Enterprise software training often focuses on teaching employees how to use systems. Whatfix wants to teach them how to perform.

The company has launched AI Roleplay training within its Mirror platform, transforming the tool into what it calls an AI-first enterprise training environment. The new capability combines adaptive AI-powered customer conversations with realistic enterprise system simulations, allowing frontline teams to practice both communication and workflows in the same environment.

The goal is to prepare employees for real-world customer interactions—not just technical system usage.

From System Simulation to Scenario-Based Training

Whatfix originally introduced Mirror in 2024 as a training platform designed to simulate enterprise applications. The idea was straightforward: allow employees to learn complex software workflows without touching live systems or risking real customer data.

But as organizations adopted the platform, Whatfix discovered a broader challenge. Many frontline employees—particularly in customer support, sales operations, and service roles—struggle not only with software tools but also with the unpredictable nature of customer conversations.

AI Roleplay training aims to fill that gap.

Instead of practicing workflows alone, employees can now engage in simulated conversations powered by AI agents that respond dynamically to what the learner says.

The system mirrors real customer interactions while simultaneously requiring the employee to navigate enterprise applications inside the simulated environment.

A Unified Training Environment

Whatfix argues that combining roleplay with system simulation addresses a major limitation in most AI training platforms.

Many roleplay tools allow employees to practice conversations with AI-generated customers, but those simulations usually occur outside the systems employees actually use. That disconnect can limit training effectiveness.

Mirror’s new approach attempts to replicate the full working environment.

Employees practice both sides of the job at once:

• What to say to the customer

• How to execute the workflow inside the system

That combination creates a more realistic simulation of day-to-day work scenarios.

“Simulation teaches process, and roleplay builds judgment and confidence,” said Khadim Batti, co-founder and CEO of Whatfix. “With AI Roleplay in Mirror, we’re helping enterprises reduce time-to-proficiency and improve customer outcomes before employees go live.”

Rapid Adoption and Revenue Growth

The company says enterprise demand for the platform is accelerating quickly.

According to Whatfix, Mirror’s annual recurring revenue grew more than 200% year over year after introducing AI roleplay features in 2025. The platform also reached $3 million in ARR within six quarters, a milestone the company achieved through deployments across customer support and operations teams.

Looking ahead, Whatfix expects Mirror’s revenue to triple in 2026, fueled by expanded enterprise rollouts.

Several Fortune 100 companies have already implemented Mirror as part of their training programs for frontline teams, the company said. Early adopters have reported improvements in metrics such as:

• Time-to-proficiency for new employees

• Average Handle Time (AHT) in customer support interactions

• Customer Satisfaction (CSAT) scores

These metrics are closely tied to operational efficiency and customer experience—two areas enterprises increasingly view as strategic priorities.

Key Features of AI Roleplay in Mirror

The AI Roleplay capability introduces several features designed to make training more adaptive and scalable.

Adaptive AI conversations
AI-generated customers respond dynamically to learner inputs, creating realistic interaction scenarios rather than scripted dialogues.

AI-assisted scenario creation
Training managers can generate new roleplay scenarios quickly using AI prompts, reducing the time required to build training modules.

Readiness evaluation
The platform assesses employee performance during simulated workflows, giving managers visibility into whether learners are prepared for live environments.

Multilingual support
Global organizations can deploy consistent training experiences across international teams.

Together, these capabilities aim to make training programs easier to scale while maintaining realism.

A Growing Category in Enterprise Learning

Industry analysts say the convergence of AI roleplay and system simulation reflects a broader shift in workforce enablement.

Traditional corporate training programs often rely on static modules, documentation, or classroom sessions that struggle to replicate real-world conditions.

Gina Smith, research director at IDC, says combining simulated workflows with AI-generated conversations could significantly improve readiness for customer-facing roles.

“By combining AI-driven roleplay and system simulation in a single solution, Whatfix offers organizations a unified approach to employee enablement,” Smith said. “Learners can safely gain hands-on experience before transitioning to live systems.”

As enterprises continue investing in AI-powered productivity tools, training systems capable of preparing employees for those environments are becoming increasingly important.

Part of a Larger AI Strategy

The AI Roleplay launch is also part of Whatfix’s broader push toward AI-native enterprise enablement.

The company’s platform already focuses on guiding employees through complex software environments with in-app assistance and contextual learning. Mirror extends that strategy into pre-deployment training.

Rather than learning on the job—or in live systems—employees can now practice high-stakes workflows and customer interactions before entering production environments.

For large enterprises, the payoff could be significant. Faster training cycles and fewer real-world mistakes translate into lower operational risk and better customer experiences.

Training for the AI Workplace

As AI reshapes enterprise workflows, employee training is evolving alongside it.

Organizations are increasingly looking for tools that go beyond documentation and basic tutorials. Instead, they want environments where employees can rehearse complex tasks in realistic scenarios.

By combining AI roleplay with system simulations, Whatfix is positioning Mirror as a bridge between traditional training and real-world performance.

If the approach proves effective at scale, enterprise training platforms may begin to look less like learning portals—and more like simulated workplaces.

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Enterprises may be experimenting with AI everywhere—but scaling it safely is another story. That’s the gap Alteryx says it’s closing.

At the Gartner Data & Analytics Summit, the analytics and automation company announced it has surpassed $1 billion in annual recurring revenue (ARR) while powering more than 380 million automated workflows each year across its customer base. The milestone comes as organizations move from AI experimentation to full operational deployment—where governance, data quality, and automation become mission-critical.

Central to that strategy is Alteryx One, the company’s unified platform designed to help enterprises operationalize AI and analytics with trusted, repeatable workflows.

AI Ambition Meets Data Reality

Corporate AI spending isn’t slowing down. According to Alteryx, 89% of enterprises plan to maintain or increase AI investment in 2026 as generative and agentic AI technologies reshape enterprise operations.

But enthusiasm hasn’t solved one of the most persistent barriers: data trust.

The company cites research showing that 28% of organizations have limited or no confidence in the accuracy of their data, while nearly half of business leaders say high-quality, governed data is the single most important factor for successful AI deployment.

That gap—between AI ambition and data reliability—is exactly what Alteryx is positioning its platform to address.

The Logic Layer Behind Enterprise AI

Alteryx One is designed as a “logic layer” for enterprise AI, connecting data, workflows, and business context into a governed automation framework.

Rather than simply delivering analytics dashboards or AI models, the platform focuses on repeatable workflows that capture how decisions are made.

These workflows preserve critical elements such as:

• Business logic and decision rules

• Data lineage and traceability

• Governance and compliance controls

• Repeatable automation pipelines

For enterprises deploying AI agents that can take actions—rather than just generate insights—those safeguards become increasingly important.

“When automation becomes agentic, inconsistency isn’t just inefficient—it becomes an enterprise risk,” said Alteryx CEO Andy MacMillan. “AI requires a governed and repeatable logic layer.”

In other words, organizations don’t just need smarter AI—they need systems that ensure AI-driven decisions remain transparent and auditable.

Automation at Massive Scale

Alteryx’s growth metrics suggest enterprises are already leaning heavily on workflow automation to operationalize analytics.

Customer organizations executed over 380 million automated workflows in 2025, a sharp increase from 260 million in 2023.

Those workflows typically handle data preparation, analytics processes, and operational automation tasks that once required manual intervention.

The scale reflects a broader shift happening inside enterprise analytics teams. Instead of running one-off analyses, organizations are embedding data-driven processes directly into operational systems.

In that environment, automation becomes the delivery mechanism for analytics—and the foundation for AI execution.

Generative AI Meets Governed Workflows

To support the latest wave of AI capabilities, Alteryx has also embedded generative AI features directly into the Alteryx One platform.

These capabilities allow users to:

• Interact with enterprise data using natural language queries

• Accelerate model development through AI-assisted workflows

• Embed AI-generated insights directly into operational automation

The company says the goal is to combine the productivity gains of generative AI with the governance and traceability required by large organizations.

Without that governance layer, enterprises risk scaling unreliable outputs as quickly as they scale productivity.

Simplified Access and Growing Adoption

Part of Alteryx’s current momentum is tied to a new simplified edition pricing model, designed to make advanced analytics and AI capabilities more accessible across business teams.

The company says thousands of customers are already upgrading to the updated Alteryx One editions.

Because the platform integrates directly with enterprise data sources, AI models, and business applications, organizations can embed analytics and automation deeper into existing workflows rather than relying on standalone tools.

Security and governance features built into the platform also address enterprise concerns around compliance, data access, and model oversight.

A Community That Fuels Adoption

Beyond product innovation, Alteryx credits much of its long-term adoption to its user community.

In 2025, the company celebrated 10 years of its global community platform, which now includes more than 750,000 members worldwide.

The community has become a hub for shared workflows, peer-driven solutions, and best practices—resources that help organizations deploy analytics projects faster and reduce the learning curve for new users.

Alexander Abi-Najm of Aimpoint Digital, an Alteryx ACE community leader, says the ecosystem continues to play a major role in driving innovation.

“It’s exciting to see how the tools continue evolving,” Abi-Najm said. “The community helps users solve complex problems and share insights that create real business impact.”

Expanding the Cloud Data Ecosystem

As part of its broader growth strategy, Alteryx is also deepening partnerships with major cloud providers.

The company recently expanded its collaboration with Google Cloud, enabling organizations to work directly with large-scale cloud data environments while accelerating analytics and AI development.

Cloud-native integrations have become essential as enterprises increasingly centralize data pipelines in cloud platforms and run AI workloads at scale.

A Brand Refresh for the AI Era

At the Gartner Data & Analytics Summit in Orlando, Alteryx also unveiled a refreshed brand identity designed to reflect its shift from a traditional analytics vendor to a unified AI and automation platform.

The rebrand aligns with the company’s broader push to position Alteryx One as the foundation for enterprise AI execution—a platform where data preparation, analytics, automation, and AI-driven insights converge.

With more than $1 billion in ARR and hundreds of millions of automated workflows running annually, Alteryx is betting that the next phase of enterprise AI won’t be about building models.

It will be about operationalizing them.

And for many organizations, that means turning trusted data and governed workflows into the backbone of AI at scale.

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Independent grocery retailers are getting a digital boost. Swiftly, a leading provider of retail technology, has announced a new partnership with Merchants Distributors (MDI) to modernize web, circular, and digital marketing capabilities for the distributor’s network of independent stores.

The collaboration leverages Swiftly’s SmartCircular™ solution alongside its Audience Optimizer™ platform to bring AI-driven promotions, digital circular amplification, and website tools to MDI retailers—helping them better connect deals with shoppers and in-store performance.

Bridging Digital and Physical Retail

The modern grocery shopper moves seamlessly between online and offline channels, making omnichannel strategies critical for smaller retailers. Swiftly’s technology is designed to consolidate web presence, digital circulars, and advertising into a single platform, giving independent grocers tools previously available only to national chains.

“Swiftly’s website and digital circular capabilities allow us to offer a modern digital presence for retailers who may not have ecommerce, while seamlessly integrating with existing platforms where applicable,” said Mary Kellmanson, SVP Marketing at MDI. “This partnership expands our digital toolkit in ways that support measurable sales growth inside the store.”

The combined platform enables MDI retailers to:

• Deliver more relevant deals across channels

• Connect digital engagement directly to in-store performance

• Accelerate digital maturity with scalable, flexible solutions

AI-Powered Promotions with Audience Optimizer™

A standout component of the partnership is Swiftly’s Audience Optimizer™, which leverages first-party data to target shoppers with product offers across digital channels. By analyzing engagement patterns, retailers can reactivate lapsed shoppers, increase trip frequency, and drive incremental basket growth—all while linking campaigns back to measurable sales results.

“As independent grocers navigate a rapidly evolving digital landscape, having the right mix of foundational tools and targeted capabilities is essential,” said Keith Kirk, CFO at Swiftly. “We’re proud to support MDI and its retailers with flexible technology that strengthens their digital presence and helps translate shopper engagement into meaningful store results.”

Rolling Out in Early 2026

The partnership rollout is scheduled to begin in early 2026, with phased deployments across MDI’s network for:

• Website platforms

• SmartCircular™ digital circulars

• Audience Optimizer™ promotional campaigns

By unifying these tools under one system, Swiftly and MDI aim to provide independent retailers with a scalable digital foundation that supports both shopper connection and long-term revenue growth.

The collaboration highlights the growing importance of AI-powered, omnichannel technology in regional and independent grocery markets—bringing the type of digital sophistication once reserved for large retailers to smaller operators looking to compete in a hybrid shopping environment.

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CallMiner, a leader in customer experience (CX) automation, has rolled out a suite of advanced AI capabilities designed to deliver deeper insights, richer context, and more flexible automation for enterprises. The updates, announced today, enhance the company’s market-leading platform with new AI classifiers, customizable summaries, and advanced sentiment analysis.

Smarter Classification Across Channels

Central to the update is CallMiner’s expanded collection of AI classifiers, which now support whole-contact sentiment analysis. These classifiers automatically categorize and interpret conversations across multiple channels and languages, leveraging company-specific contextual intelligence from recent interactions.

By capturing the subtleties of tone—including mixed emotions, domain-specific language, and short-form interactions like voicemails and chat—CallMiner enables organizations to move beyond traditional sentiment detection. The new classifiers align with emerging regulatory frameworks, including the EU AI Act, ensuring transparency, explainability, and human oversight while retaining customizable category creation for coaching, agent evaluation, and business decision-making.

“Advanced AI classifiers make it easier than ever to extract actionable insights from customer conversations,” said Bruce McMahon, Chief Product Officer at CallMiner. “Organizations can now automate smarter, act faster, and better understand the context behind every interaction.”

Customizable Summaries for Actionable Insights

CallMiner has also introduced flexible AI-generated summary templates, allowing organizations to tailor interaction summaries to specific compliance, operational, or analytical goals. Whether providing CX-focused summaries for agents or generating internal insights for business teams, the platform gives users control over format, content, and presentation.

Unlike platforms with a one-size-fits-all approach, CallMiner allows teams to:

• Write custom prompts from scratch

• Adapt pre-built templates for faster deployment

• Test and refine summaries for any business use case

This ensures every summary captures the most relevant insights in real time, improving agent performance and customer outcomes.

Integrated Intelligence for Faster, Smarter Decisions

With the combined power of AI classifiers and CallMiner AI Assist, the platform now offers:

• Advanced business intelligence through agentic AI-driven insights

• Enhanced visibility with rich visualizations such as tree maps, Sankey diagrams, and stacked bar charts

• Flexible workflows via seamless export and integration with enterprise systems

• Scalable automation that accelerates action from insights to decision-making

The updates underscore CallMiner’s focus on delivering an intelligent foundation for CX automation, enabling enterprises to operationalize conversation insights and enhance both agent performance and customer experience.

“These enhancements build on our market-leading platform, delivering greater flexibility, speed, and relevance,” McMahon said. “By strengthening the foundational intelligence layer, we help organizations unlock measurable improvements in efficiency and CX outcomes.”

A Platform for Agentic AI Discovery

With these new capabilities, CallMiner reinforces its position as a trusted partner for enterprises looking to leverage AI-driven automation across customer interactions. From richer contextual understanding to smarter workflow automation, the platform is designed to empower organizations to turn insights into action faster, while maintaining transparency, governance, and adaptability.

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Enterprise AI is moving faster than many organizations can govern, and Dataiku aims to provide the missing control layer. Today, the company unveiled its Platform for AI Success, a major evolution of its enterprise AI platform designed to take AI initiatives from pilots into measurable business performance.

The launch introduces three first-to-market products:

• Dataiku Agent Management – Cross-platform visibility, governance, and business-value measurement for deployed AI agents

• Dataiku Cobuild – AI-assisted agent and workflow creation in a fully visual, inspectable environment

• Dataiku Reasoning Systems – Orchestrated, industry-specific decision intelligence delivered by coordinated agent teams

The platform will be demonstrated at the Gartner Data & Analytics Summit in Orlando (March 9–11, Booth 401), showing how enterprises can move beyond experimentation to accountable AI at scale.

A Unified Orchestration Layer

As AI spreads across clouds, models, and agents, fragmentation is creating risk: duplicated work, blind spots, inconsistent performance, and rising operational costs. Dataiku’s Platform for AI Success unifies people, orchestration, and governance in a single environment, allowing enterprises to:

• Connect data platforms, foundation models, and third-party agent frameworks without vendor lock-in

• Build, validate, deploy, monitor, and manage AI systems under embedded governance

• Enable domain experts, analysts, and engineers to contribute safely and productively

“Without orchestrating complex technologies and governing AI at every step, initiatives never move beyond proof-of-concept,” said Florian Douetteau, CEO of Dataiku. “We built our platform to solve exactly that roadblock.”

Dataiku Agent Management: Measuring Impact, Not Just Uptime

One blind spot in enterprise AI is operational relevance. An agent may be technically running but fail to deliver real business value. Dataiku Agent Management addresses this by measuring agents against business KPIs, detecting performance drift, flagging risks, and triggering governance workflows. Organizations can finally answer: Is this agent actually worth running?

The Early Access Program for Agent Management is available now.

Dataiku Reasoning Systems: Orchestrated Decision Intelligence

Going beyond individual automation, Reasoning Systems combine data, models, agents, and business rules into a single governed decision environment. Enterprises can orchestrate AI systems that mirror real-world operations while maintaining transparency and oversight.

The Manufacturing Operations module is available immediately, with Supply Chain and Financial Risk modules planned for 2026.

Dataiku Cobuild: Visual, Business-Friendly AI Development

Launching in June 2026, Cobuild allows business users to describe objectives in natural language, generating end-to-end AI projects—including pipelines, models, agents, and applications—within a visual, step-by-step interface. Unlike opaque “vibe coding” assistants, Cobuild ensures transparency, traceability, and governance from design to deployment.

From AI Activity to Measurable Performance

The Platform for AI Success reflects a larger market shift: competitive advantage now comes from coordinating AI across systems, empowering experts, and embedding governance, not just access to models.

“No amount of prompt engineering replaces structured orchestration,” said Clément Stenac, CTO of Dataiku. “Enterprise decisions require data feeding models, models informing agents, and agents controlled by business rules and human oversight. That coordination layer is missing in most deployments, so our platform fills that void.”

By acting as an independent orchestration layer, Dataiku helps enterprises scale AI responsibly while retaining flexibility in technology choice—turning AI pilots into measurable, trusted business outcomes.

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Adobe (Nasdaq:ADBE) and Major League Baseball (MLB) are stepping up their multi-year partnership, aiming to redefine fan engagement with AI-powered digital experiences. As part of the expansion, Adobe will serve as the official Presenting Sponsor of MLB Opening Day in 2026, 2027, and 2028, while providing the league with advanced marketing, content, and creative solutions.

The partnership builds on MLB’s existing Adobe integrations, which have unified data and content across the league to deliver personalized, fan-centric experiences. Now, Adobe’s AI-driven tools will extend that capability, helping both the league and its clubs scale content, optimize campaigns, and empower fans to create their own digital expressions of baseball passion.

AI and Creativity at the Heart of Fan Engagement

MLB and Adobe aim to enhance the fan experience both inside and outside the ballpark. From home viewers to attendees at stadiums, fans will have AI-assisted tools to customize content, while the league gains the ability to personalize offers, campaigns, and communications in real time.

“Adobe is a global leader in digital experiences and creativity,” said Uzma Rawn, CMO and SVP, Global Corporate Partnerships, MLB. “This relationship provides us with the technology to better understand and deliver what our fans want and need digitally.”

Adobe echoes that sentiment: “Our work with MLB is setting a benchmark for engaging fans in the era of AI,” said Rachel Thornton, CMO Enterprise at Adobe. “We’re helping deliver personalized content and gameday experiences while empowering individual fan creativity.”

What the Expanded Partnership Delivers

1. Scaled Marketing Campaigns
Adobe GenStudio for Performance Marketing allows MLB to accelerate the planning, creation, activation, and measurement of campaigns. Teams can quickly generate multiple on-brand variations for different channels, keeping content relevant whether fans are at home or in the stadium.

2. Improved Brand Discoverability
With Adobe LLM Optimizer, MLB can ensure its content surfaces prominently in AI-driven search results, helping fans find tickets, stats, and experiences while maintaining brand visibility across digital platforms.

3. Faster Asset Production
Adobe Firefly Services and Custom Models help MLB meet the growing demand for personalized content, streamlining workflows from asset creation to resizing for multiple marketing channels. Generative AI enables teams to produce high-quality campaigns faster than ever.

4. Empowering Fan Creativity
Fans can leverage Adobe Express, paired with Firefly generative AI, to craft personalized posts, stories, and graphics in MLB team colors and logos. This brings a new level of engagement, allowing fans to participate in the digital fan ecosystem actively.

Setting a New Standard for Digital Fan Experiences

By combining AI-driven marketing tools, generative content services, and fan-facing creativity applications, Adobe and MLB aim to redefine how fans interact with the sport. The expanded partnership underscores a broader trend: sports leagues increasingly rely on personalization, real-time engagement, and fan-generated content to build loyalty and capture attention in a competitive digital landscape.

With Opening Day sponsorship through 2028, Adobe’s technology will be front and center as MLB blends the excitement of the ballpark with AI-enhanced digital experiences, providing fans and teams with tools to create, explore, and engage like never before.

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• Integrated campaign planning
  
• Unified data insights
  
• Creative production support
  
• Post-campaign measurement
  

Global footwear and accessories retailer ALDO Group is consolidating its digital marketing strategy—and it’s handing the keys to P3 Media. The company has named the Shopify-focused agency its digital marketing partner for the entire ALDO portfolio, including ALDO, Sperry, G.H.Bass, and Call It Spring.

The move signals a deeper push into performance marketing and digital commerce as the retailer manages an increasingly complex ecosystem spanning ecommerce, marketplaces, and thousands of physical stores worldwide.

A Global Digital Overhaul

ALDO Group has long positioned itself as an omnichannel retail innovator, blending brick-and-mortar retail with robust ecommerce operations. But as digital retail evolves—and performance marketing grows more data-driven—the company is aiming to tighten its media strategy across brands and markets.

P3 Media’s mandate: unify media buying, streamline campaign execution, and build a scalable digital growth engine that works across ALDO’s global portfolio.

In practice, that means aligning paid media, creative, data analytics, and AI-powered optimization under a single strategy rather than fragmented campaigns by brand or region.

The goal is straightforward but ambitious: accelerate digital revenue while maintaining consistent brand performance worldwide.

“ALDO Group operates at the forefront of digital retail, managing a highly complex global commerce ecosystem across multiple brands and markets,” said Monica Provenza, Head of Digital Commerce at ALDO Group. “At this inflection point in digital commerce technology, it was critical to partner with an agency that can operate as a true extension of our team while bringing every tool necessary to help us achieve our ambitious growth vision.”

Why P3 Media Won the Pitch

The partnership follows a competitive pitch process in which ALDO evaluated potential agencies on media expertise, responsiveness, strategic vision, and innovation capabilities.

P3 Media ultimately secured the contract by positioning itself as a hybrid partner: part media agency, part ecommerce growth consultancy.

The firm is best known as a Shopify Platinum Partner, a designation reserved for agencies with deep expertise in Shopify’s enterprise commerce platform. Its client work typically blends performance marketing, data science, and AI-driven optimization—an increasingly common formula among digital commerce agencies trying to differentiate beyond basic media buying.

For ALDO, that blend of commerce and marketing expertise appears to have been decisive.

“P3 demonstrated the strategic depth, technical fluency, and collaborative mindset we were looking for,” Provenza said.

A Portfolio Play

Unlike many agency appointments that focus on a single brand, this deal spans ALDO Group’s entire footwear portfolio.

That includes:

• ALDO, the company’s flagship global fashion footwear brand

• Sperry, known for boat shoes and heritage lifestyle products

• G.H.Bass, a historic American footwear label

• Call It Spring, a younger, trend-driven brand aimed at Gen Z shoppers

Managing performance marketing across four distinct brand identities—and multiple regions—adds a layer of complexity.

Each brand serves different audiences, price tiers, and geographic markets. Aligning them under a single performance marketing framework requires balancing centralized data with localized creative strategies.

That’s precisely where ALDO expects P3 Media to deliver.

The AI Factor

Another notable element of the partnership is the emphasis on AI-driven marketing.

Retailers increasingly rely on machine learning for tasks like audience targeting, campaign optimization, predictive merchandising, and customer lifetime value modeling.

Agencies, meanwhile, are racing to build AI into their marketing stacks to stay competitive.

P3 Media says it plans to combine its AI capabilities with media and creative strategy to support ALDO’s digital expansion.

“It’s an honor to partner with ALDO Group and support such an iconic portfolio of brands,” said Aanarav Sareen, CEO and co-founder of P3 Media.

David Wagoner, the agency’s CMO and co-founder, emphasized the collaborative nature of the partnership.

“From the outset, the ALDO team has communicated a clear and compelling vision for where they want to go,” Wagoner said. “We’re excited to align our media, data, creative, and AI capabilities around that vision and help bring their next chapter of digital marketing to life.”

Why This Matters for Retail Marketing

ALDO’s agency consolidation reflects a broader shift happening across retail.

Brands with complex global footprints are increasingly moving away from fragmented marketing stacks—multiple agencies, regional media buyers, disconnected analytics platforms—and toward unified growth partners.

The reasons are both strategic and practical:

• Performance marketing has become highly data-intensive

• AI-driven optimization requires centralized datasets

• Ecommerce growth demands tight integration between media and commerce platforms

Retailers also face rising acquisition costs across platforms like Google, Meta, and TikTok, making optimization and efficiency critical.

By consolidating media execution under one agency partner, brands aim to improve attribution, streamline decision-making, and scale campaigns more effectively.

The Bigger Commerce Strategy

For ALDO Group, the move fits into a broader digital transformation effort underway across the fashion retail industry.

Footwear brands are increasingly investing in:

• Direct-to-consumer ecommerce

• Marketplace expansion

• Omnichannel fulfillment

• Data-driven marketing strategies

Retailers that once relied heavily on physical stores are now treating digital channels as their primary growth engine.

ALDO already operates thousands of retail locations globally, but its ecommerce presence continues to expand across multiple platforms and markets.

Partnering with a Shopify-specialized agency suggests the company intends to deepen its commerce integration with marketing performance—something many fashion brands are now prioritizing.

What Comes Next

The immediate focus for P3 Media will likely involve consolidating campaign infrastructure and optimizing media performance across ALDO’s global operations.

Longer term, the partnership could extend into broader areas such as AI-powered personalization, creative automation, and deeper integration between marketing and commerce analytics.

For ALDO, the outcome will be measured in a familiar metric: digital growth.

For the agency world, the deal is another sign that enterprise retailers increasingly want marketing partners capable of blending technology, commerce expertise, and performance marketing under one roof.

In the modern retail playbook, media buying alone no longer cuts it.

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