The rapid rise of generative AI has dramatically increased the volume of visual content being created every day. However, much of that content comes with a frustrating limitation: once generated, the design is often locked inside a static image file.

Even small edits—changing text, repositioning elements, or adjusting layouts—can require starting the creative process over again.

To address this challenge, Canva has introduced Magic Layers, a new AI-powered technology designed to transform flat images into fully editable design files.

The feature, now available in public beta, allows users to convert static images into structured layers that can be edited directly within the Canva editor.

The result is a workflow where AI-generated visuals serve as a starting point for design rather than a finished, unchangeable output.

Unlocking Static Images

Traditional image formats such as PNG or JPG store designs as flattened visuals. Once exported, the original structure of the design disappears.

Text becomes pixels, shapes merge together, and the relationships between design elements are lost.

That means editing an image often requires rebuilding it from scratch.

Magic Layers aims to reverse that process.

By analyzing the structure of a flat image, the technology identifies separate components within the design and reconstructs them as editable elements.

These elements are then placed into layers within Canva’s editor, allowing users to move, modify, or replace them as if they were working with the original design file.

From Prompt to Editable Design

Magic Layers also works with AI-generated designs created inside Canva.

Instead of producing a static image, the system generates designs that remain fully editable from the beginning.

Users can create visual content from a prompt and immediately refine it by:

• Moving design elements

• Changing fonts or text content

• Replacing backgrounds

• Adjusting layout positioning

• Customizing colors and styles

This approach eliminates the need to repeatedly generate new images when a design requires small adjustments.

Instead, creators can iterate directly within the design environment.

How Magic Layers Works

The feature begins by analyzing the structure of an image.

When a user uploads a flat image, the system performs several actions automatically:

• Separates visual elements into individual movable objects

• Restores text as editable text boxes

• Preserves layout relationships between design components

• Maintains the original visual structure

The result is a design that closely matches the original image but behaves like a fully layered file.

For designers and marketers, this means existing images can be transformed into scalable assets without recreating them manually.

Beyond Traditional Vector Tracing

Tools capable of converting raster images into vector shapes have existed for years, but they come with important limitations.

Traditional vector tracing tools focus on identifying shapes and converting pixel regions into outlines. While this can reproduce visual forms, it does not capture the meaning or relationships between elements.

For example, a tracing tool cannot determine whether a shape represents:

• A background object

• A text block

• A design element grouped with others

Magic Layers approaches the problem differently.

Instead of simply tracing shapes, it analyzes the entire design structure to interpret how elements relate to each other.

This allows the system to restore editable text, maintain alignment relationships, and preserve the overall layout of the design.

The result is not just a traced image, but a reconstructed design file.

Built on Canva’s AI Design Model

Magic Layers is powered by the Canva Design Model, the company’s proprietary AI foundation model designed specifically for visual communication.

Since its introduction in 2024, the model has generated hundreds of millions of editable assets across formats such as:

• Presentations

• Documents

• Social media posts

The model also powers Canva’s integrations with major AI ecosystems including:

• ChatGPT

• Claude

• Microsoft Copilot

With Magic Layers, Canva extends the model’s capabilities beyond generating designs to reconstructing existing ones.

A New Workflow for Creators

For creative teams, the biggest value of Magic Layers may lie in how it changes the workflow around AI-generated content.

Generative AI has made it easier than ever to produce visual ideas quickly. However, the inability to edit those outputs has often limited their practical usefulness.

Magic Layers turns that process into a more flexible creative loop.

Instead of treating AI-generated images as final products, users can treat them as drafts that can be refined and adapted to different contexts.

For example:

• Marketing teams can modify AI-generated visuals to match brand guidelines

• Small businesses can update messaging without redesigning assets

• Content creators can remix visual concepts into new formats

This shift moves AI-generated content from a “one-shot” process to an iterative design workflow.

Beta Availability and Future Expansion

Magic Layers is currently available in public beta and supports single-page PNG and JPG files.

The feature is rolling out initially in:

• The United States

• The United Kingdom

• Canada

• Australia

Canva plans to expand support for additional file types and design capabilities in future updates.

The Bigger Picture

The launch of Magic Layers reflects a broader trend in AI-powered creativity.

While generative AI tools have made it easier to produce visual content, creators still need control over the final design.

Editable AI outputs represent a significant step toward combining automation with traditional design workflows.

By enabling users to modify AI-generated images without starting over, Canva aims to bridge the gap between generative AI and real-world creative production.

For designers, marketers, and everyday creators, the message is clear: AI may generate the first version of a design, but the creative process doesn’t end there.

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Enterprise data teams may soon spend less time writing code—and more time supervising AI agents that do the work.

At its annual platform rollout this week, Databricks introduced Genie Code, a new autonomous AI agent designed to handle complex data engineering, data science, and analytics tasks end-to-end. The release marks a major step in the company’s push toward what it calls “Agentic Data Work,” where AI systems plan, execute, and maintain data workflows while humans provide oversight.

The launch also comes alongside Databricks’ acquisition of Quotient AI, a startup focused on evaluating and improving AI agents through reinforcement learning. The technology will be embedded into Genie and Genie Code to continuously monitor and refine agent performance in production environments.

Taken together, the announcements signal a broader shift in enterprise data tooling—from AI that assists with coding to AI that actively manages data operations.

From Coding Assistants to Autonomous Data Agents

For years, AI tools in data engineering have focused on productivity boosts: autocomplete suggestions, SQL generation, and automated debugging.

But according to Databricks, those capabilities still leave much of the heavy lifting to human engineers.

Planning pipelines, orchestrating workflows, validating models, and maintaining production systems remain largely manual tasks—even with AI assistance.

Genie Code aims to change that dynamic.

“Software development has shifted from code-assistance to full agentic engineering in the past six months,” said Ali Ghodsi, co-founder and CEO of Databricks. “Genie Code brings this revolution to data teams. We’re moving from a world where data professionals are assisted by AI to one where AI agents do the work, guided by humans.”

The company calls the new paradigm Agentic Data Work, positioning it as the next stage in AI-driven enterprise software.

Extending the Genie Platform for Data Professionals

Genie Code builds on Genie, Databricks’ conversational data interface that allows business users to ask questions about enterprise data in natural language.

Genie connects to Unity Catalog, the company’s governance layer that captures metadata, business semantics, and lineage across enterprise datasets. This contextual layer enables Genie to deliver more accurate answers and enforce security policies.

Genie Code extends that same contextual intelligence to developers and data teams.

Instead of simply generating snippets of code, the agent can reason through multi-step problems, design production-ready systems, and deploy them across the Databricks platform.

In practical terms, that means the AI can handle tasks such as:

• Building and orchestrating data pipelines

• Debugging pipeline failures and data anomalies

• Creating dashboards and analytics workflows

• Deploying machine learning models into production

• Maintaining operational systems over time

Databricks says the system’s access to enterprise context—such as data lineage and governance policies—helps it avoid the pitfalls that have limited other coding agents.

Closing the Context Gap in Data Engineering

One of the biggest challenges facing AI coding tools is lack of context.

While AI can generate code effectively, it often lacks visibility into how enterprise systems are structured—what data sources exist, how they relate to each other, and what governance rules apply.

That gap is especially problematic in data engineering, where workflows often depend on complex pipelines, multiple environments, and strict compliance requirements.

Genie Code addresses this by integrating directly with Unity Catalog.

Through this connection, the agent gains visibility into:

• Data lineage and usage patterns

• Enterprise governance policies

• Business semantics and domain context

• Access controls and audit requirements

• External data sources across platforms

This context allows the AI agent to design systems that are production-ready from the start, rather than prototypes that require extensive manual adjustments.

A Machine Learning Engineer—In Software Form

Databricks positions Genie Code as functioning like a senior-level machine learning engineer embedded in the development environment.

The system can plan, write, and deploy machine learning models end-to-end, while also logging experiments through MLflow, Databricks’ open-source ML lifecycle platform.

It can also optimize model performance by fine-tuning serving endpoints and adjusting infrastructure configurations.

For organizations managing large-scale machine learning operations, these automated workflows could significantly reduce the time required to move models from experimentation to production.

Designed Like a Senior Data Architect

Beyond machine learning, Genie Code also handles the complexities of modern data engineering.

For example, the agent can automatically account for differences between staging and production environments—an area where less experienced engineers often run into problems.

It can also design workflows for change data capture (CDC), implement data quality expectations, and orchestrate pipeline processes that scale across enterprise data infrastructure.

Rather than writing quick scripts that work on test datasets, the system is designed to build durable architectures suitable for large production environments.

Autonomous Monitoring and Optimization

Perhaps the most ambitious feature of Genie Code is its ability to maintain systems after deployment.

The agent continuously monitors data pipelines and AI models running within the Databricks platform. When anomalies appear—such as failed workflows or degraded model performance—it can investigate and resolve issues autonomously.

The system can also analyze AI agent traces to identify hallucinations or incorrect outputs and adjust behavior accordingly.

Additionally, it optimizes resource allocation automatically, ensuring that compute resources are used efficiently before a human operator needs to intervene.

In effect, the agent acts as both developer and operator—writing systems and then managing them throughout their lifecycle.

Learning from Every Interaction

Another distinguishing feature is persistent memory.

Genie Code remembers prior interactions with development teams, adapting its internal instructions based on coding styles, workflow preferences, and project requirements.

Over time, that memory allows the agent to become increasingly tailored to each organization’s development environment.

In internal testing across real-world data science tasks, Databricks says Genie Code improved the success rate of coding agents from 32.1% to 77.1%, more than doubling the effectiveness of existing tools.

Early Enterprise Feedback

Some early enterprise users are already experimenting with the system.

At SiriusXM, the platform is being used across multiple data engineering tasks, including notebook authoring, SQL development, and debugging complex pipelines.

“Genie Code acts as a hands-on development partner that helps our data teams deliver high-quality work in less time,” said Bernie Graham, vice president of data engineering at SiriusXM.

Energy company Repsol is also testing the system within its analytics operations.

According to Emilio Martín Gallardo, principal data scientist at Repsol’s Data Management & Analytics division, the platform enables teams to hand off complex workflows to an AI system that understands enterprise governance and internal tools.

Instead of manually connecting notebooks, pipelines, and models, engineers can rely on the AI agent to orchestrate those processes automatically.

Databricks Acquires Quotient AI to Improve Agent Reliability

To strengthen the reliability of its AI agents, Databricks simultaneously announced the acquisition of Quotient AI.

Quotient specializes in evaluating and improving the performance of AI systems through continuous monitoring and reinforcement learning.

Its technology measures answer quality, detects regressions, and identifies failures early—feeding that data back into AI models to improve future performance.

The startup’s founders previously worked on improving code quality systems for GitHub Copilot, giving them direct experience with large-scale AI coding platforms.

By embedding Quotient’s evaluation capabilities into Genie and Genie Code, Databricks aims to ensure that AI agents not only execute tasks but also improve over time.

The Bigger Picture: Autonomous Data Platforms

The launch of Genie Code reflects a broader transformation underway in enterprise data platforms.

As AI capabilities expand, the industry is moving beyond tools that simply assist engineers toward systems that can autonomously operate complex workflows.

This shift mirrors what has already happened in software development, where AI coding agents are increasingly capable of building entire applications with minimal human intervention.

For enterprise data teams, the implications could be significant.

Data engineering and machine learning pipelines are notoriously complex and resource-intensive to maintain. Automating those processes could dramatically accelerate analytics development and reduce operational costs.

But it also raises new questions about governance, oversight, and trust—areas where enterprise platforms like Databricks are investing heavily.

With Genie Code, Databricks is betting that the future of data work won’t just involve smarter tools—it will involve AI teammates capable of running the entire system.

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Retail marketers have long obsessed over abandoned carts and first-purchase conversions. But the real growth opportunity may start after the checkout confirmation page.

That’s the key takeaway from the 2026 Cross-Channel Benchmark Report released by Listrak, which suggests retailers are increasingly turning post-purchase engagement into a revenue driver rather than treating it as a simple “thank you” message.

According to the report, post-purchase campaigns generated a 38% increase in revenue per send (RPS) as brands adopted more advanced personalization strategies—sometimes tailoring messaging down to the exact SKU a shopper bought.

The shift signals a broader change in retail marketing strategy: lifecycle marketing is moving deeper into the customer journey, where retention and cross-sell opportunities often outperform acquisition in terms of ROI.

Retailers Turn the Post-Purchase Moment Into a Growth Engine

Historically, post-purchase emails served mainly as order confirmations or shipping updates. The report argues that approach is rapidly becoming outdated.

Retailers in 2025 increasingly personalized follow-up communications based on specific products purchased, layering in:

• Product-based recommendations tied to the original purchase

• Cross-category offers to expand basket size over time

• Targeted win-back campaigns triggered by previous buying behavior

The result: a more dynamic lifecycle approach where post-purchase messaging functions as a revenue engine rather than a transactional obligation.

“Gone are the days of a generic one-touch ‘Thank You’ message,” said Ross Kramer, co-founder and CEO of Listrak. “Lifecycle precision is becoming the competitive advantage, and the post-purchase journey is now a meaningful cross-channel touchpoint.”

In other words, retailers are learning that the best time to recommend the next purchase might be immediately after the last one.

Transactional Messages Get a Boost From Inbox Changes

The report also highlights an unexpected factor helping engagement metrics: changes in email inbox design.

Transactional messages—including order confirmations, shipping notifications, and related updates—saw improvements in click-through rates across both email and SMS campaigns. One reason is evolving inbox categorization systems from major providers like Google and Apple.

Both companies have continued expanding message sorting and foldering features—particularly for transactional communications—making it easier for consumers to find and interact with purchase-related updates.

For marketers, that means transactional messages are gaining renewed value as engagement channels, especially when they include contextual product recommendations or promotions.

SMS Continues Its High-Intent Marketing Streak

Another standout trend in the report is the continued growth of SMS-driven marketing campaigns.

Triggered SMS programs saw year-over-year increases in both message volume and revenue contribution, reinforcing the channel’s role as a high-intent communication tool. The fastest-growing SMS campaigns included:

• Browse abandonment alerts (page and product views)

• Post-purchase follow-ups

• Time-sensitive promotional triggers

These campaigns perform well partly because SMS reliably reaches consumers on their primary device—and typically within minutes.

For retailers navigating increasingly crowded email inboxes, SMS offers a direct path to consumers who have already demonstrated purchase intent.

Why Lifecycle Marketing Is Becoming a Retail Imperative

The findings arrive at a time when ecommerce brands face rising acquisition costs and growing pressure to maximize the value of existing customers.

In that environment, lifecycle marketing—particularly post-purchase engagement—offers a relatively efficient path to revenue growth.

Industry analysts have increasingly pointed to post-purchase marketing as a key driver of:

• Higher repeat purchase rates

• Increased customer lifetime value (CLV)

• Stronger brand loyalty

The Listrak report reinforces that trend, suggesting retailers are beginning to operationalize post-purchase engagement with the same level of sophistication once reserved for acquisition campaigns.

Data Across 12 Ecommerce Verticals

Listrak’s benchmark report analyzes cross-channel marketing performance across 12 ecommerce verticals, combining campaign performance data with industry-specific insights.

The platform also highlights its use of AI-driven analytics—marketed as Listrak Intelligence—to surface patterns across large retail datasets.

While AI-powered marketing analytics tools have become increasingly common across the martech ecosystem, the growing emphasis on lifecycle insights reflects a broader shift in how brands measure success.

Instead of focusing purely on immediate conversion metrics, many retailers are now tracking how well their messaging programs influence repeat purchases over time.

The Competitive Stakes for Retailers

If the report’s findings hold true across the broader market, retailers that fail to evolve their post-purchase strategy may leave significant revenue on the table.

In the modern ecommerce funnel, the sale is no longer the finish line—it’s the starting point for the next transaction.

Retailers that can orchestrate personalized cross-channel journeys immediately after checkout may gain a measurable edge in customer retention and lifetime value. Those that rely on static confirmation emails risk missing one of the most valuable marketing touchpoints available.

And as lifecycle marketing grows more sophisticated, the humble order confirmation may quietly become one of the most strategic messages in retail.

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The generative AI boom has made it easier than ever for brands to produce ads. But according to a new entrant in the martech space, speed isn’t the real problem—effectiveness is.

That’s the pitch behind BrandComms.AI, an agentic AI platform that officially launched in the United States this week. The company says its system aims to solve a growing industry challenge: how to use AI to create advertising that actually performs, rather than simply generating more content at scale.

With early customers including Taco Bell and Realtor.com, BrandComms.AI is positioning its technology as an alternative to both traditional agency workflows and the wave of basic generative AI creative tools flooding the market.

Instead of focusing primarily on automation, the platform blends AI production capabilities with decades of marketing science to guide how campaigns are conceived, tested, and deployed.

An AI Platform Built Around Advertising Effectiveness

The company’s approach is grounded in roughly 30 years of proprietary marketing science from Forethought. That research focuses on how consumers make decisions within specific product categories—a dataset BrandComms.AI says can inform more effective creative development.

By embedding those insights directly into its AI workflows, the platform attempts to ensure every piece of creative output is tied to brand strategy, category dynamics, and consumer psychology.

“The industry doesn’t have an AI problem—it has an effectiveness problem,” said Isobell Roberts, the company’s chief AI officer.

“Generative and agentic AI has made it easy to produce more advertising, but not better advertising,” Roberts said. “Our platform applies existing brand insights, governance, and proven consumer decision-making science so creative is built to perform before it reaches the market.”

That distinction—optimizing ads before they launch rather than analyzing performance after the fact—reflects a broader shift in how marketers are beginning to use AI tools.

The Brand Engine Behind the Platform

At the center of the platform is the BrandComms.AI Content Store, a proprietary system designed to function as a brand intelligence engine.

Rather than relying on generic large language model outputs, the Content Store is trained on a company’s:

• Historical brand assets

• Consumer insights and research

• Creative learnings from past campaigns

• Category-level decision drivers

The goal is to ensure that AI-generated creative remains aligned with brand identity and differentiation.

In practice, that means a fast-food chain, for example, would generate creative concepts rooted in the specific competitive dynamics and consumer behaviors of the quick-service restaurant category rather than generic advertising frameworks.

The approach also aims to address a growing concern among marketers: that generative AI tools often produce content that feels polished but indistinguishable across brands.

An “Agentic Workforce” of AI Models

Another key differentiator is the platform’s agentic architecture.

Instead of relying on a single generative model, BrandComms.AI orchestrates an “AI workforce” that can involve up to 64 specialized models across different stages of creative production.

These models can handle tasks such as:

• Concept ideation

• Script and dialogue generation

• Voice synthesis

• Visual generation

• Production workflows

Because the platform is model-agnostic, brands can select the most appropriate AI models for each stage of the process—or combine AI-generated assets with traditional creative production in a hybrid workflow.

That flexibility may prove important as enterprises experiment with multiple AI ecosystems while avoiding lock-in with a single vendor.

Compressing Creative Timelines

Traditional advertising production cycles often take months, particularly for large campaigns that require multiple rounds of concept development, stakeholder reviews, and pre-testing.

BrandComms.AI claims its platform can reduce that process to weeks rather than months by unifying several stages of creative development within a single system.

The workflow integrates:

1. Concept ideation

2. AI-assisted content production

3. Pre-launch effectiveness testing

4. Campaign execution

Because concepts are evaluated against predefined performance thresholds before deployment, the platform attempts to identify stronger creative options earlier in the development process.

For marketers operating under tighter budgets and faster campaign cycles, that compression could become a competitive advantage.

Humans Still Stay in the Loop

Despite its heavy use of AI, BrandComms.AI emphasizes that human governance remains central to the platform.

Creative development includes human oversight to ensure campaigns maintain emotional resonance, strategic alignment, and brand safety—areas where fully automated systems often struggle.

This hybrid approach reflects a growing consensus across the advertising industry: AI may accelerate creative workflows, but human judgment still plays a critical role in shaping brand storytelling.

A Crowded—but Rapidly Growing—AI Advertising Market

BrandComms.AI enters a market already crowded with generative AI tools promising faster content production. Platforms from major tech vendors and emerging startups alike are competing to become the backbone of AI-powered marketing operations.

But many of those tools focus primarily on content generation rather than creative effectiveness.

By centering its pitch on marketing science and pre-launch performance validation, BrandComms.AI is betting that brands will increasingly prioritize tools that improve outcomes rather than simply increasing output.

If that thesis proves correct, the next wave of AI advertising platforms may look less like automated content factories—and more like decision engines guiding how campaigns are built from the start.

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Metal additive manufacturing firm Velo3D is entering fiscal 2026 with a leaner balance sheet after a pair of debt conversions cut the company’s outstanding debt by roughly 60%.

The move came after CEO Arun Jeldi acquired a $5 million promissory note from an existing debt holder and converted it into common stock at $16.38 per share—a price notably higher than the company’s recent trading levels.

At the same time, company director Ken Thieneman converted a $10 million promissory note into equity at $10.50 per share, according to the terms of the original convertible debt agreement.

Together, the transactions reduce Velo3D’s outstanding debt to roughly $10 million, strengthening the company’s financial position as it attempts to scale its additive manufacturing platform.

Insider Confidence Signals Strategic Reset

Debt-to-equity conversions aren’t uncommon in capital-intensive technology sectors, but when they occur at a premium to market price, they often signal strong internal confidence.

In this case, Jeldi’s conversion price of $16.38 per share stands out because it exceeds the company’s prevailing stock valuation at the time of the transaction. That effectively represents a voluntary premium paid by the CEO to increase his equity stake.

“My decision to acquire and convert this debt at a significant premium to market reflects my belief in the long-term value of Velo3D,” Jeldi said in a statement.

He added that the company has now “substantially deleveraged” its balance sheet and is focusing on growth initiatives in the coming fiscal year.

Why Debt Reduction Matters in Additive Manufacturing

The financial restructuring comes at a critical moment for companies in the additive manufacturing sector.

Metal 3D printing platforms require substantial capital investments in:

• Advanced manufacturing systems

• Materials research and development

• Aerospace-grade quality control infrastructure

• Customer deployment and support networks

Reducing debt can provide breathing room for companies pursuing long-term industrial adoption.

For Velo3D, whose systems are widely used in high-performance industries such as aerospace and defense, balance sheet flexibility may be essential as customers expand production programs and supply chain requirements.

A Platform Built for Complex Metal Printing

Velo3D has built its reputation around high-precision metal additive manufacturing systems designed to produce complex geometries that are difficult—or impossible—to manufacture using traditional methods.

The company’s technology has been used in applications across aerospace, defense, and energy sectors where lightweight components, intricate cooling channels, and optimized structural designs can deliver performance gains.

In recent years, additive manufacturing has gained renewed attention as governments and manufacturers seek to modernize supply chains and localize production capacity, particularly for critical industrial components.

Entering Fiscal 2026 With a Leaner Capital Structure

With the debt conversion complete, Velo3D now enters fiscal 2026 with significantly lower leverage and increased insider equity alignment.

While the company still faces the broader challenges affecting the additive manufacturing market—including fluctuating capital spending and adoption cycles—the balance sheet improvement could provide greater flexibility as it pursues growth opportunities.

For investors, the insider-led conversion sends a clear message: leadership is betting that the company’s next chapter will be worth more than its current market valuation suggests.

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As social platforms increasingly double as retail storefronts, public relations agencies are retooling their services to help brands convert online buzz into actual sales.

That’s the strategy behind the latest move from 5W PR, one of the largest independently owned PR and digital marketing agencies in the United States. The firm announced an expansion of its beauty practice that will now include TikTok Shop–aligned PR campaigns and creator amplification programs, aimed at helping beauty brands capitalize on the explosive growth of social commerce.

The new offering focuses on integrating traditional PR tactics with commerce-driven social media strategies—particularly on TikTok and its rapidly growing in-app retail feature, TikTok Shop.

For beauty brands, the goal is simple: turn viral attention into measurable revenue.

PR Meets Social Commerce

Social commerce has quickly become one of the fastest-growing areas of digital retail, with platforms blending entertainment, influencer content, and instant purchasing.

Beauty products, in particular, have become a dominant category on TikTok thanks to viral trends, creator tutorials, and real-time product reviews.

Recognizing that shift, 5W PR’s expanded service offering is designed to align public relations campaigns with TikTok Shop activations.

The programs include:

• Creator partnerships and influencer collaborations

• Product seeding campaigns with social creators

• Strategic media and trend-based pitching

• Real-time trend activation and content alignment

• Affiliate management for creator-driven sales

• Performance measurement and campaign amplification

By linking PR efforts directly to social commerce initiatives, the agency aims to ensure that brand storytelling translates into both engagement and transactions.

Turning Viral Moments Into Sales

The rise of TikTok has transformed how beauty brands launch products and build communities. Viral videos can drive massive spikes in product demand, sometimes selling out items within hours.

But capturing that momentum requires coordinated marketing across creators, media coverage, and retail channels.

“Tiktok has become a pivotal platform for beauty brands to reach highly engaged, trend-conscious audiences,” said Ilisa Wirgin.

“By aligning PR programs with TikTok Shop and creator amplification, we are helping brands translate social momentum into measurable business impact while maintaining authentic storytelling and credible media coverage,” Wirgin said.

In other words, the agency wants to bridge the gap between traditional brand storytelling and direct-response social commerce.

The Evolution of Beauty Marketing

The beauty industry has long been an early adopter of influencer marketing and digital-first campaigns. Platforms like YouTube and Instagram previously shaped the category’s marketing playbook.

Now TikTok is taking center stage.

Short-form video, algorithm-driven discovery, and built-in commerce features have made the platform a powerful launchpad for new products and emerging brands.

At the same time, beauty consumers increasingly expect authentic recommendations from creators rather than traditional advertising.

That shift has pushed agencies to rethink how PR campaigns operate.

Instead of focusing solely on editorial coverage and press placements, modern beauty PR now blends:

• Media relations

• Influencer marketing

• Social-first content creation

• Commerce-driven performance tracking

The expansion from 5W PR reflects this broader transformation.

Blending PR With Creator-Led Growth

The agency says its new services build on its experience running social-first beauty campaigns that combine media outreach, influencer collaborations, and digital PR.

By integrating those tactics with TikTok Shop activations, the firm hopes to provide brands with a more unified marketing approach across both awareness and conversion channels.

That’s particularly important for emerging beauty brands, which often rely heavily on social discovery and creator endorsements to compete with established players.

At the same time, larger brands are increasingly investing in social commerce strategies to reach younger audiences and drive faster purchase cycles.

The Bigger Trend: Commerce Everywhere

The expansion also highlights a larger industry trend: the continued blending of content, community, and commerce.

Platforms once used purely for entertainment or social networking are rapidly becoming end-to-end shopping environments.

For agencies, that means marketing campaigns must now operate across multiple layers simultaneously—storytelling, creator relationships, algorithmic distribution, and direct sales.

With TikTok continuing to reshape how consumers discover and purchase products, agencies that can bridge the gap between PR credibility and social commerce performance may find themselves increasingly in demand.

And for beauty brands navigating the crowded digital marketplace, the next viral moment might not just build awareness—it might ring the cash register.

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Enterprise customer experience platform Glance has promoted longtime executive Heather Nightingale to Vice President of Product, a move that underscores the company’s focus on evolving guided customer experience tools in the age of AI.

Nightingale, who has worked with Glance since 2016, will now oversee product strategy and product marketing, helping shape the company’s roadmap as enterprises rethink how automation and human interaction coexist in digital customer journeys.

The promotion comes as AI-driven CX tools proliferate across industries, forcing vendors to differentiate not only on automation capabilities but also on how effectively they enable real-time human support when digital interactions become complex.

A Longtime Leader Steps Into Product Strategy

Before the promotion, Nightingale served as Senior Director of Product Marketing and Partnerships, where she helped define the company’s product positioning and market strategy.

During that time, she also expanded the company’s analyst relations and partner programs while supporting Glance during a key period of growth.

In her new role, Nightingale will focus on aligning market trends, enterprise customer needs, and company growth priorities into a unified product strategy.

Her responsibilities include:

• Strategic product planning

• Expanding customer feedback initiatives

• Guiding AI-related product development

• Strengthening the company’s enterprise security and compliance positioning

The goal is to ensure the platform continues to support complex enterprise environments where customer support, sales, and service interactions often involve sensitive data and regulated workflows.

Guided CX Meets the AI Boom

Glance’s core technology centers on guided customer experience, enabling companies to provide real-time visual collaboration between customers and support agents.

Through co-browsing and screen-sharing features embedded in websites, mobile apps, and authenticated portals, agents can visually guide customers through digital processes—such as completing applications, troubleshooting issues, or navigating complicated workflows.

The approach has become increasingly relevant as businesses push customers toward self-service digital channels while still needing human support for high-stakes interactions.

Nightingale believes the rapid rise of AI will only increase the importance of that balance.

“Customer experience is entering a new phase where AI is more visible and accessible than ever before,” she said. “But AI doesn’t replace the moments where personal connection is most impactful—it enhances them.”

In practice, that means combining automated tools with human assistance when digital journeys become confusing or complex.

A Focus on Secure Enterprise AI

As AI capabilities expand across CX platforms, Glance says it is prioritizing secure, practical implementations designed for highly regulated industries.

Many large enterprises—particularly in financial services, healthcare, and telecommunications—must comply with strict security and privacy standards when deploying new technologies.

Glance’s visual collaboration platform is designed to operate within those environments, enabling real-time assistance without exposing sensitive information or disrupting existing technology ecosystems.

The company says Nightingale’s product leadership will help guide how AI features are integrated into those workflows.

CEO Signals Strategic Direction

For Glance CEO Tom Martin, the promotion reflects both Nightingale’s contributions and the company’s future priorities.

“Heather has been instrumental in shaping how we think about product, market alignment, and the evolving CX landscape at Glance,” Martin said.

He added that as AI continues reshaping digital customer interactions, the company needs focused leadership to guide both product vision and execution.

That leadership will be critical as CX technology vendors compete to deliver smarter automation while preserving the human interactions that still define customer loyalty.

The Future of Human-Centered Digital Support

As businesses continue automating customer service with chatbots, AI assistants, and self-service portals, the challenge is ensuring those systems don’t create friction when customers encounter problems.

That’s where guided CX platforms are carving out a niche—providing human assistance layered on top of digital experiences.

Glance’s technology aims to bridge that gap by allowing agents to visually guide customers through tasks in real time, reducing frustration and improving resolution rates.

For Nightingale, the mission remains simple: keep the human element at the center of digital interactions.

“Our customers choose us because we help them humanize and bring resolution to the high-stakes interactions that define brand loyalty,” she said.

As enterprises continue blending automation with human support, the next evolution of customer experience may depend less on replacing people—and more on helping them collaborate more effectively with AI.

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Global adtech firm Affle is doubling down on product innovation in the AI era, appointing veteran adtech executive Martin Price as Vice President of Products at its U.S. subsidiary.

In the new role, Price will lead product strategy and innovation for Affle’s 3i verticalized AI platform, with a mandate to accelerate growth of the company’s consumer intelligence and mobile advertising ecosystem.

The hire comes as Affle expands its footprint in developed markets and looks to scale its AI-driven advertising platforms amid intensifying competition across the mobile adtech sector.

A Veteran of the Mobile Advertising Industry

Price brings more than two decades of experience across the digital advertising ecosystem, having previously held senior product leadership roles at companies including Liftoff, Vungle, Yahoo, Vdopia, OpenX, and BidMachine.

That background spans mobile advertising networks, programmatic platforms, and large-scale digital media ecosystems—experience Affle says will help guide the next phase of its product development.

Price’s immediate focus will be expanding Affle’s AI-led consumer platform, which combines data-driven audience insights, mobile advertising technology, and generative AI capabilities to deliver personalized marketing experiences for brands and advertisers.

Driving the 3i Strategy

At the center of Affle’s technology roadmap is its “3i” strategy, which emphasizes innovation, impact, and intelligence across its AI-powered marketing platforms.

According to Charles Yong, Price’s appointment is intended to translate that strategy into market-ready products with stronger vertical integration of data and AI capabilities.

Yong also pointed to the company’s intellectual property portfolio—39 unique patents covering roughly 300 enforceable patent claims—as a key foundation for future product innovation.

The company believes that combining proprietary technology with AI-driven product leadership will help it unlock new opportunities across advertising and consumer engagement.

Expanding in Developed Markets

The appointment also reflects Affle’s broader expansion push across developed markets under Sameer Sondhi, CEO of North America and Chief Strategic Investments Officer.

Sondhi said Price’s track record building scalable products in the adtech ecosystem made him a natural fit for the company’s next growth phase.

“Martin brings a powerful combination of strategic vision, deep industry knowledge, strong market understanding, and execution discipline,” Sondhi said.

His experience developing high-impact advertising platforms will help the company strengthen its presence among brands and advertisers in mature digital markets, where competition among adtech providers is particularly intense.

AI and the Future of Mobile Advertising

Affle’s platforms focus on AI-powered consumer intelligence, enabling advertisers to optimize targeting, personalize engagement, and maximize marketing ROI across mobile channels.

The company combines proprietary data, audience insights, and generative AI technologies to help brands connect with users throughout the mobile marketing journey.

Increasingly, that means moving beyond traditional automation toward adaptive AI systems capable of responding to user behavior in real time.

Price says that shift is reshaping the industry.

“Affle is uniquely positioned at the intersection of AI-driven consumer intelligence and performance-led marketing,” he said.

His priority will be maintaining an innovation-driven product strategy while delivering measurable business outcomes for advertisers and partners.

The Next Phase of AdTech Platforms

The global adtech sector is undergoing rapid transformation as generative AI and machine learning reshape everything from ad creation to campaign optimization.

Platforms that once focused primarily on media buying and programmatic infrastructure are now evolving into end-to-end intelligence engines capable of predicting user behavior and influencing marketing outcomes.

Affle’s strategy reflects that broader shift.

By strengthening its product leadership and expanding its AI platform capabilities, the company is aiming to position itself as a key player in the next generation of mobile marketing technology.

And with Price now guiding product development, Affle is betting that deeper AI integration—and smarter consumer intelligence—will define the future of digital advertising.

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