The Ally launch builds on a partnership that began in 2015, when Infosys became the ATP’s Digital Innovation Partner. Over the past ten years, Infosys has played a central role in shaping the ATP’s digital infrastructure, including the ATP app, ATP PlayerZone, and the ATP Stats Centre itself.

Extending the relationship through 2028 signals that the ATP sees AI and data as foundational—not experimental—to its future. As fan expectations evolve toward personalization, interactivity, and real-time insights, governing bodies are under pressure to modernize how they package and distribute information.

For Infosys, the ATP partnership also serves as a high-visibility showcase for its AI capabilities in a real-world, global environment—an approach the company has mirrored across other sports and entertainment properties.

Beyond Fan Engagement: Sustainability and Carbon Tracking

AI isn’t the only focus of the Infosys–ATP collaboration. In 2025, the partners rolled out Carbon Tracker 2.0, an updated version of the sustainability initiative launched in 2023 to help players measure and reduce travel-related emissions.

More than 300 players have used the tool, collectively tracking 2.3 million kilometers of travel and offsetting 585 tonnes of carbon in 2025 alone. The initiative reflects how data platforms originally built for performance analysis are increasingly being repurposed to address sustainability and operational efficiency—another growing priority across global sports organizations.

Why This Matters for Sports, Media, and MarTech

The launch of Ally highlights a larger shift at the intersection of sports, MarTech, and AI. Fans no longer want static stats pages or one-way broadcasts; they expect interactive, personalized experiences that mirror how they engage with digital products elsewhere.

By embedding AI directly into its data infrastructure, the ATP is effectively turning its statistics engine into a conversational product. That approach has implications beyond tennis, offering a blueprint for leagues, federations, and media companies looking to unlock more value from their data while strengthening fan loyalty.

It also reflects how enterprise AI platforms like Infosys Topaz are moving beyond back-office optimization into highly visible, consumer-facing roles—where accuracy, trust, and experience design are just as critical as technical performance.

Looking Ahead

As AI becomes more deeply woven into live sports, the differentiator won’t be whether organizations adopt it, but how responsibly and creatively they deploy it. With Ally, Infosys and the ATP are betting that conversational AI—backed by verified data and strong governance—can make tennis more accessible without sacrificing credibility.

For fans, it promises a more interactive way to follow the sport. For the industry, it’s another sign that AI-powered engagement is quickly becoming table stakes.

Get in touch with our MarTech Experts.

Shopify is making a decisive move to ensure commerce doesn’t just survive the AI era—it defines it. The company has announced a sweeping expansion of native commerce across major AI platforms, anchored by a new open standard co-developed with Google called the Universal Commerce Protocol (UCP). The initiative positions Shopify as the infrastructure layer for what it calls agentic commerce, where AI agents don’t just recommend products but actively complete purchases on a shopper’s behalf.

This is more than another integration update. It’s Shopify signaling that AI assistants, chat interfaces, and conversational agents are becoming the next dominant storefront—and that it intends to power them all.

Commerce Moves Into the Conversation

AI chats have rapidly become a discovery engine. Millions of users now ask AI tools what to buy, compare products, and plan purchases without ever visiting a traditional ecommerce site. Shopify’s bet is that commerce needs to meet customers inside those conversations, natively and seamlessly.

With UCP, Shopify merchants will soon be able to sell directly within AI Mode in Google Search, the Gemini app, Microsoft Copilot, and ChatGPT, all managed centrally from the Shopify Admin through what Shopify calls Agentic Storefronts. Embedded checkout experiences remove the friction of redirects, logins, and siloed carts—allowing transactions to happen where intent peaks.

This marks a shift from “AI as a referral channel” to “AI as the transaction layer.”

Universal Commerce Protocol: A Shared Language for AI Shopping

At the center of Shopify’s announcement is UCP, an open standard designed to let AI agents connect to and transact with merchants at scale. Co-developed with Google and already endorsed by more than 20 retailers and platforms, UCP aims to solve a growing fragmentation problem: every AI platform currently handles commerce differently, if at all.

Shopify is proposing a shared language that allows agents to understand and execute real-world checkout complexity. That includes applying discount codes, honoring loyalty programs, handling subscriptions, confirming pre-orders, and respecting selling terms like final-sale policies—all within a conversational interface.

Importantly, UCP is payment-agnostic. While it works seamlessly with Shopify Payments, it also supports any payment processor. Under the hood, it’s designed to adapt to diverse commerce stacks using REST APIs, Model Context Protocol (MCP), Agent Payments Protocol (AP2), and Agent-to-Agent (A2A) standards.

For merchants with more nuanced requirements—such as furniture retailers that need delivery date selection—UCP provides a structured way to prompt agents for missing customer inputs without breaking the flow.

In short, Shopify is trying to make agentic checkout as flexible as the real world of commerce itself.

Google AI Mode and Gemini: The First Major Showcase

UCP will power a new generation of native shopping experiences inside Google’s AI products. Shopify merchants will soon be able to sell directly within AI Mode in Google Search and the Gemini app, using embedded checkouts controlled from Shopify Admin.

Google is also introducing a Direct Offers pilot, allowing select Shopify merchants to surface exclusive deals inside AI conversations. The idea is simple: when a shopper expresses high purchase intent in an AI interaction, merchants can respond with the right incentive at exactly the right moment—without leaving the chat.

For Google, this helps close the loop between discovery and transaction. For Shopify, it extends merchant reach into one of the largest AI-driven discovery surfaces on the planet.

Microsoft Copilot Checkout Expands the AI Commerce Map

Shopify is also expanding its partnership with Microsoft through a new Copilot Checkout experience. Users can now shop directly inside Copilot, completing purchases without jumping across tabs or platforms.

As Microsoft positions Copilot as a productivity and planning assistant, commerce becomes a natural extension—especially when users are researching products, organizing events, or managing personal tasks. Shopify merchants stay in control of inventory, offers, and fulfillment, while Copilot becomes another high-intent sales channel.

Brands including Keen, Pura Vida, and Kyte Baby are already using Copilot Checkout, while Monos, Gymshark, and Everlane are preparing to sell directly through Google’s AI experiences.

Shopify Catalog Opens to Every Brand

One of the most strategic shifts in this announcement is Shopify opening its Catalog to brands that don’t run their online stores on Shopify at all.

Through a new Agentic plan, any brand—regardless of ecommerce platform—can list products in the Shopify Catalog, a massive, continuously updated dataset of billions of products. Shopify uses specialized large language models to categorize, enrich, and standardize product data so AI agents can surface the right product instantly, even from ambiguous or conversational prompts.

Once listed, that data flows through Agentic Storefronts and appears across AI channels like ChatGPT, Google AI Mode, Gemini, Microsoft Copilot, the Shop app, and future Shopify partners. Merchants set up their data once; Shopify handles the distribution everywhere discovery happens.

This effectively turns Shopify into a neutral commerce backbone for the AI web—similar to how payment networks operate behind the scenes of traditional commerce.

Why Agentic Commerce Matters Now

Shopify’s push reflects a broader industry reality: AI assistants are evolving from information tools into action-taking agents. They don’t just answer questions—they plan, recommend, and execute.

In that environment, commerce can’t rely on static product pages and search ads alone. It needs to be programmable, conversational, and interoperable across platforms. Shopify’s decades of experience handling checkout edge cases, tax rules, inventory quirks, and global payments gives it a credibility advantage as standards emerge.

Rivals like Amazon, Google, and emerging AI-native commerce startups are all exploring similar territory. But Shopify’s approach—open standards, cross-platform reach, and support for non-Shopify merchants—suggests it’s aiming to be the Switzerland of agentic commerce rather than a closed ecosystem.

From Tools to Infrastructure

Shopify executives describe this moment as a platform shift on par with mobile or cloud computing. Where previous waves focused on storefronts and apps, this one centers on interfaces without screens—conversations that can transact.

By investing in protocols instead of point solutions, Shopify is betting that no single AI assistant will dominate. Instead, commerce will flow across many agents, surfaces, and contexts. UCP is designed to make sure merchants don’t have to rebuild their business logic for each one.

As AI-driven discovery accelerates, Shopify’s role is becoming less about where a store lives and more about how commerce works everywhere.

The Bigger Picture

If successful, Shopify’s strategy could reshape how brands think about customer acquisition, SEO, and paid media. When AI agents handle discovery and checkout in one flow, the line between marketing and transaction blurs. Offers, pricing, loyalty, and product data become just as important as creative messaging.

For merchants, the promise is reach without fragmentation. For shoppers, it’s fewer clicks and more relevance. And for the industry, it’s a signal that agentic commerce is moving from theory to infrastructure.

Shopify isn’t just adapting to AI-driven commerce—it’s trying to define the rules.

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As artificial intelligence reshapes how organizations plan, operate, and measure impact, nonprofits are at risk of being left behind—not for lack of vision, but for lack of resources. Deloitte is betting it can help close that gap.

The consulting giant this week unveiled Dot Good, a new suite of services designed specifically to help nonprofits apply AI and other advanced technologies in practical, mission-aligned ways. The offering blends Deloitte’s social impact expertise with its growing AI, data, and technology muscle, packaged at discounted rates to make it more accessible to cash-constrained organizations.

The launch reflects a broader reality in the nonprofit sector: leaders see the promise of AI, but many simply can’t afford to experiment, scale, or hire the talent needed to do it responsibly.

AI ambition meets nonprofit reality

Dot Good didn’t emerge from a whiteboard exercise. Deloitte says it interviewed 50 nonprofit leaders while shaping the program, and a clear pattern emerged. Most leaders believe AI could significantly improve strategic decision-making, operational efficiency, and program impact. But they also acknowledged that limited budgets, talent shortages, and competing priorities make adoption difficult.

That tension—high expectations, low capacity—has become a defining theme across the nonprofit world. While large enterprises race ahead with generative AI pilots and agent-based automation, many nonprofits are still wrestling with legacy systems, manual processes, and data silos.

Dot Good is positioned as a response to that imbalance.

What Dot Good actually offers

Rather than a single product or platform, Dot Good is structured as a customized service model that can support nonprofits at different stages of their technology journey. Deloitte says engagements can include:

• AI and technology strategy, helping organizations identify where advanced tools can realistically support their mission

• Tech-focused human capital services, including workforce planning and change management

• System customization and implementation, translating strategy into usable systems rather than abstract roadmaps

The key differentiator, Deloitte argues, is flexibility. Nonprofits can engage at an early exploratory stage or move directly into implementation, depending on readiness and resources.

Just as important, Deloitte is offering these services at discounted rates for the nonprofit market—an acknowledgment that traditional consulting price tags often put firms like Deloitte out of reach for social sector organizations.

Beyond consulting: pro bono AI education

To complement paid engagements, Deloitte is also rolling out a pro bono AI learning series for nonprofit professionals. The program is designed to meet organizations where they are, whether they’re just beginning to understand AI or preparing for more strategic deployments.

The idea is to raise baseline AI literacy across the sector, not just deliver one-off projects.

Dana O’Donovan, US Purpose leader at Deloitte Services LP, framed the initiative as a response to rapid technological change. “Technology is rapidly evolving, leaving many resource-constrained nonprofits struggling to keep up in today’s tech-driven world,” she said. Dot Good, she added, is meant to help nonprofits use advanced technologies to transform their organizations while staying focused on their core missions.

A people-first framing for AI

Deloitte is careful to position Dot Good as people-first, not automation-for-automation’s-sake. Nina Gonzalez, a principal at Deloitte Consulting LLP, emphasized that AI’s value lies in how it supports human decision-making and mission delivery.

By combining AI-driven insights, human capital solutions, and implementation support, Gonzalez said Dot Good aims to improve operational value, unlock innovation, and enable transformative change—without pulling nonprofits away from their purpose.

That framing aligns with a growing trend in the AI market. As skepticism rises around hype-heavy AI claims, organizations are increasingly focused on practical, ethical, and trust-based adoption, especially in sensitive sectors like healthcare, education, and social services.

How this fits into Deloitte’s AI push

Dot Good also serves as another showcase for Deloitte’s expanding AI ecosystem. Over the past decade, the firm has invested heavily in AI capabilities, including:

• Its Generative AI practice

• Zora AI™, an agentic platform offering ready-to-deploy digital workers

• The Deloitte Ascend™ delivery platform, used to build and deploy AI solutions and agents

• Its Trustworthy AI™ framework, designed to manage sector-specific risks and governance concerns

• The Deloitte AI Academy™, which focuses on AI fluency and workforce training

While Dot Good isn’t about selling Zora AI or prebuilt agents directly, it benefits from the same underlying infrastructure and governance frameworks—an important consideration for nonprofits that must balance innovation with accountability and public trust.

Why this matters for the MarTech and AI ecosystem

At first glance, Dot Good may seem far removed from mainstream MarTech. But the implications ripple outward.

Nonprofits are increasingly digital-first organizations, relying on data, marketing automation, CRM systems, and analytics to fundraise, engage donors, and measure outcomes. AI-powered insights can influence everything from campaign targeting to impact reporting and resource allocation.

By lowering the barrier to AI adoption in the nonprofit sector, Deloitte is helping expand the addressable AI market beyond enterprises and into mission-driven organizations. That shift mirrors what’s happening in SMB MarTech, where vendors are racing to simplify AI tools for smaller teams with limited budgets.

It also puts pressure on rival consultancies and tech providers. If Dot Good gains traction, competitors may need to rethink how they package AI services for nonprofits—or risk ceding influence in a sector that, while not always lucrative, carries reputational and long-term strategic value.

A pragmatic step, not a silver bullet

Dot Good won’t magically solve the nonprofit sector’s funding or talent challenges. AI tools still require clean data, leadership buy-in, and ongoing change management—areas where many organizations struggle.

But by combining discounted consulting, tailored implementation, and free education, Deloitte is making a pragmatic bet: that nonprofits don’t need moonshot AI experiments, but practical, guided adoption that respects their constraints.

In a market saturated with AI promises, that grounded approach may be exactly what resonates.

Get in touch with our MarTech Experts.

Radio frequency signals run modern networks—but they remain invisible, abstract, and notoriously difficult to interpret in real-world environments. VIAVI Solutions wants to change that.

The company has announced the integration of RF Viewer, a new augmented reality (AR) solution, into its OneAdvisor 800 Wireless test platform. The move signals a broader shift in how RF analysis is performed: away from dense charts and static measurements, and toward intuitive, visual, in-the-field understanding.

Developed in close collaboration with Verizon Wireless, RF Viewer overlays real-time RF signal strength directly onto a live video feed, allowing technicians to “see” RF emissions as they move through physical spaces. For telecom operators, smart building designers, and RF safety teams, the result is faster diagnostics, clearer decision-making, and improved on-site safety.

Making the invisible visible

Traditional RF testing tools require users to interpret spectrum graphs, signal metrics, and numeric readouts—skills that take years to master and are prone to error under time pressure. RF Viewer tackles that problem by translating RF data into a visual AR overlay, showing signal intensity, location, and distribution in real time.

Using a live camera feed, RF Viewer superimposes RF signal strength onto the physical environment, making it immediately clear where emissions are strongest, how they propagate, and where potential issues may exist. What once required educated guesswork now becomes visually obvious.

This approach is particularly valuable in dense RF environments, such as urban deployments, indoor venues, and smart buildings, where reflections, interference, and passive intermodulation (PIM) can degrade performance in hard-to-diagnose ways.

Built with operators, not just for them

VIAVI’s collaboration with Verizon Wireless played a key role in shaping RF Viewer’s design and use cases. According to Vikramjeet Singh, Associate Director of System Performance at Verizon Wireless, the AR-based approach has immediate operational benefits.

“This joint collaboration helps us promptly and efficiently locate PIM sources in a safe and effective manner,” Singh said. “RF Viewer enhances our ability to maintain optimal network performance while ensuring technician safety.”

That focus on safety is notable. RF safety assessments are often time-consuming and conservative by necessity. By visualizing RF exposure levels directly in the environment, RF Viewer can help teams identify high-exposure zones more quickly and plan mitigation strategies with greater confidence.

From expert-only tools to broader usability

While RF engineering has traditionally been the domain of highly specialized professionals, RF Viewer is designed to be accessible beyond expert users. VIAVI highlights a user-friendly interface that supports both seasoned RF engineers and less technical personnel who still need situational awareness of RF conditions.

Key features of RF Viewer include:

• Live AR overlays showing RF signal strength and spatial distribution

• Real-time diagnostics for troubleshooting, optimization, and interference identification

• Intuitive interaction, reducing reliance on complex RF charts and manual interpretation

This democratization of RF insight aligns with a wider industry trend. As networks become more complex—spanning private 5G, IoT, smart buildings, and hybrid indoor-outdoor deployments—organizations need tools that reduce cognitive load and speed up decision cycles.

Strengthening the OneAdvisor 800 platform

RF Viewer is not a standalone product. It extends the capabilities of the VIAVI OneAdvisor 800 Wireless, an all-in-one test platform already used across telecom, enterprise, and public-sector deployments.

The OneAdvisor 800 Wireless combines functions such as:

• Spectrum analysis

• Interference detection

• Transport network validation

• End-to-end performance testing

By adding AR-driven RF visualization, VIAVI is enhancing the platform’s value for field teams who need to diagnose issues quickly without switching between tools or relying on remote experts.

The integration also reinforces OneAdvisor 800’s positioning as networks evolve toward 5G Advanced and early 6G architectures, where higher frequencies, denser deployments, and more complex propagation characteristics increase the difficulty of RF planning and maintenance.

AR meets next-generation networks

The timing of RF Viewer’s launch is not accidental. As operators roll out mid-band and mmWave 5G—and begin laying the groundwork for 6G—the industry is grappling with new RF challenges. Higher frequencies behave differently, with shorter ranges, increased sensitivity to obstacles, and more complex interference patterns.

AR-based tools like RF Viewer offer a glimpse into how network testing may evolve: blending physical context with digital intelligence to create situational awareness that static dashboards cannot match.

Competitors in the test and measurement space have explored AI-driven analytics and automation, but AR remains a relatively untapped interface. VIAVI’s move could pressure rivals to rethink how they present RF data, especially as technician shortages make ease of use a strategic advantage.

Beyond telecom: broader implications

While telecom operators are an obvious audience, RF Viewer’s use cases extend into smart buildings, enterprise wireless, and RF safety compliance. As offices, factories, and public venues deploy private wireless networks and dense IoT infrastructures, understanding RF behavior indoors becomes critical.

For designers and facility managers, being able to visually assess RF coverage and exposure could streamline planning, compliance, and optimization—areas that increasingly overlap with enterprise IT and digital transformation initiatives.

A more human interface for RF intelligence

“RF Viewer bridges the gap between invisible RF data and human perception,” said Ian Langley, Senior Vice President of VIAVI’s Wireless Business Unit. By combining AR with RF analytics, he noted, the company aims to help technicians and engineers make faster, smarter decisions in the field.

That statement captures the broader significance of the launch. As networks grow more complex, the challenge is no longer just collecting data—it’s making that data understandable and actionable in real-world conditions.

With RF Viewer, VIAVI is betting that augmented reality can become a practical interface for network intelligence, not just a futuristic add-on. If adoption follows, AR may soon be as common in RF testing as spectrum analyzers are today.

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