Industrial manufacturers are increasingly tightening cybersecurity controls as global operations become more connected. Now, automotive supplier Ronal Group is taking a major step in that direction.

The company has consolidated remote access management across its global facilities using secure connectivity technology from Rockwell Automation, aiming to improve operational resilience while aligning with emerging cybersecurity regulations.

For manufacturers operating across multiple production sites and supplier networks, remote access is both a productivity necessity and a potential security risk. Ronal’s new centralized system is designed to address both concerns by introducing standardized governance, encrypted communications, and tighter access control.

A Unified Approach to Global Industrial Connectivity

The new implementation allows Ronal Group to manage authorized external access across its facilities through a single, consistent framework.

Under the system, external users—including vendors, technicians, and service partners—can securely access operational technology environments when required, while remaining subject to clearly defined governance rules.

Key features of the deployment include:

• Encrypted communication channels to protect industrial network traffic

• Role-based access authorization to ensure users only access permitted systems

• Centralized monitoring capabilities for improved oversight and auditing

The approach helps reduce the complexity that often arises when individual facilities adopt separate remote-access tools or policies.

According to Stefan Turi, industrial networks and cybersecurity sales executive at Rockwell Automation, secure connectivity has become a fundamental requirement for modern manufacturing environments.

“Secure and reliable connectivity is an essential component of modern industrial operations,” Turi said in a statement. “We are pleased to support the Ronal Group in strengthening its global connectivity framework.”

Addressing Regulatory Pressure and Cyber Risk

The move also reflects the growing influence of cybersecurity regulations in industrial sectors.

Manufacturers operating in Europe are preparing for stricter compliance obligations under NIS2 Directive, which requires stronger protection of critical infrastructure and digital systems.

Remote access pathways are a frequent target for cyberattacks on industrial environments. Consolidating access through a centrally managed system can help organizations enforce consistent policies, track activity, and respond quickly to potential threats.

For Ronal Group, the platform supports regulatory alignment while improving transparency across its manufacturing IT environment.

Simplifying Vendor and Supplier Access

A major challenge in industrial cybersecurity is balancing protection with operational efficiency.

Manufacturers rely heavily on third-party vendors, equipment suppliers, and service providers who require remote access to maintain machinery, perform diagnostics, or deploy updates.

According to Matthias Kratz, head of manufacturing IT at Ronal Group, the company needed a solution that could streamline those interactions without sacrificing security.

“The introduction of a harmonized solution supports our ongoing efforts to enhance operational resilience and transparency,” Kratz said. “It allows us to manage authorized external access in a structured and controlled manner while supporting our compliance objectives.”

He added that the platform’s configuration flexibility allowed Ronal to tailor the system to its operational needs and quickly roll it out to suppliers and partners.

Industrial Cybersecurity Becomes a Strategic Priority

The deployment reflects a broader trend across the manufacturing sector: cybersecurity is moving from an IT concern to a core operational priority.

As factories adopt connected production systems, industrial IoT technologies, and remote diagnostics tools, the attack surface expands significantly. At the same time, manufacturers face growing regulatory scrutiny and supply-chain security requirements.

Companies like Rockwell Automation are increasingly positioning their solutions as foundational infrastructure for secure digital transformation in industrial environments.

For Ronal Group, which manufactures wheels for consumer and commercial vehicles across multiple global facilities, the move is part of an ongoing effort to strengthen operational resilience.

“Cybersecurity is a strategic priority for the Ronal Group,” Kratz said. “We continuously review and enhance our security framework to protect our operations, partners, and customers.”

In an era where manufacturing uptime depends as much on network security as mechanical reliability, that strategy is becoming essential.

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Artificial intelligence is steadily reshaping the programmatic advertising stack. But the next phase may look less like a smarter dashboard and more like an AI teammate.

That’s the premise behind a new partnership between Dstillery and connected TV ad platform Keynes, which are rolling out what they describe as an “agentic” advertising workflow powered by Dstillery’s DS‑1.

The idea: use AI agents to handle complex tasks—audience discovery, custom modeling, and campaign activation—that traditionally required multiple tools, manual processes, and hours of coordination across platforms.

With DS-1 integrated into Keynes’ workflow, those tasks can now be executed through a conversational interface inside Slack, dramatically reducing turnaround time for campaign planning.

The Rise of Agentic Programmatic Advertising

Programmatic advertising has always promised automation. But in practice, many workflows remain fragmented.

Media planners frequently bounce between audience platforms, data providers, demand-side platforms (DSPs), and analytics tools. Building a custom audience model can involve multiple handoffs between teams and systems, often stretching timelines into days.

Agentic advertising aims to eliminate that friction.

AI agents embedded directly into operational tools can handle complex sequences of tasks autonomously—interpreting requests, analyzing data, generating models, and executing actions across platforms.

For the programmatic ecosystem, that shift could fundamentally change how campaigns are built.

Instead of manually assembling audiences and activating them across systems, buyers increasingly instruct AI agents to perform those tasks automatically.

DS-1 Turns Slack Into a Programmatic Control Center

In the case of the Dstillery–Keynes partnership, the automation happens inside Slack, a platform many marketing and ad operations teams already use for collaboration.

DS-1 connects to Keynes’ Slack workspace through Model Context Protocol (MCP), allowing teams to interact with the AI system conversationally.

That integration eliminates several traditional bottlenecks:

• Switching between multiple adtech platforms

• Manual data transfers between systems

• Waiting for model builds or audience analysis

According to the companies, work that previously required 48 hours can now be completed in about five minutes.

For teams managing high-volume programmatic campaigns—especially in fast-moving channels like connected TV—that speed advantage can translate directly into operational efficiency and faster campaign launches.

From Audience Modeling to Activation

Once an audience model is generated through DS-1, campaigns can be activated through The Trade Desk, one of the largest independent demand-side platforms in the programmatic ecosystem.

This integration links together several critical pieces of the advertising supply chain:

1. Audience modeling and targeting via Dstillery’s AI

2. Campaign planning and operations through Keynes

3. Media activation on The Trade Desk’s DSP

The result is a streamlined workflow where planning, modeling, and activation happen in a unified environment.

For agencies and brands, the biggest benefit may be the ability to test and deploy audience strategies quickly without extensive operational overhead.

Building an Agentic Advertising Ecosystem

Dstillery says the DS-1 platform is designed to operate within a broader network of industry partners rather than as a standalone product.

The company is working with agencies, adtech vendors, and data providers to integrate agentic capabilities across the programmatic stack.

At the same time, emerging industry initiatives are beginning to define standards for AI-driven advertising workflows.

Two efforts gaining traction include:

• IAB Tech Lab’s Agentic Advertising Initiative, which explores how AI agents can operate safely and effectively in digital advertising systems

• The Agentic Advertising Organization, which is developing the Ad Context Protocol (AdCP) to enable interoperability between AI advertising tools

As those frameworks mature, platforms like DS-1 could connect more easily with additional partners, expanding their capabilities without requiring major workflow changes for advertisers.

Industry Leaders See AI as a “Co-Pilot” for Media Buyers

For programmatic platforms, agentic workflows represent a natural evolution of automation.

Instead of replacing human media buyers, many vendors are positioning AI agents as operational co-pilots—systems that handle complex data processing and execution while buyers focus on strategy.

According to Peter Ibarra, general manager of data partnerships at The Trade Desk, the shift is already underway.

“Media buyers using AI as a co-pilot to their strategies and tactics is a win-win for the industry,” Ibarra said, noting that agentic systems can help unlock greater efficiency in campaign planning and execution.

That efficiency could become particularly valuable as programmatic channels—especially connected TV—grow more complex and data-intensive.

Why Agentic Advertising Matters Now

The timing of this development is notable.

Advertisers are navigating several major shifts simultaneously:

• The continued growth of connected TV advertising

• Increasing reliance on AI-driven targeting and optimization

• The need to manage campaigns across an expanding set of platforms and data environments

Agentic AI has the potential to simplify that complexity.

By embedding automation directly into operational tools and enabling systems to communicate with one another, the approach could reduce manual workload while accelerating decision-making.

For Michael Beebe, CEO of Dstillery, the partnership with Keynes represents an early step toward a broader transformation in programmatic infrastructure.

“Custom modeling, planning, and activation can now happen in minutes instead of days,” Beebe said. “Together with partners like The Trade Desk, we’re building the infrastructure for programmatic’s next chapter.”

If that vision plays out, the next era of advertising automation may not just optimize campaigns—it may run much of the workflow itself.

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As generative AI reshapes how consumers discover brands online, search marketing is entering a new phase. To address that shift, Semrush has announced Spotlight 2026, its flagship marketing conference focused on helping brands compete in what the company calls the emerging “AI search era.”

The event will take place in London on October 13 and is expected to draw more than 1,000 senior marketing leaders, including chief marketing officers, SEO executives, brand leaders, and growth strategists.

At its core, Spotlight 2026 is designed to address one of the most pressing questions facing digital marketers today: how to maintain brand visibility when search is no longer limited to traditional search engines.

SEO Is Colliding With AI Search

For years, search engine optimization focused primarily on ranking within platforms like Google. But the rapid rise of large language models and AI-driven assistants has expanded the digital discovery landscape.

Consumers increasingly find brands through AI-generated answers, conversational interfaces, and recommendation systems powered by large language models.

That shift is forcing marketing leaders to rethink how visibility works.

According to Andrew Warden, chief marketing officer at Semrush, the traditional SEO playbook is no longer enough.

“In a world where LLMs and AI agents are changing every facet of brand discovery, staying static is not an option,” Warden said in the announcement. “Spotlight 2026 is designed to provide a practical roadmap to adapt and to own brand visibility.”

Rather than focusing solely on rankings or keyword strategy, marketers are increasingly being asked to manage brand presence across multiple AI-driven discovery channels.

A Blueprint for “Total Brand Visibility”

Semrush is positioning the conference around what it calls “total digital brand visibility”—a broader strategy that combines traditional search optimization with new AI-driven discovery techniques.

The event will feature a mix of keynote sessions, breakout discussions, and curated networking opportunities aimed at senior marketing leaders.

Key topics expected to dominate the agenda include:

The SEO + AI Search Opportunity
Marketers are exploring how traditional search optimization can work alongside AI-powered discovery systems to capture market share across both environments.

Total Digital Brand Visibility
Brands must now maintain share of voice across a fragmented ecosystem that includes search engines, AI chat interfaces, content platforms, and large language models.

Executive-Level Performance Management
Marketing leaders are under pressure to deliver measurable results even as technology platforms—and consumer discovery behavior—rapidly evolve.

Peer-to-Peer Strategy Exchange
With many organizations navigating similar challenges, the event will emphasize collaborative discussions among marketing executives facing comparable competitive pressures.

By focusing on operational strategies rather than theory, the conference aims to give attendees actionable frameworks they can implement immediately.

Hands-On Training With the AI Visibility Bootcamp

In addition to the main conference sessions, Semrush will also host a practical training track called the AI Visibility Bootcamp.

The program will provide hands-on instruction for marketers looking to expand brand presence across both traditional search engines and AI-powered discovery platforms.

Participants can expect workshops focused on real workflows, tools, and implementation strategies designed to help teams adapt to the evolving search environment.

This practical component reflects a growing demand among marketing leaders for tactical guidance on navigating AI-driven changes to digital discovery.

Building on Momentum From a Record 2025 Event

Spotlight 2026 follows a record-breaking 2025 edition of the conference, signaling strong industry interest in strategies that address the convergence of SEO, AI, and digital visibility.

That momentum reflects a broader shift in the marketing technology landscape.

Search is no longer just a channel—it’s becoming part of a larger ecosystem that includes AI assistants, recommendation engines, and conversational interfaces. As these technologies mature, brands will need to optimize their digital presence across a wider range of platforms and contexts.

For Semrush, which built its reputation on SEO analytics and visibility tools, the conference also underscores the company’s evolving positioning around broader online visibility management.

Additional details about speakers, agenda highlights, and ticket availability for Spotlight 2026 are expected to be announced in the coming months.

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The influencer economy is rapidly evolving—and increasingly, the star of the show isn’t human.

Creative platform Picsart is leaning into the rise of “faceless” content with the launch of two new AI tools designed to help creators build and scale digital personalities. The features, called Persona and Storyline, aim to solve two of the biggest challenges in AI-generated media: character consistency and narrative scalability.

With more than 130 million monthly users, Picsart says the tools are intended to help creators—from casual hobbyists to professional content teams—build digital characters that can anchor entire content ecosystems across social platforms.

The move also reflects a broader trend: as the influencer marketing economy pushes toward an estimated $40 billion, creators are increasingly experimenting with AI-generated identities that allow them to produce content without appearing on camera.

Faceless Content Is Becoming a Creator Strategy

Faceless content isn’t new, but the model has accelerated in the past two years thanks to generative AI tools that automate visual creation, voiceovers, and storytelling.

Instead of building a personal brand around their own identity, creators can develop fictional characters, animated mascots, or stylized avatars to represent their content channels. The approach offers several advantages: greater privacy, more creative flexibility, and the ability to scale production without the constraints of filming traditional video.

According to Hovhannes Avoyan, founder and CEO of Picsart, the trend is becoming a core strategy for creators seeking to grow digital businesses.

“Whether you're camera-shy, value your privacy, or just want creative freedom without the pressure of being the face of your brand, faceless content has become the go-to strategy for creators who want to scale,” Avoyan said in the announcement.

With Persona and Storyline, Picsart is attempting to streamline the entire process—from character creation to serialized storytelling.

Persona: A Digital Character Studio for Creators

The first new tool, Persona, functions as a character design engine inside the Picsart platform.

Users can create customized avatars that range from realistic human personas to animals, fantasy characters, or sci-fi-inspired figures. The system supports fine-grained customization, allowing creators to add details such as freckles, birthmarks, or stylized features that give characters a recognizable identity.

The goal is to bridge the gap between character design and scalable content production.

Once a persona is created, it can function as a digital brand ambassador or recurring protagonist across social posts, videos, and marketing campaigns.

For brands and creators experimenting with AI influencers, that consistency can be critical for building audience recognition.

Storyline: Solving the Character Consistency Problem

While many generative AI tools can produce impressive individual images or clips, maintaining the same character across different scenes remains a challenge.

Outfit changes, facial inconsistencies, and shifting visual details often break narrative continuity.

The new Storyline tool is designed to address that problem. It allows creators to build a character once and reuse it across multiple scenes and environments—whether a classroom, a cityscape, or a futuristic cyberpunk setting.

With Storyline, creators can generate short films, episodic series, or educational explainers while preserving the character’s visual identity between scenes.

This feature could prove especially valuable for creators producing serialized content, one of the fastest-growing formats on platforms like TikTok and YouTube.

AI Models Selected Automatically

Under the hood, the system relies on multiple generative AI models.

Picsart says the platform automatically selects the most suitable model for each creative task, drawing from systems such as Veo 3.1 and Kling 3.0.

That model orchestration allows users to focus on creative direction rather than technical configuration—an approach increasingly common among consumer-facing AI tools.

By abstracting away the underlying models, platforms like Picsart aim to make advanced generative AI accessible to a wider creator audience.

Expanding Picsart’s AI Creator Ecosystem

The release of Persona and Storyline continues Picsart’s push to evolve from a simple editing app into a full AI-powered content creation platform.

The company recently introduced several AI-driven features, including Aura, Flow, and AI Assistant, all designed to automate different parts of the creative process.

Combined with the new character and storytelling tools, these features support the company’s broader vision: lowering the technical barriers that traditionally limit content creation.

Picsart says the platform has now surpassed 2.5 billion lifetime downloads, a milestone that highlights the growing demand for accessible creative tools powered by generative AI.

The Future of AI Influencers and Digital Characters

The launch of Persona and Storyline arrives as AI-generated influencers and digital characters gain traction across social media and marketing.

Brands are experimenting with virtual ambassadors that can post around the clock, adapt to different campaigns, and exist entirely in digital form. Meanwhile, independent creators are exploring AI personas as a way to scale content production without the logistical challenges of filming traditional videos.

If those trends continue, tools that simplify character creation and narrative production could become foundational for the next generation of creator-driven media.

For Picsart’s global user base, the message is straightforward: you don’t have to appear on camera to build a successful content brand anymore.

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The AI infrastructure race is accelerating—and networking may be the quiet bottleneck everyone’s trying to solve.

Netris, a vendor focused on network automation and multi-tenancy for AI infrastructure, says demand for its platform is surging. The company reported 622% year-over-year ARR growth in 2025, alongside rapid adoption from AI cloud operators building large-scale GPU infrastructure.

In the past 10 months alone, Netris says it has onboarded 15 AI cloud operators across more than 20 deployments, many spanning multiple data centers. The company claims this footprint now makes its platform the most widely deployed network automation and multi-tenancy layer for AI infrastructure.

That momentum reflects a broader shift in the AI cloud market: as organizations race to build GPU-heavy infrastructure, networking—particularly multi-tenant, automated networking—has emerged as a critical layer for delivering AI services at scale.

AI Infrastructure Is Moving Faster Than Traditional Networking

The scale of AI infrastructure investment is staggering. According to industry projections, global AI infrastructure spending could reach $758 billion by 2029, while AI-driven economic impact may exceed $22 trillion by 2030.

But the networking tools originally designed for traditional enterprise data centers weren’t built with AI workloads in mind.

Training clusters and GPU clouds demand extremely high bandwidth, dynamic resource allocation, and strict tenant isolation. At the same time, operators must deliver cloud-like functionality such as elastic networking, rapid provisioning, and secure multi-tenancy.

Legacy approaches struggle to keep up.

Enterprises that attempt to build network automation internally often face long development timelines and fragile results. Manual configuration errors remain common, and delays in provisioning infrastructure directly translate into lost revenue—particularly when expensive GPUs sit idle.

In AI infrastructure, every idle GPU is effectively money left on the table.

What Netris Is Actually Building

Netris positions its platform—known as NAAM (Network Automation and Abstraction for Multi-tenancy)—as a purpose-built control layer for AI infrastructure operators.

Instead of relying on legacy fabric managers or manually built automation scripts, the platform enables cloud operators to automate the entire lifecycle of AI networking, including provisioning, segmentation, and capacity allocation.

The result, the company argues, is the ability to launch GPU cloud services far faster than traditional approaches.

Among the capabilities Netris highlights:

• Automated multi-tenancy: Tenants receive dedicated network isolation automatically when GPU resources are provisioned.

• Dynamic GPU pool resizing: Operators can adjust cluster capacity without interrupting active AI workloads.

• Elastic networking features: Capabilities like elastic IPs and load balancing that resemble hyperscale cloud infrastructure.

• Reduced configuration errors: Automation helps eliminate manual networking mistakes that can disrupt customer workloads.

For AI cloud providers trying to monetize GPU infrastructure quickly, these features can make the difference between launching services in weeks versus years.

Tight Integration With NVIDIA’s AI Networking Stack

A key factor behind Netris’ growth appears to be its integration with NVIDIA’s expanding AI infrastructure ecosystem.

The company says it is the first independent software vendor validated by NVIDIA for AI network automation, with deployments supporting multi-tenant environments built on NVIDIA Spectrum-X Ethernet networking.

Using AI factory simulations in NVIDIA Air, Netris has extended integrations across several pieces of the AI networking stack, including:

• Spectrum-X Ethernet networking

• Quantum InfiniBand

• NVL72 GPU architectures

• BlueField DPUs

• Edge and virtual networking components

That ecosystem alignment matters because many emerging AI cloud providers rely heavily on NVIDIA reference architectures to build GPU clusters.

Networking platforms that integrate seamlessly with those architectures can dramatically simplify deployment.

The Softgate Layer: Filling the Gaps Switches Don’t Cover

Beyond switch-level automation, Netris also introduced a new component called Softgate HS, designed as a horizontally scalable, multi-tenant edge gateway.

In practice, this fills a networking gap that traditional switching infrastructure doesn’t address.

While switches can provide segmentation and traffic management inside the data center fabric, cloud providers also need application-level networking capabilities such as tenant routing, edge services, and flexible connectivity.

Softgate aims to deliver those features as a software layer integrated with the Netris automation platform.

According to the company, 95% of customers running Netris-managed switch fabrics have adopted Softgate as well, suggesting operators see value in extending automation beyond the core network fabric.

Neoclouds and Sovereign AI Operators Drive Demand

The company’s customer base reflects several fast-growing segments in the AI infrastructure market.

One major category is neocloud providers—new entrants focused specifically on delivering GPU-based AI compute. Companies such as STN, Boost Run, and TensorWave have built AI cloud services using Netris as their networking foundation.

These providers compete with hyperscalers by offering highly specialized GPU clusters optimized for AI training and inference workloads.

Another major segment is sovereign AI infrastructure operators, which are building national AI capabilities in response to data sovereignty and geopolitical concerns.

Organizations including TELUS in Canada, DCAI in Denmark, and Yotta Data Services in India are deploying AI infrastructure designed to meet national compliance and security requirements.

In these environments, strict multi-tenancy and workload isolation are essential.

“Dedicated GPU isolation, compliance, and predictable performance are table stakes,” said Sabur Mian, founder and CEO of STN. “Netris provides the network-level abstraction and segmentation that makes secure, cloud-scale multi-tenancy possible.”

Global Expansion Signals Market Momentum

Alongside customer growth, Netris has also expanded its global footprint.

The company now operates teams in:

• The United States

• Taiwan

• Australia

• India

It plans to expand further in 2026 with new operations in the United Kingdom and Singapore.

That geographic spread reflects where AI infrastructure demand is emerging: not just in hyperscale markets but also in regional cloud ecosystems and sovereign AI initiatives.

Governments and enterprises increasingly want domestic AI capacity rather than relying entirely on global cloud providers.

The Bigger Trend: AI Infrastructure’s Missing Layer

The rapid growth Netris is reporting highlights a broader trend in the AI infrastructure stack.

Much of the industry’s attention has focused on GPUs, AI accelerators, and data center buildouts. But networking automation—particularly multi-tenant networking—has become a critical layer enabling AI infrastructure to function as a cloud service.

Without automation, GPU clusters are difficult to scale, expensive to operate, and slow to provision.

That’s why infrastructure vendors across the ecosystem—from networking companies to AI platform providers—are racing to build orchestration layers for GPU-heavy environments.

If Netris can maintain its current trajectory, it could become one of the defining control layers in the emerging AI cloud stack.

For now, the company’s pitch is straightforward: if AI infrastructure is the next trillion-dollar buildout, networking automation may determine who can actually deploy it at scale.

What’s Next

Netris says its partner ecosystem continues to expand as compute and platform vendors integrate with its networking automation stack.

The company is also deepening collaboration with NVIDIA and other infrastructure providers to support new GPU generations and AI networking architectures.

CEO Alex Saroyan frames the moment as an early phase in a much larger transformation.

“Building AI infrastructure is the opportunity of a generation,” he said. “The road ahead is even bigger as the industry enters its next phase of growth.”

Netris plans to showcase new capabilities and live demonstrations of its platform at the upcoming NVIDIA GTC conference in San Jose.

For AI cloud operators racing to build the next generation of infrastructure, networking automation may increasingly determine who wins the GPU cloud race.

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Marketing technology firm AppLovin is set to appear at the upcoming Morgan Stanley Technology, Media & Telecom Conference in San Francisco, where company executives will discuss strategy, market trends, and the evolving digital advertising landscape.

The session will take the form of a fireside chat scheduled for March 4 at 8:30 a.m. PT, offering investors and industry watchers an opportunity to hear directly from one of the fastest-growing players in mobile marketing infrastructure.

For a company that has steadily expanded its footprint across ad tech, app monetization, and performance marketing, the appearance comes at a moment when the mobile advertising ecosystem is undergoing significant transformation.

A Platform Built for Performance Marketing

Founded to help mobile developers scale user acquisition and monetization, AppLovin has evolved into a broader marketing technology platform that leverages machine learning to optimize advertising performance.

Its platform combines several core components, including:

• Ad discovery and demand aggregation

• Machine-learning-driven campaign optimization

• App monetization and mediation tools

• Real-time data analytics for advertisers and developers

These capabilities allow mobile app publishers and advertisers to manage the full lifecycle of digital campaigns—from acquiring users to maximizing revenue within apps.

Over time, AppLovin has expanded its focus beyond gaming, where it originally built much of its presence, toward a broader range of mobile-first businesses seeking scalable growth channels.

Why the TMT Conference Matters

The Morgan Stanley TMT conference is widely regarded as one of the most influential investor gatherings in the technology, media, and telecommunications sectors. Held annually in San Francisco, the event brings together executives from leading tech companies alongside institutional investors and analysts.

For publicly traded technology firms, these conferences serve as a key platform to:

• Provide updates on business performance

• Outline product and platform strategies

• Address investor questions about market trends and competition

For AppLovin, the fireside chat offers a chance to highlight how its platform is evolving amid major shifts in the digital advertising ecosystem.

Navigating a Rapidly Changing Ad Tech Landscape

The mobile advertising market has experienced a series of structural changes in recent years.

Privacy regulations, operating system policy changes, and new data governance rules have forced advertisers to rethink how they target users and measure performance. These shifts have accelerated the demand for AI-driven ad optimization platforms capable of delivering results without relying heavily on traditional tracking methods.

Platforms like AppLovin increasingly compete on algorithmic efficiency and large-scale data modeling, areas where machine learning plays a critical role.

As marketers search for alternatives to older attribution models, performance-focused advertising platforms have gained renewed attention from investors.

Industry observers are also watching how companies like AppLovin position themselves relative to larger ecosystems run by companies such as Google and Meta Platforms, which still dominate global digital advertising.

What Investors Will Be Listening For

While the conference session is structured as a conversation rather than a formal presentation, analysts will likely focus on several key areas:

AI-driven advertising technology.
Machine learning has become the backbone of modern marketing platforms, and investors will want insight into how AppLovin continues to refine its predictive models.

Expansion beyond mobile gaming.
The company has been steadily diversifying its customer base across different app categories and digital businesses.

Competitive positioning in ad tech.
As consolidation reshapes the industry, platforms that can deliver measurable performance at scale are attracting strong investor interest.

Monetization and platform growth.
Investors will be looking for signals about demand from developers and advertisers navigating a more complex privacy environment.

How to Watch the Session

The fireside chat will be streamed via webcast through the company’s investor relations portal. Interested viewers can access the event through the AppLovin investor website, where a replay will also be made available following the conference.

Conference appearances like this rarely introduce major product launches, but they often provide valuable insight into how technology companies see their role in rapidly evolving markets.

In AppLovin’s case, the conversation comes at a time when marketing platforms are increasingly defined by automation, AI-driven optimization, and the ability to adapt to privacy-first advertising models.

For marketers and investors alike, the discussion could offer a useful window into where performance marketing platforms are headed next.

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Global supply chain platform Avetta has been named to the The Hackett Group 50 to Know for 2025–2026, an annual assessment recognizing technology providers shaping the future of procurement.

The recognition comes from The Hackett Group, a global strategy and operations consultancy known for benchmarking enterprise performance and evaluating emerging technology trends across procurement, finance, and supply chain operations.

Avetta was selected from roughly 220 procurement technology providers worldwide, following an evaluation process conducted by Hackett’s Solution Intelligence analysts. The review included product briefings, demonstrations, year-round market monitoring, and direct engagement with vendors.

The list highlights companies that are pushing the procurement technology market forward, whether through innovation, market adoption, or the ability to deliver measurable business outcomes.

A Competitive Procurement Technology Landscape

Procurement technology has become one of the fastest-evolving segments in enterprise software. As supply chains grow more complex and risk-prone, organizations are turning to digital platforms that provide greater visibility into supplier performance, compliance, and operational readiness.

The Hackett Group’s annual “50 to Know” report reflects that shift. The consultancy evaluates vendors based on a data-driven framework covering technology capabilities, solution maturity, innovation, customer adoption, and market impact.

In its latest assessment, Hackett analysts noted that AI capabilities are now widely embedded across procurement platforms, raising the bar for differentiation. Vendors are increasingly judged not simply on their technical features, but on their ability to translate innovation into practical results for enterprises.

“Organizations evaluating procurement technology investments often use these lists as a starting point,” said Nikhil Gaur, Director of Strategic Projects and Research Analyst at The Hackett Group. According to Gaur, the growing density of the market means that standout vendors must demonstrate both innovation and tangible business impact.

Avetta’s Focus: “Intelligent Work Readiness”

Avetta’s platform centers on a concept it calls intelligent work readiness, which combines AI-driven insights with operational data to help organizations assess supplier risk and readiness across global supply chains.

The platform is designed to help procurement teams answer a critical question: Are suppliers truly ready to perform safely, securely, and compliantly before work begins?

To address that challenge, Avetta aggregates supplier information across safety, sustainability, regulatory compliance, and operational capabilities. The platform then uses AI-based insights to surface risks and readiness gaps across a company’s vendor network.

CEO Arshad Matin said the recognition reflects Avetta’s approach to helping organizations manage complex supplier ecosystems.

“The Hackett Group is a highly respected voice on global procurement strategies and enterprise operations,” Matin said. “This recognition reinforces our focus on helping organizations build supply chains that are truly ready to work—capable, trusted partners aligned on safety, security, and sustainability.”

Bridging the Global “Readiness Gap”

One of the central themes in Avetta’s positioning is the “global readiness gap.” The company argues that the pace of operational risk—from regulatory changes to safety incidents—often moves faster than organizations can respond.

Procurement teams, which historically focused on cost control and vendor sourcing, are increasingly responsible for managing:

• Worker safety and compliance

• Environmental and sustainability requirements

• Cybersecurity risks in vendor networks

• Operational reliability across distributed supply chains

Platforms like Avetta aim to close this gap by giving procurement teams continuous visibility into supplier readiness, allowing them to identify risks before they disrupt operations.

Benefits for Buyers and Suppliers

Avetta’s platform is structured to support both sides of the supply chain equation.

For enterprise hiring clients, the network provides a centralized environment to vet suppliers and contractors. The goal is to ensure work is performed safely and compliantly across complex projects and geographically dispersed operations.

For suppliers, the platform acts as a gateway into large enterprise ecosystems. Avetta provides training resources, compliance guidance, and AI-driven insights designed to help suppliers meet the requirements of major buyers.

In theory, the approach reduces friction in supplier onboarding while also improving safety and compliance outcomes.

AI’s Expanding Role in Procurement Platforms

The Hackett Group’s evaluation highlights a broader trend across enterprise software: AI is rapidly becoming a baseline capability rather than a differentiator.

Many procurement platforms now use machine learning to:

• Assess supplier risk

• Automate compliance checks

• Predict disruptions in supply chains

• Optimize sourcing decisions

The challenge for vendors is demonstrating that those AI capabilities translate into real-world benefits—something analysts increasingly emphasize when evaluating solutions.

According to Hackett’s analysts, the procurement technology market is becoming crowded, making it harder for vendors to stand out without clear proof of impact.

Why Recognition Lists Still Matter

Industry rankings and “top vendor” lists can sometimes feel like marketing exercises. But in procurement technology—where organizations must sift through hundreds of vendors—they often serve a practical purpose.

Enterprise buyers frequently use these reports as shortlists when evaluating new technology investments, particularly in complex areas like supplier risk management and compliance.

Being included in the “50 to Know” list therefore places Avetta among a group of vendors that analysts believe are worth watching as procurement technology continues to evolve.

The Bigger Picture: Procurement as a Strategic Function

The recognition also reflects a larger shift happening inside enterprises. Procurement, once viewed primarily as a back-office function, is increasingly seen as a strategic driver of resilience and operational continuity.

Supply chain disruptions, geopolitical tensions, and regulatory scrutiny have pushed procurement teams into a more prominent role.

Technology platforms that can deliver real-time supplier intelligence, automated compliance workflows, and scalable risk management are quickly becoming central to that transformation.

For vendors like Avetta, the opportunity lies in helping enterprises move from reactive risk management to proactive supplier readiness.

And as AI becomes more deeply embedded across procurement software, the companies that succeed will likely be those that can translate algorithms into operational confidence across global supply networks.

Get in touch with our MarTech Experts.

Manufacturers have spent the past few years grappling with supply chain shocks and workforce volatility. Now, a new threat is looming larger: the slow but steady loss of institutional knowledge.

That’s the focus of an upcoming March 3, 2026 webinar from Intellect, hosted by CEO Heather Preu and featuring analysts Allison Kuhn and James Wells from LNS Research. The session, titled Navigating the Chaos of Manufacturing in 2026, zeroes in on a challenge that’s quickly becoming existential for industrial and life sciences firms: how to preserve operational expertise before it walks out the door.

The webinar arrives on the heels of what many in the industry describe as one of the toughest recall years in recent memory. In 2025, manufacturers faced mounting product recalls, compliance scrutiny, and operational disruptions—often tied not to a lack of data, but to disconnected systems and fractured processes.

The Real Risk: Institutional Amnesia

As experienced operators, engineers, and quality leaders retire or transition roles, decades of tacit knowledge—how to troubleshoot a finicky line, how to interpret subtle quality deviations, how to navigate compliance gray areas—can vanish with them.

According to Intellect, this loss isn’t just a talent issue; it’s a structural risk to quality, compliance, and production continuity.

The March 3 discussion will examine how AI-driven platforms can capture frontline expertise and convert it into reusable, scalable operational intelligence. The premise: if knowledge can be codified, connected to execution systems, and embedded into workflows, it doesn’t disappear when a veteran worker does.

That’s a sharp departure from traditional manufacturing IT architectures, where Quality Management Systems (QMS), frontline worker tools, and operational data often sit in separate silos.

From Fragmented Systems to Unified Platforms

A central theme of the webinar is the convergence of QMS and Connected Frontline Worker technologies. Rather than treating compliance and execution as parallel tracks, modern manufacturers are increasingly seeking integrated systems that unify them.

Preu is expected to discuss how customer demand for this integration has shaped Intellect’s acquisition strategy—specifically its push to combine quality management and frontline execution into a single operational framework. The goal: link compliance data, production workflows, and institutional knowledge in one AI-enabled environment.

It’s a move that mirrors broader industry trends. As AI adoption accelerates in manufacturing, companies are moving beyond isolated predictive maintenance pilots and toward enterprise-wide operational intelligence. The focus is shifting from dashboards to decision-making—embedding insights directly into frontline workflows.

When quality events, deviations, and corrective actions are digitally connected to shop-floor execution, manufacturers gain more than traceability. They gain context.

Why This Matters Now

The timing of this conversation is no accident.

Life sciences and industrial manufacturers are under intensifying regulatory pressure, while operating with thinner margins and more complex global supply networks. Recalls don’t just hurt financially—they damage brand equity and invite long-term scrutiny.

At the same time, digital transformation initiatives are entering a new phase. Early adopters have already digitized documentation and basic workflows. The next frontier is intelligence: systems that don’t just record events, but actively guide decisions.

That’s where AI becomes less about hype and more about resilience.

If knowledge from seasoned operators can be embedded into digital workflows—automating best practices, flagging risks, standardizing responses—new hires can ramp faster, quality deviations can be caught earlier, and compliance gaps can be closed before they trigger audits or recalls.

In other words, AI shifts from experimental to operational.

Analyst Perspective: 2026 as an Inflection Point

Kuhn and Wells are expected to provide independent analysis on how workforce transformation, AI adoption, and digital platform consolidation are reshaping performance expectations across manufacturing and life sciences.

Industry analysts have increasingly framed 2026 as a pivotal year: demographic shifts are accelerating, regulatory environments are tightening, and boards are demanding measurable ROI from digital investments.

For vendors, that means platform narratives must translate into tangible outcomes—fewer recalls, faster onboarding, improved yield, and lower compliance risk.

For manufacturers, it means rethinking architecture. Point solutions may still solve local problems, but disconnected systems create blind spots. Unified platforms promise continuity—of data, of processes, and of expertise.

A Subtle Competitive Signal

While positioned as an educational event, the webinar also underscores Intellect’s broader market positioning: AI-native, unified, and purpose-built for regulated manufacturing.

The emphasis on integrating QMS and frontline execution places the company in direct conversation with larger enterprise software providers and niche connected worker vendors alike. The differentiator, according to Intellect, is natively linking quality data with real-time execution in one system rather than stitching tools together post-deployment.

Whether that approach becomes the dominant model remains to be seen. But as recalls mount and workforce churn persists, manufacturers are clearly reassessing legacy architectures.

What Attendees Can Expect

The on-demand webinar will explore:

• Strategies to capture and preserve institutional expertise

• How AI can convert operational data into actionable intelligence

• Approaches to reducing recall risk through unified platforms

• The evolving role of QMS in a connected workforce environment

Manufacturing, quality, and operations leaders navigating digital transformation initiatives in 2026 may find the discussion particularly timely.

Because in today’s environment, the real chaos isn’t just external volatility—it’s what happens when critical knowledge is fragmented, disconnected, or gone entirely.

Get in touch with our MarTech Experts.