Machine builders are rethinking how industrial equipment delivers value long after deployment. That shift is the focus of the latest season of ROKStudios, a thought leadership video series from Rockwell Automation, which brings together executives from leading OEMs and manufacturing associations to discuss how digital technologies, cybersecurity, data connectivity, and lifecycle services are transforming industrial machinery. The discussions highlight a growing industry move away from project-based equipment delivery toward lifecycle-driven business models centered on long-term performance, resilience, and operational efficiency.

Industrial manufacturers are facing a new reality. Customers no longer evaluate machinery solely on purchase price or production speed. Instead, they increasingly expect equipment to deliver measurable value throughout its operational life, including improved uptime, predictive maintenance, cybersecurity protection, sustainability performance, and digital connectivity.

That evolving expectation sits at the center of Rockwell Automation's latest season of ROKStudios, a video interview series featuring executives from machine builders, packaging equipment providers, and industry organizations across Europe and global manufacturing markets.

The new season explores how original equipment manufacturers (OEMs) are adapting their business strategies to support the full machine lifecycle—from design and engineering to commissioning, operation, maintenance, and modernization.

The conversations arrive at a pivotal moment for industrial automation. According to IDC, global spending on digital transformation technologies continues to grow as manufacturers invest in connected operations, intelligent automation, and data-driven decision-making. Meanwhile, Gartner research suggests industrial organizations are increasingly prioritizing operational resilience and asset optimization as supply chains become more complex and production requirements continue to evolve.

Rockwell Automation's latest discussions reflect these broader industry priorities.

A recurring theme across the interviews is the growing importance of cybersecurity as industrial systems become more connected. Olaf Clemens, CEO of SN Maschinenbau, discusses how cybersecurity has evolved from an IT requirement into a core component of machine design. As manufacturers deploy connected machinery capable of exchanging operational data across facilities and cloud environments, secure infrastructure is becoming essential for maintaining uptime and protecting production systems.

Another major trend highlighted throughout the series is the expansion of digital services. Gian Paolo Crasta, Director General of UCIMA, points to increasing demand for packaging equipment capable of delivering flexibility, sustainability, and measurable lifecycle performance. Manufacturers are seeking machines that can adapt to changing product requirements while generating operational data that supports continuous optimization.

Robotics and standardized automation architectures are also playing a larger role in lifecycle management strategies. Alessandro Rocca, Vice President of Global Sales at Cama Group, explains how modular systems and standardized machine designs can accelerate deployment, improve repeatability, and simplify long-term maintenance in complex production environments.

The growing adoption of digital twins receives significant attention as well. Once primarily used for virtual commissioning and design validation, digital twin technology is increasingly being viewed as a lifecycle asset. Bino Bastian of ECONO-PAK describes how virtual machine models are helping manufacturers improve engineering collaboration, support operational optimization, and address evolving compliance and traceability requirements long after equipment installation.

This evolution aligns with broader Industry 4.0 initiatives across the manufacturing sector. Digital twins, industrial IoT platforms, and cloud-connected analytics are enabling organizations to create continuous feedback loops between machine performance and operational decision-making.

Several participants also emphasize the role of data in supporting new service-based business models. Piers Lamb of Universal Pack highlights how data-ready machine architectures can accelerate commissioning while enabling advanced traceability, compliance reporting, and long-term customer support programs.

Sustainability is another area reshaping machine design priorities. Michael Lampe of Meurer Verpackungssysteme discusses how manufacturers are adapting equipment to support emerging packaging materials and sustainability goals without compromising efficiency or production flexibility.

The challenge is particularly relevant for packaging manufacturers navigating increasing regulatory requirements and consumer demand for environmentally responsible products. OEMs are being asked to balance sustainability objectives with productivity expectations, often requiring new machine architectures and enhanced digital capabilities.

Steve Rackham of Bradman Lake Group notes that modular machine designs are becoming increasingly important as manufacturers face growing SKU complexity. Flexible systems that can accommodate frequent product changes while maintaining uptime are becoming critical competitive differentiators.

Industry associations are also recognizing these shifts. Luis Villegas of AMEC Envasgraf points to digitalization, workforce challenges, and sustainability pressures as major factors driving lifecycle-focused thinking across the manufacturing sector.

Across all interviews, a clear pattern emerges. Machine builders are moving beyond the traditional approach of delivering equipment and concluding engagement after installation. Instead, they are positioning themselves as long-term technology partners capable of supporting performance optimization throughout the operational life of industrial assets.

This transformation reflects a broader change occurring across industrial automation markets. As connected technologies become standard and operational data grows in strategic importance, value creation increasingly depends on what happens after deployment rather than at the point of sale.

For manufacturers investing in automation infrastructure, the implications are significant. Decisions made during machine design—including cybersecurity architecture, connectivity standards, modularity, and service readiness—can directly influence maintenance costs, production efficiency, scalability, and future upgrade opportunities.

Rockwell Automation's latest ROKStudios season offers a window into how OEM leaders are preparing for that future. The message is consistent: the machine lifecycle is becoming the new battleground for industrial innovation, customer value, and competitive differentiation.

Market Landscape

The global industrial automation market is undergoing rapid transformation as manufacturers adopt Industry 4.0 technologies, digital twins, AI-driven analytics, and connected operations platforms. According to IDC and Gartner, industrial organizations are increasingly investing in lifecycle management solutions that improve asset utilization, reduce downtime, and support sustainability initiatives. OEMs are responding by integrating cybersecurity, predictive maintenance, cloud connectivity, and service-based business models directly into machine design. As industrial digital transformation accelerates, lifecycle value is becoming a key purchasing criterion for manufacturing customers worldwide.

Top Insights

• Rockwell Automation's latest ROKStudios season examines how OEMs are shifting from equipment delivery models toward lifecycle-focused manufacturing strategies.
• Industry leaders highlight cybersecurity, digital twins, data connectivity, and modular design as critical enablers of long-term machine performance.
• OEMs are increasingly using connected technologies to improve commissioning efficiency, operational resilience, and predictive maintenance capabilities.
• Sustainability requirements and changing packaging materials are driving innovation in machine architectures across manufacturing sectors.
• Lifecycle services and data-driven support models are emerging as major competitive differentiators for industrial automation providers.

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FlackTek, a provider of high-performance mixing and material processing technologies, has appointed industry veteran Dustin Becker as Director of Sales & Marketing as the company looks to expand its presence across advanced manufacturing sectors. The move comes as manufacturers in aerospace, electronics, energy storage, and industrial materials increasingly invest in precision processing technologies to support next-generation product development and scalable production.

As advanced manufacturing industries continue to prioritize automation, precision engineering, and materials innovation, suppliers of specialized production technologies are expanding leadership teams to capitalize on growing demand. FlackTek's appointment of Dustin Becker as Director of Sales & Marketing signals the company's intention to strengthen its position in high-growth industrial sectors where material consistency and process reliability are becoming increasingly important.

The Colorado-based company develops high-performance mixing and material processing systems used across aerospace, defense, electronics, energy, medical technology, and industrial manufacturing applications. Its equipment is designed to help engineers, researchers, and production teams improve material uniformity, reduce waste, and accelerate product development cycles.

Becker brings nearly two decades of commercial leadership experience spanning several advanced manufacturing industries. His background includes sales operations, strategic account management, business development, and market expansion initiatives across North America.

In his new position, Becker will lead FlackTek's global sales and marketing strategy, overseeing efforts to expand market reach, deepen customer relationships, and identify new opportunities in sectors that increasingly rely on precision material processing technologies.

The appointment reflects broader industry trends shaping modern manufacturing. As product designs become more complex and material formulations more sophisticated, manufacturers are seeking greater control over production processes. This is particularly evident in industries such as aerospace, semiconductor packaging, battery development, medical devices, and specialty chemicals, where material performance directly impacts product quality and regulatory compliance.

Prior to joining FlackTek, Becker held several leadership roles at Krayden, a distributor of specialty materials and engineered products serving industrial and technology-driven markets. Most recently, he managed North American sales operations and strategic growth programs for a business unit generating approximately $45 million in annual revenue. Earlier roles involved leading sales organizations supporting up to $190 million in annual business across the United States, Canada, and Mexico.

His experience extends beyond sales management into consultative customer engagement, a capability that has become increasingly valuable as manufacturing technology suppliers move toward solution-based selling models. Industrial buyers today are less focused on individual products and more interested in integrated solutions that improve productivity, reduce operational risks, and accelerate innovation.

According to Matt Gross, General Manager at FlackTek, Becker's experience aligns closely with the company's long-term strategy of expanding within technically demanding industries. Aerospace, electronics, adhesives, sealants, and advanced industrial applications remain key growth markets where precision processing capabilities can influence both product performance and manufacturing efficiency.

The appointment also comes at a time when global manufacturers are accelerating investments in research and development. According to IDC, worldwide spending on digital transformation and advanced industrial technologies continues to rise as organizations modernize production environments and strengthen supply chain resilience. At the same time, McKinsey research has highlighted growing adoption of advanced manufacturing technologies designed to improve operational efficiency, quality control, and product innovation.

Material processing technologies are becoming increasingly important within this transformation. Industries developing advanced batteries, semiconductor materials, aerospace composites, specialty coatings, and medical-grade compounds require highly repeatable mixing processes capable of delivering consistent results at both research and production scales.

FlackTek's technology portfolio serves many of these emerging applications. The company's systems are widely used in laboratory environments, product development programs, and manufacturing facilities where precise material preparation can influence product reliability, safety, and performance outcomes.

Becker's previous leadership experience with organizations including 3M, Scott Safety, Jadak, and Universal Packaging Solutions further broadens his understanding of industrial supply chains and customer requirements across diverse manufacturing sectors. Throughout his career, he has focused on aligning commercial strategies with technical problem-solving, an approach increasingly favored by industrial technology providers seeking long-term customer relationships.

The hiring also reflects a larger trend among industrial technology companies investing in commercial leadership to support international growth. As manufacturing ecosystems become more interconnected and innovation cycles accelerate, suppliers are competing not only on technology performance but also on technical support, application expertise, and strategic customer collaboration.

Looking ahead, sectors such as battery manufacturing, advanced electronics, aerospace engineering, and energy storage are expected to remain major growth opportunities for material processing technology providers. The increasing complexity of materials used in these industries is creating demand for equipment capable of delivering greater precision, repeatability, and scalability.

For FlackTek, Becker's appointment represents more than a leadership change. It signals a continued push into advanced manufacturing markets where innovation, process control, and material performance are becoming central competitive differentiators. As manufacturers seek technologies that support faster development cycles and higher-quality production outcomes, companies supplying specialized processing solutions are positioning themselves to play a larger role in the next phase of industrial innovation.

Market Landscape

The global advanced manufacturing sector is undergoing significant transformation driven by automation, Industry 4.0 initiatives, advanced materials research, and electrification programs. According to McKinsey and IDC, manufacturers are increasing investments in precision production technologies, digital engineering platforms, and material science innovations to improve competitiveness. Industries including aerospace, semiconductor manufacturing, battery production, medical devices, and specialty chemicals are creating new opportunities for suppliers of material processing and industrial automation solutions. Companies capable of delivering consistent, scalable, and high-performance manufacturing technologies are expected to benefit from long-term industry modernization trends.

Top Insights

• FlackTek has appointed Dustin Becker as Director of Sales & Marketing to support expansion across advanced manufacturing, aerospace, electronics, and industrial technology markets.
• Becker brings nearly 20 years of commercial leadership experience managing strategic growth initiatives and large sales organizations throughout North America.
• The company is targeting sectors where precision material processing directly impacts product quality, performance, and manufacturing efficiency.
• Growing investment in battery technology, advanced materials, and semiconductor manufacturing is increasing demand for specialized mixing and processing systems.
• The appointment reflects broader industrial trends toward solution-based selling, customer collaboration, and innovation-driven manufacturing growth.

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Artificial intelligence has become a core investment priority for enterprise marketing teams, but proving its business value remains a significant challenge. A new global survey from Comviva reveals that while 90% of organizations have increased AI marketing investments over the past two years, only 12% can demonstrate measurable business outcomes. The findings highlight a growing accountability gap as marketing leaders face mounting pressure from executives to justify AI spending with clear revenue and performance metrics.

As AI adoption accelerates across marketing organizations, the conversation is shifting from experimentation to accountability. Enterprises have invested heavily in AI-powered marketing automation, predictive analytics, customer segmentation, personalization engines, and campaign optimization tools. Yet many organizations remain unable to quantify whether those investments are generating meaningful returns.

According to Comviva's latest Global CMO Survey Report, titled The AI Efficiency Divide: Measuring AI's Real Value Beyond the Hype, most organizations continue to struggle with AI measurement maturity despite widespread deployment across marketing operations.

The report paints a picture of an industry that has embraced AI technology faster than it has developed the frameworks needed to evaluate success. Only 16% of marketing leaders say they are confident in defending AI investments using concrete business evidence. Meanwhile, 79% rely on estimated calculations rather than precise measurement methodologies, and 67% cannot accurately determine the total cost of their AI initiatives.

For chief marketing officers, this challenge is becoming increasingly urgent. The report found that 86% of executive leadership teams now demand stronger proof of AI-generated return on investment, creating pressure on marketing departments to connect AI-driven activities directly to business outcomes such as revenue growth, customer acquisition efficiency, and customer lifetime value.

The findings reflect a broader shift occurring across the marketing technology landscape. Enterprise organizations have rapidly integrated AI into customer engagement strategies, often leveraging platforms from industry leaders such as Salesforce, Adobe, Microsoft, and Google. However, the ability to attribute revenue impact across increasingly complex customer journeys remains a persistent challenge.

One of the report's most notable findings is the lack of standardized measurement infrastructure. While 35% of organizations rely on rough estimates to evaluate AI performance, 32% track campaign-level activity without connecting those efforts to revenue outcomes. Another 21% lack consistent measurement systems entirely.

This measurement gap is becoming particularly problematic as AI tools become embedded across multiple marketing functions. AI-generated insights may influence customer targeting, content personalization, media buying decisions, and conversion optimization simultaneously, making attribution significantly more complex than traditional marketing measurement models.

Comviva's research identifies cost fragmentation as the largest barrier to accurate AI measurement. Sixty-two percent of respondents reported difficulty tracking AI expenditures because costs are distributed across cloud infrastructure, software subscriptions, third-party vendors, data management systems, and internal talent resources.

Revenue attribution presents another major obstacle. Fifty-eight percent of organizations say AI influences too many customer touchpoints to accurately isolate its contribution to business performance. Similarly, 55% struggle to connect customer experience improvements with financial outcomes, while half of respondents cite governance and integration challenges that limit consistent performance tracking.

Despite these concerns, the report highlights several areas where AI investments are delivering measurable value. Customer segmentation and audience targeting emerged as the strongest-performing use case, cited by 57% of respondents. Campaign automation and optimization followed at 43%, while predictive personalization and recommendation engines were identified by 41% of marketing leaders as effective drivers of customer engagement.

Other high-performing applications include pricing and offer optimization, cited by 39% of respondents, and demand forecasting at 36%. These use cases share a common characteristic: they are closely linked to revenue generation and operational decision-making rather than experimental or standalone AI deployments.

The findings align with broader industry research. Gartner has projected that organizations increasingly expect AI initiatives to demonstrate measurable business outcomes rather than operational novelty. Similarly, McKinsey research has consistently shown that companies achieving the highest returns from AI investments are those that embed AI into core business processes and establish clear performance metrics from the outset.

Another important takeaway from the survey involves hidden costs. While many organizations account for software licensing, API consumption, and cloud infrastructure expenses, talent acquisition, governance requirements, integration efforts, and ongoing optimization costs are frequently overlooked.

According to the report, these untracked expenses may result in organizations underestimating total AI investment costs by as much as 30% to 50%. Such blind spots can artificially inflate perceived ROI and create inaccurate assumptions about future investment decisions.

The report also identifies operational execution as a critical success factor. More than half of organizations struggle to define deployment timelines and measure time-to-value. Meanwhile, concerns around explainability, trust, and governance continue to hinder broader AI adoption.

Rajesh Chandiramani, Chief Executive Officer at Comviva, argues that the next phase of enterprise AI adoption will be defined by accountability rather than experimentation. Organizations that successfully connect AI initiatives to measurable business outcomes will likely gain a competitive advantage as digital transformation strategies mature.

For enterprise marketing teams, the message is clear. AI implementation alone is no longer sufficient. The organizations that will realize sustainable value are those that establish robust measurement frameworks, improve cost visibility, strengthen governance structures, and align AI initiatives directly with revenue-driving business objectives.

As marketing leaders prepare for increasing scrutiny over technology spending, AI success may ultimately depend less on the sophistication of algorithms and more on an organization's ability to measure what those algorithms actually deliver.

Market Landscape

The findings arrive at a critical moment for the global MarTech industry. According to Gartner, worldwide spending on marketing technology continues to rise as enterprises prioritize automation, customer intelligence, and AI-driven decision-making. Meanwhile, IDC forecasts sustained growth in enterprise AI software investments as organizations seek competitive advantages through predictive analytics and personalization.

However, the Comviva report highlights a growing industry reality: AI adoption is outpacing AI accountability. As enterprise organizations move beyond pilot programs, vendors and marketing leaders alike will face increasing pressure to demonstrate measurable business outcomes, not simply technology deployment. This trend is expected to influence future investments in customer data platforms, marketing analytics solutions, attribution technologies, and AI governance frameworks.

Top Insights

• Comviva's survey found that 90% of organizations increased AI marketing investments, yet only 12% can demonstrate measurable business impact and ROI.
• Executive accountability is rising, with 86% of leadership teams demanding stronger evidence that AI initiatives contribute directly to revenue and growth.
• Customer segmentation, predictive personalization, and campaign automation emerged as the AI use cases delivering the strongest measurable returns.
• Cost fragmentation across cloud infrastructure, software, vendors, and talent remains the largest obstacle to accurate AI performance measurement.
• Organizations underestimate AI investment costs by up to 50%, potentially distorting ROI calculations and future technology investment decisions.

Get in touch with our MarTech Experts

As television advertising increasingly spans linear TV, streaming platforms, connected TV (CTV), and ad-supported video services, marketers face a growing challenge: turning measurement data into actionable optimization decisions quickly enough to improve campaign performance. EDO is seeking to address that gap with the launch of Ad EnGage Optimize, a new AI-powered platform designed to automate campaign optimization across audience targeting, frequency management, creative rotation, and media planning in real time.

The television advertising industry has spent the past decade building increasingly sophisticated measurement systems. Marketers today can access more data than ever about audience behavior, campaign effectiveness, and media performance.

Yet despite those advances, one challenge has persisted: converting insights into action.

EDO, a company specializing in TV outcomes measurement and advertising intelligence, believes the next phase of television advertising will be defined not by measurement itself but by optimization. The company’s newly launched Ad EnGage Optimize platform aims to automate that process, enabling brands and agencies to continuously improve campaign performance while campaigns are still running.

The launch arrives as convergent TV advertising—a term increasingly used to describe campaigns running across linear television, streaming services, connected TV, and digital video—becomes the dominant buying model for major advertisers.

While marketers have embraced cross-platform advertising to reach fragmented audiences, managing those campaigns has become significantly more complex. Media teams must evaluate performance across multiple publishers, audience segments, geographic markets, creative variations, and frequency levels, often simultaneously.

According to EDO's research, that complexity is creating substantial inefficiencies.

The company found that some convergent TV campaigns could redirect more than 35% of impressions toward higher-performing inventory through improved frequency management alone. It also reported that optimizing creative rotation strategies across streaming and linear environments can increase campaign effectiveness by approximately 20%.

For large advertisers managing multimillion-dollar media budgets, those gains can translate into significant financial impact.

The new platform is built on EDO’s proprietary TV outcomes dataset, which connects television exposures with consumer behavioral signals that predict business outcomes. The company has spent more than a decade developing measurement capabilities used by television networks, streaming services, media agencies, and brand advertisers to evaluate advertising effectiveness.

Rather than simply reporting campaign performance, Ad EnGage Optimize applies artificial intelligence to recommend and automate decisions across multiple campaign variables simultaneously.

This represents a notable shift from traditional optimization approaches.

Historically, advertising teams often adjusted one variable at a time, such as audience targeting, creative performance, or frequency caps. EDO's platform is designed to evaluate combinations of factors together, including audience characteristics, geographic regions, media placements, creative assets, and exposure frequency.

The goal is to identify performance opportunities that may not be visible through isolated analysis.

Among the platform's key capabilities are automated frequency optimization, media plan adjustments, creative rotation management, and audience targeting refinement. The company has also introduced agentic AI integration through a Model Context Protocol (MCP) layer, allowing organizations to connect optimization workflows directly into broader AI-driven marketing operations.

The announcement reflects larger shifts occurring throughout the advertising technology industry.

As third-party identifiers become less reliable and privacy regulations continue evolving, marketers are increasingly relying on first-party data, predictive analytics, and outcome-based measurement frameworks. At the same time, advances in artificial intelligence are creating opportunities to automate tasks that previously required extensive manual analysis.

Industry analysts have long argued that optimization remains one of the most underdeveloped areas of television advertising.

While measurement tools can identify opportunities for improvement, executing those changes across dozens of publishers, hundreds of markets, and multiple campaigns often exceeds the capacity of human teams. This challenge becomes even more pronounced as advertisers allocate spending across an expanding mix of streaming and traditional television environments.

The growing popularity of connected TV advertising has intensified this issue.

According to industry forecasts from Statista and Insider Intelligence, CTV advertising spending continues to grow as audiences shift toward streaming platforms. Advertisers are increasingly seeking solutions that can unify campaign management and performance optimization across fragmented media ecosystems.

EDO’s strategy positions optimization as the logical next layer of advertising intelligence.

The company argues that the industry has already invested heavily in attribution, measurement, and performance analytics. The remaining opportunity lies in applying those insights automatically and at scale.

The launch also aligns with broader enterprise AI trends.

Across industries, organizations are moving beyond AI-powered reporting and toward systems capable of making recommendations, coordinating workflows, and supporting decision-making. In marketing and advertising, these technologies are increasingly being described as "agentic" systems—AI applications that not only analyze information but also help execute actions.

For media agencies and brand marketers facing increasing pressure to demonstrate return on advertising spend, the ability to continuously optimize campaigns while they are active may become a significant competitive advantage.

As convergent TV advertising matures, measurement alone is unlikely to differentiate platforms. The companies that succeed may be those capable of transforming performance data into actionable decisions faster than competitors.

With Ad EnGage Optimize, EDO is making the case that television advertising has entered that next phase.

Market Landscape

The convergent TV and connected TV advertising market is evolving rapidly as brands shift budgets toward cross-platform video campaigns. Research from Gartner and Statista indicates that marketers are increasingly prioritizing outcome-based measurement, AI-powered media planning, and first-party data strategies as traditional audience measurement models become less effective.

Major industry players including Google, Amazon, Disney, Netflix, Roku, and The Trade Desk are investing heavily in advertising infrastructure that supports audience targeting, attribution, and optimization across streaming and television environments.

As AI adoption accelerates, industry focus is increasingly shifting from campaign measurement to autonomous optimization, creating a new competitive battleground within the advertising technology ecosystem.

Top Insights

• EDO launched Ad EnGage Optimize, an AI-powered platform that automates TV advertising optimization across frequency, creative rotation, audience targeting, and media planning.
• The company found that some convergent TV campaigns can reallocate more than 35% of impressions to higher-performing inventory through smarter frequency management.
• Creative rotation optimization across streaming and linear TV environments can improve campaign performance by approximately 20%, according to EDO research.
• The platform uses EDO’s outcomes measurement data to continuously identify and recommend performance improvements while campaigns remain active.
• Agentic AI integrations allow brands and agencies to connect optimization workflows directly into broader AI-powered marketing operations.

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Pipedrive is expanding its artificial intelligence strategy through a new integration with OpenAI’s Codex platform, allowing sales teams to connect CRM data directly to AI-powered workflows. The partnership reflects a broader trend across the CRM and sales technology market, where software vendors are embedding business context into AI tools to help organizations streamline research, preparation, reporting, and customer engagement activities.

The race to integrate artificial intelligence into enterprise software is increasingly moving beyond standalone AI features and toward deeper workflow integration.

Pipedrive, the customer relationship management (CRM) platform focused on small and medium-sized businesses, announced it is among the launch partners for OpenAI’s new sales-focused plugin for Codex. The integration enables users to access and leverage Pipedrive CRM data within AI-powered sales workflows, bringing customer information, deal context, and sales insights directly into the environments where teams conduct analysis and planning.

The announcement highlights how AI is evolving from an experimental productivity tool into a core layer within business applications.

OpenAI’s Codex platform is introducing role-specific plugins designed to connect enterprise applications with AI-driven workflows. The new sales category aims to help teams incorporate trusted business information into activities such as account analysis, meeting preparation, reporting, opportunity assessment, and workflow automation.

For sales organizations, one of the biggest challenges has been the fragmentation of information across systems.

Customer records, communications, pipeline data, forecasts, and account history often reside in separate platforms, forcing sales professionals to spend valuable time gathering information before engaging with prospects or customers. AI integrations are increasingly being positioned as a solution to this challenge by surfacing relevant context at the moment it is needed.

Through the new integration, Pipedrive customers can connect CRM data directly into OpenAI-powered workflows, reducing the need to manually search across applications and potentially accelerating sales decision-making.

The move comes as CRM providers face growing pressure to demonstrate practical business value from AI investments.

Over the past two years, virtually every major CRM vendor has launched AI initiatives. Companies including Salesforce, Microsoft, HubSpot, Zoho, and Oracle have introduced AI-powered assistants, predictive analytics capabilities, automated workflow tools, and conversational interfaces designed to improve productivity and customer engagement.

The next phase of competition appears to be centered on accessibility.

Rather than requiring users to operate within a specific CRM interface, vendors are increasingly enabling their data to flow into external AI ecosystems where professionals already work. This reflects a broader shift toward AI becoming an operational layer that spans multiple business applications rather than remaining confined within individual software platforms.

For Pipedrive, the integration aligns with its strategy of delivering practical AI capabilities for smaller organizations that may not have dedicated technical resources or enterprise-scale technology teams.

Small and midsize businesses are often looking for AI tools that reduce administrative overhead and simplify everyday workflows rather than introducing complex implementation projects. Access to CRM insights within AI-powered environments could help sales teams prepare for meetings, review account activity, prioritize opportunities, and generate reports more efficiently.

The announcement also underscores the growing importance of business context in generative AI applications.

Large language models are powerful at generating responses and analyzing information, but their value increases significantly when connected to structured organizational data. CRM platforms contain some of the most important customer intelligence within an enterprise, including sales history, engagement patterns, revenue opportunities, and relationship data.

By connecting those datasets to AI systems, organizations can potentially improve the relevance and usefulness of AI-generated outputs.

Industry analysts view these integrations as a natural progression of enterprise AI adoption.

According to Gartner, organizations are increasingly moving from AI experimentation toward workflow-centric implementations that embed intelligence directly into day-to-day operations. IDC has similarly highlighted the growing role of AI copilots and business assistants that leverage enterprise data to support decision-making across departments.

Sales teams are among the most active adopters of these technologies.

Research from multiple industry firms suggests sales professionals spend a significant portion of their time on administrative activities, data entry, and information gathering rather than direct customer engagement. AI-powered workflow integrations are designed to reduce that burden, allowing teams to focus on revenue-generating interactions.

For OpenAI, the introduction of role-specific plugins signals a broader effort to position Codex as a platform for enterprise productivity rather than solely a development-focused tool. By supporting functions such as sales, the company is expanding AI’s reach into business processes that rely heavily on organizational knowledge and contextual information.

As AI becomes increasingly integrated into CRM platforms and sales technology stacks, the differentiator may no longer be whether a vendor offers AI capabilities, but how effectively those capabilities connect data, workflows, and decision-making.

Pipedrive’s participation in the launch of OpenAI’s sales plugin reflects this emerging reality. The future of sales technology is likely to be defined less by standalone AI features and more by how seamlessly business intelligence can be delivered within the flow of work.

Market Landscape

The CRM and sales technology market is undergoing rapid transformation as artificial intelligence becomes embedded across customer acquisition, pipeline management, forecasting, and sales enablement functions.

Major vendors including Salesforce, Microsoft, HubSpot, Oracle, and Pipedrive are investing heavily in AI copilots, workflow automation, predictive analytics, and conversational interfaces.

According to Gartner, AI-powered sales technologies are expected to play an increasingly central role in customer relationship management strategies. IDC also forecasts continued growth in AI-enabled business applications as organizations seek to improve productivity and streamline knowledge work.

The emergence of AI platforms that connect directly to enterprise data sources represents a significant evolution in how CRM systems deliver value, enabling organizations to access customer intelligence across a growing ecosystem of AI-powered tools.

Top Insights

•  Pipedrive joined the launch of OpenAI’s sales-focused Codex plugin, enabling CRM data access within AI-powered workflows.
• The integration helps sales teams use customer and pipeline information for meeting preparation, analysis, reporting, and decision-making.
• CRM vendors are increasingly connecting enterprise data to external AI ecosystems rather than limiting AI functionality to native applications.
• AI-assisted sales workflows aim to reduce administrative work and increase time spent on customer-facing activities.
• The partnership reflects a broader industry shift toward workflow-centric AI that combines organizational context with generative AI capabilities.

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Morningstar Credit Analytics is expanding its artificial intelligence strategy with a new integration that allows licensed users to access commercial real estate (CRE) and commercial mortgage-backed securities (CMBS) data directly within Anthropic’s Claude. The move reflects a broader shift across financial services, where institutional data providers are increasingly embedding proprietary intelligence into AI-powered research workflows while maintaining governance, compliance, and access controls.

Artificial intelligence is rapidly changing how financial professionals interact with research, market intelligence, and investment data. While much of the industry’s attention has focused on generative AI’s ability to summarize information and accelerate analysis, a more significant transformation is underway: the integration of proprietary financial datasets directly into AI workflows.

Morningstar Credit Analytics (MCA), a subsidiary of Morningstar, has become the latest financial intelligence provider to embrace that shift with a new integration connecting its commercial real estate and commercial mortgage-backed securities datasets to Anthropic’s Claude platform.

The integration allows licensed users to query live CRE and CMBS information using natural language prompts, eliminating the need to navigate multiple systems, dashboards, or reporting interfaces when conducting credit analysis.

The technology is built on the Model Context Protocol (MCP), an emerging standard designed to connect AI models with external data sources while preserving security, governance, and user permissions.

For institutional investors, lenders, asset managers, and credit analysts, the development represents an important step in the evolution of AI-assisted financial research.

Traditionally, analysts reviewing commercial real estate credit performance have relied on specialized platforms to access loan-level performance metrics, surveillance reports, delinquency data, and securitization structures. While those platforms provide deep analytical capabilities, accessing information often requires navigating multiple interfaces and manually extracting data for further analysis.

Morningstar’s integration aims to bring that intelligence directly into the AI environment where many professionals increasingly conduct research and analysis.

Through Claude, licensed users can ask questions about loan performance, watchlist activity, special servicing events, deal structures, and tranche-level metrics using conversational language. The system retrieves information directly from Morningstar Credit Analytics' datasets while maintaining existing entitlement controls.

This governance-first approach addresses one of the most significant concerns surrounding AI adoption in financial services.

Regulated institutions face strict requirements around data access, auditability, compliance, and information security. While generative AI platforms have demonstrated productivity benefits, many organizations remain cautious about exposing sensitive proprietary data to open AI environments.

The MCP architecture is designed to mitigate those concerns by ensuring users can only access information already covered under their existing licenses and permissions. Rather than creating a separate AI dataset, Morningstar is effectively extending governed access into AI-powered workflows.

The launch aligns with broader trends across the financial technology sector.

Major financial data providers, investment research firms, and market intelligence platforms are increasingly racing to integrate with large language models and AI assistants. Organizations are recognizing that AI interfaces may become a primary access point for institutional information, much like search engines and dashboards defined previous generations of enterprise software.

Morningstar has been particularly active in this area.

The company and its affiliate PitchBook have introduced integrations across several leading AI ecosystems, including platforms from OpenAI, Anthropic, Microsoft, and Perplexity. The strategy reflects a growing belief that financial intelligence providers must make their datasets available wherever analysts choose to work rather than requiring users to remain within proprietary applications.

For commercial real estate professionals, the timing is particularly relevant.

The CRE sector continues to face heightened scrutiny amid changing interest rate environments, refinancing pressures, office market uncertainty, and evolving credit conditions. Access to timely loan surveillance and structured credit intelligence has become increasingly important for risk management and investment decision-making.

Morningstar’s CRE Analytics platform covers multiple CMBS structures, including conduit transactions, single-asset single-borrower (SASB) deals, CRE collateralized loan obligations (CRE CLOs), and agency-backed securities. Bringing that information into an AI-powered environment could significantly streamline surveillance and portfolio monitoring activities.

Industry analysts increasingly view these integrations as part of a larger shift toward AI-native financial workflows.

According to Gartner, generative AI is expected to transform knowledge-intensive professions by enabling direct interaction with structured enterprise data through conversational interfaces. IDC has similarly highlighted the growing role of AI-powered research environments in financial services, particularly as institutions seek to improve productivity while maintaining regulatory oversight.

The key differentiator for financial organizations will be trust.

Unlike consumer AI applications, financial institutions require transparency, explainability, and governed access to data. Integrations that combine AI efficiency with institutional-grade controls are likely to gain traction as firms move from experimentation to production deployment.

Morningstar's latest integration demonstrates how that balance is beginning to take shape. Rather than replacing existing analytical frameworks, AI is increasingly serving as a new interface layer that helps professionals access trusted information more efficiently.

As AI platforms become central to financial research, the competitive advantage may no longer depend solely on who owns the best data, but on who can deliver that intelligence seamlessly into the workflows where decisions are actually made.

Market Landscape

The financial data and analytics market is entering a new phase as artificial intelligence becomes embedded within institutional research workflows. Major providers including Morningstar, Bloomberg, FactSet, S&P Global, Moody’s, and PitchBook are increasingly exploring AI integrations that enable users to interact with proprietary datasets through natural language interfaces.

According to Gartner, enterprise adoption of generative AI is accelerating across financial services, while IDC projects continued investment in AI-powered research, analytics, and decision-support platforms. At the same time, regulatory requirements around transparency, governance, and data security remain central considerations for financial institutions.

This dynamic is driving demand for AI-enabled platforms that combine productivity gains with strict access controls, making governed AI workflows a growing area of innovation across investment management, banking, and commercial real estate finance.

Top Insights

•  Morningstar Credit Analytics launched an MCP-based integration that enables licensed users to access CRE and CMBS data directly within Claude.
• The integration allows analysts to query loan-level, deal-level, and surveillance information using natural language prompts.
• Governance and entitlement controls remain intact, addressing regulatory and compliance concerns common in financial services.
• The launch reflects a broader trend toward embedding proprietary institutional data within AI-powered research environments.
• Commercial real estate professionals can conduct surveillance, risk analysis, and credit monitoring without leaving their existing AI workflows.

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Telecommunications provider One NZ has dramatically reduced enterprise mobile provisioning times from up to ten days to less than ten minutes by deploying UiPath Maestro, an AI-powered orchestration platform designed to connect automation, AI agents, and human workflows across complex enterprise environments. The implementation highlights a growing trend among large enterprises: using orchestration layers to modernize operations without replacing legacy infrastructure.

As enterprises accelerate artificial intelligence adoption, many organizations face a common challenge: how to modernize business processes built on decades of disconnected systems without undertaking costly and disruptive technology overhauls.

One NZ's latest automation initiative offers a glimpse into how that challenge is increasingly being addressed.

The telecommunications provider announced that it has significantly transformed its enterprise mobile provisioning operations using UiPath Maestro, a cloud-native orchestration platform that coordinates AI agents, software robots, and human interactions across multiple systems. The deployment reduced provisioning times from as long as ten days to under ten minutes, creating a new benchmark for operational automation within the Australia and New Zealand telecommunications market.

The project is notable not only for the scale of performance improvement but also for the implementation timeline. According to the companies, the solution was deployed in just five weeks, demonstrating how orchestration technologies are increasingly being used to accelerate digital transformation initiatives without extensive infrastructure replacement.

Enterprise mobile provisioning has traditionally been a complex process for telecommunications providers. Customer orders often move across multiple systems, departments, and workflows before activation can occur. In One NZ's case, the process relied on integrations between Salesforce, Oracle, and internally developed platforms, with manual intervention and fragmented workflows contributing to lengthy delays.

Operational bottlenecks were compounded by limited visibility into order progress and dependencies across systems, creating challenges for customer service teams and operational staff alike.

Rather than replacing those systems, One NZ adopted a different strategy.

The company implemented UiPath Maestro as an orchestration layer sitting above existing infrastructure. AI agents coordinate decision-making and workflow management, while robotic process automation (RPA) bots execute actions across enterprise applications. Human oversight remains embedded where necessary, creating a hybrid model that combines automation efficiency with operational governance.

The result is a more connected process capable of delivering near real-time provisioning while reducing operational complexity.

The deployment reflects a broader evolution occurring within enterprise automation.

Historically, automation projects focused on individual tasks, such as data entry, workflow approvals, or document processing. Increasingly, organizations are moving toward orchestrating entire business processes across multiple systems, teams, and technologies.

This shift is particularly relevant in telecommunications, where providers often operate highly complex technology environments built through years of acquisitions, infrastructure investments, and regulatory requirements.

Replacing those systems is often expensive and risky. As a result, orchestration platforms are emerging as a practical alternative, allowing companies to integrate AI and automation capabilities without disrupting critical business operations.

For UiPath, the deployment serves as a high-profile example of its broader strategy to move beyond traditional robotic process automation.

The company has increasingly positioned itself around business orchestration, combining AI agents, workflow automation, process intelligence, and human collaboration into a unified operational framework. UiPath Maestro represents a key component of that strategy, designed to coordinate work across enterprise ecosystems rather than simply automate isolated tasks.

The approach aligns with wider industry trends.

According to Gartner, organizations are increasingly investing in orchestration technologies as they seek to operationalize AI at scale. While generative AI has generated significant interest across industries, many enterprises continue to struggle with integrating AI capabilities into real-world business processes. Orchestration platforms aim to bridge that gap by connecting AI systems with existing workflows, applications, and operational controls.

IDC similarly forecasts continued growth in intelligent automation and AI-powered workflow technologies as organizations pursue productivity improvements and operational agility.

For One NZ, the benefits extend beyond provisioning efficiency.

The company has outlined plans to expand orchestration capabilities into finance, risk management, fraud detection, customer operations, and large-scale IT initiatives. This suggests the telecommunications provider views orchestration not as a standalone automation project but as a foundational layer supporting broader enterprise transformation.

The strategy aligns with One NZ's publicly stated ambition to become one of the world's most AI-enabled telecommunications providers.

For marketing and customer experience leaders, the implications are significant. Faster provisioning directly affects customer onboarding, service activation, and overall customer satisfaction. Reducing operational delays can improve enterprise account experiences while freeing employees to focus on higher-value interactions rather than administrative tasks.

The broader telecommunications industry is likely to pay close attention.

Telecom operators globally are under pressure to improve efficiency, reduce operational costs, and accelerate service delivery while managing increasingly complex technology ecosystems. AI orchestration platforms offer a pathway to achieve those goals without requiring extensive infrastructure modernization programs.

As enterprises move from AI experimentation to large-scale implementation, orchestration is emerging as one of the most important components of digital transformation strategies. One NZ's deployment demonstrates how organizations can combine AI, automation, and existing technology investments to achieve measurable business outcomes in weeks rather than years.

Market Landscape

The intelligent automation market is evolving from task-level automation toward enterprise-wide orchestration. Gartner has identified AI orchestration and autonomous business processes as key trends shaping the future of digital operations, while IDC forecasts continued growth in workflow automation and AI-powered process management technologies.

Major technology vendors including Microsoft, Salesforce, ServiceNow, Oracle, SAP, and UiPath are expanding orchestration capabilities designed to connect AI systems with existing enterprise applications. Rather than replacing legacy infrastructure, organizations are increasingly adopting orchestration layers that enable AI-driven transformation while preserving existing investments.

The telecommunications industry has emerged as a major adopter of these technologies due to its reliance on complex operational systems, customer service workflows, and large-scale infrastructure management processes.

Top Insights

•  One NZ reduced enterprise mobile provisioning times from up to ten days to less than ten minutes using UiPath Maestro orchestration technology.
• The deployment connected Salesforce, Oracle, and internal systems without requiring replacement of existing infrastructure.
• AI agents, robotic process automation, and human workflows were orchestrated through a unified operational layer to streamline provisioning processes.
• The implementation was completed in five weeks, highlighting how orchestration platforms can accelerate enterprise transformation initiatives.
• One NZ plans to expand AI orchestration into finance, fraud management, risk operations, IT programs, and broader customer service workflows.

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Consumers are spending more time and money on video entertainment than they have in years, according to TiVo’s latest Video Trends Report. The findings suggest that video remains one of the most resilient categories in the media industry, even as streaming fragmentation, subscription fatigue, and economic uncertainty reshape how audiences discover and consume content. For advertisers, media companies, and streaming platforms, the report highlights a growing challenge: viewers are watching more, but finding content is becoming increasingly difficult.

The streaming industry has spent years focused on competition for audience attention. According to new research from TiVo, the challenge may no longer be convincing consumers to watch more video—it may be helping them navigate an increasingly crowded entertainment ecosystem.

TiVo's Q4 2025 Video Trends Report paints a picture of a media landscape where engagement remains remarkably strong despite growing fragmentation. Households now subscribe to more than 10 video services on average, daily viewing exceeds five hours, and monthly entertainment spending has climbed to $161, reversing declines that followed the post-pandemic normalization of viewing habits.

The findings underscore a reality that many media executives have long suspected: video entertainment continues to occupy a privileged position in consumer spending priorities, even as economic pressures affect discretionary purchases across other categories.

Yet the report also reveals a paradox. While viewers have access to more content than ever before, discovering that content is becoming increasingly complex.

Approximately 40% of consumers report checking two or three separate streaming applications before deciding what to watch. That behavior highlights a growing friction point for both consumers and content providers. As streaming services multiply and content libraries become fragmented across platforms, viewers are spending more time searching and less time engaging.

For marketers and media companies, the implications are significant.

Content discovery is no longer occurring solely within streaming platforms. Word-of-mouth recommendations influence nearly half of viewers, while social media now plays a major role in helping audiences find programming. This shift is reshaping how entertainment brands think about audience acquisition, promotion, and engagement.

The trend aligns with broader changes across the digital media industry, where recommendation engines, social platforms, and algorithmic feeds increasingly act as gateways to content consumption.

One of the most notable findings from the report is the continued strength of local programming and live content.

Local content now accounts for nearly 30% of viewing time, representing a meaningful increase compared with the previous year. Sports programming also remains a powerful driver of audience engagement, with nearly 60% of sports viewers relying on traditional pay television as their primary source for live events.

The continued importance of sports is particularly relevant as media companies invest billions of dollars in live rights agreements. While streaming platforms have aggressively expanded into sports, the report suggests traditional television remains deeply embedded in how many viewers access premium live content.

For advertisers, this creates a dual-platform environment where audiences are increasingly fragmented across streaming services while remaining concentrated around key live events.

The report also highlights continued growth in ad-supported viewing models, one of the most important developments in the streaming industry.

More than half of consumers now subscribe to ad-supported streaming tiers, while adoption of ad-supported video-on-demand (AVOD) and free ad-supported television (FAST) services has reached 70%. Together, AVOD and FAST platforms account for 13% of total viewing time.

This shift reflects changing consumer attitudes toward subscription spending. As streaming costs rise, viewers are becoming more willing to accept advertising in exchange for lower subscription fees or free access to content.

Platforms such as Pluto TV, Tubi, Roku Channel, and Amazon Prime Video continue to benefit from this trend, attracting audiences seeking value-oriented entertainment options.

The growing popularity of FAST channels is particularly important for the advertising technology ecosystem.

Unlike subscription-only streaming services, FAST platforms offer advertisers scalable inventory, audience targeting capabilities, and measurable engagement opportunities. As marketers look for alternatives to traditional television advertising, these platforms are emerging as increasingly attractive channels for brand campaigns.

Another finding with implications for advertisers involves the role of smart TV interfaces.

According to the report, consumers spend 57% of their non-viewing television time on smart TV home screens. This transforms the home screen from a navigation tool into a valuable advertising and content discovery environment, creating new opportunities for platform operators and marketers alike.

The findings arrive as streaming services face mounting pressure to balance subscriber growth, profitability, and user experience. While content investment remains critical, the report suggests discovery and curation may become equally important competitive differentiators.

Industry analysts have increasingly argued that the next phase of streaming competition will center less on content quantity and more on helping viewers efficiently find relevant programming.

As entertainment options continue expanding, consumers are demonstrating a clear preference for simplicity, convenience, and value. Services that reduce search friction and improve content discovery may gain a meaningful advantage in an increasingly saturated market.

For advertisers, publishers, and streaming platforms, TiVo’s latest research delivers a clear message: audiences remain deeply engaged with video, but engagement alone is no longer enough. In a fragmented media ecosystem, helping consumers find what they want may become just as important as creating the content itself.

Market Landscape

The global streaming and connected TV market is entering a new maturity phase. According to industry forecasts from Gartner and Statista, streaming adoption remains strong, but consumer attention is increasingly distributed across subscription, ad-supported, and free streaming environments.

Major media and technology companies including Netflix, Disney, Amazon, Google, Roku, Comcast, Warner Bros. Discovery, and Paramount are investing heavily in content discovery, advertising infrastructure, recommendation systems, and audience analytics.

As connected TV advertising continues to grow, industry focus is shifting toward viewer retention, content personalization, and cross-platform discoverability. For advertisers and publishers, solving discovery challenges may become one of the most valuable opportunities in the next generation of digital media.

Top Insights

•  TiVo's Q4 2025 report found consumers are watching more than five hours of video daily and subscribing to over 10 services on average.
• Streaming fragmentation is creating discovery challenges, with 40% of viewers checking multiple apps before deciding what to watch.
• Ad-supported streaming continues to gain momentum, with 54% of consumers using ad-supported subscription tiers and 70% engaging with AVOD or FAST services.
• Local programming and live sports remain key audience drivers, reinforcing the value of premium live content in an increasingly fragmented media environment.
• Smart TV home screens are becoming critical discovery and advertising channels as viewers spend more non-viewing time navigating connected television interfaces.

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