As organizations increasingly embed artificial intelligence into sales workflows, one challenge continues to limit adoption: AI systems often lack access to trusted business context. Highspot is aiming to close that gap with the launch of its MCP Server in the OpenAI ChatGPT App Store, enabling sales teams to access deal intelligence, content recommendations, buyer engagement insights, and sales guidance directly within ChatGPT.

The rapid adoption of generative AI across enterprise organizations is transforming how revenue teams research prospects, prepare for meetings, create sales content, and manage opportunities. However, many organizations are discovering that general-purpose AI tools deliver limited business value when disconnected from the systems that contain critical sales data.

Highspot's latest announcement addresses this challenge by connecting ChatGPT directly to the company's go-to-market performance platform through a Model Context Protocol (MCP) Server integration.

The move reflects a broader trend across enterprise software markets, where organizations are seeking ways to combine large language models with proprietary business systems to generate more accurate, actionable, and context-aware outputs.

Rather than relying solely on public information or generic prompts, sellers using the Highspot MCP Server can access deal-specific intelligence, buyer engagement signals, sales content, and performance insights without leaving ChatGPT.

This integration positions AI as more than a conversational assistant. Instead, it transforms ChatGPT into a contextual sales workspace capable of supporting revenue teams throughout the deal lifecycle.

The launch comes at a time when sales organizations are under increasing pressure to improve productivity while navigating more complex buying journeys.

Research from Gartner and Forrester indicates that B2B buying committees continue to expand, procurement cycles are becoming longer, and buyers are conducting more independent research before engaging with sales representatives. As a result, sellers require faster access to insights that help them personalize outreach, identify risks, and guide opportunities toward successful outcomes.

According to Highspot, the MCP Server enables sales teams to perform several high-value tasks directly inside ChatGPT.

Users can ask complex sales questions and receive responses informed by content repositories, opportunity activity, buyer engagement signals, meeting information, and sales execution data stored within Highspot. The integration also provides visibility into deal health indicators, helping teams identify risks earlier and take corrective action before opportunities stall.

Another significant capability is personalized content generation.

Sales professionals can generate pitches, messaging frameworks, and customer communications tailored to specific industries, buyers, and opportunities while leveraging contextual insights from active deals. This approach moves beyond generic AI-generated content by grounding recommendations in real customer interactions and engagement data.

The launch also highlights the growing importance of contextual AI in enterprise environments.

Many organizations initially adopted generative AI to automate basic content creation and information retrieval tasks. However, the next phase of AI adoption is increasingly focused on connecting models to operational systems where critical business knowledge resides.

This trend has accelerated the adoption of technologies such as retrieval-augmented generation (RAG), enterprise knowledge integrations, AI agents, and Model Context Protocol implementations.

By enabling AI systems to securely access enterprise data sources, organizations can generate outputs that are more relevant, accurate, and aligned with business objectives.

The integration aligns with broader developments across the enterprise software ecosystem. Major technology providers including OpenAI, Microsoft, Salesforce, and HubSpot are increasingly investing in AI-powered workflows that connect language models with business applications.

For sales enablement platforms, the opportunity extends beyond productivity gains.

Organizations increasingly want AI systems capable of guiding decision-making, recommending actions, identifying risks, and improving execution quality. This shift represents the emergence of agentic AI within revenue operations, where AI acts as an active participant in sales workflows rather than simply a content-generation tool.

Highspot describes its platform as an agentic solution for go-to-market performance, and the MCP Server integration advances that vision by making sales intelligence accessible wherever sellers are already working.

The concept aligns with the company's broader "Highspot Everywhere" strategy, which focuses on delivering enablement resources directly within the tools used by revenue teams rather than requiring users to switch between multiple applications.

Reducing tool fragmentation has become an increasingly important priority for sales organizations. Studies consistently show that excessive application switching can reduce productivity, slow decision-making, and create inefficiencies throughout the sales process.

By embedding Highspot's intelligence into ChatGPT, the company aims to create a more unified workflow that combines conversational AI with sales execution insights.

As enterprises continue investing in AI-powered revenue operations, integrations that connect language models with trusted business systems are likely to become increasingly important. The value of AI in sales is no longer determined solely by model sophistication but by the quality, relevance, and accessibility of the business context that powers it.

For organizations seeking to operationalize AI across go-to-market functions, contextual intelligence may ultimately prove more valuable than generative capabilities alone.

Market Landscape

The enterprise sales AI market is rapidly evolving as organizations seek to combine generative AI with proprietary business intelligence. Key trends include:

• Growing adoption of Model Context Protocol (MCP) integrations.
• Expansion of agentic AI across revenue operations.
• Increased demand for contextual AI experiences.
• Rising investments in sales enablement and revenue intelligence platforms.
• Greater focus on workflow consolidation and productivity optimization.

Industry analysts predict that AI systems connected to enterprise data sources will drive the next wave of productivity gains across sales, marketing, and customer success teams.

Top Insights

• Highspot's MCP Server is now available through the OpenAI ChatGPT App Store.
• The integration allows sales teams to access Highspot content, deal intelligence, and buyer engagement insights directly within ChatGPT.
• Sellers can identify deal risks, receive next-best-action recommendations, and generate personalized sales content using real opportunity context.
• The launch reflects a broader shift toward contextual AI and agentic workflows in enterprise sales.
• Connecting AI systems to trusted business data is becoming essential for improving sales effectiveness and revenue outcomes

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Chief Marketing Officers are increasingly directing marketing budgets toward customer acquisition and digital channels as artificial intelligence transforms campaign execution, measurement, and optimization. According to new research from Gartner, awareness and conversion activities now account for 62.6% of total media spending, highlighting a growing focus on growth-oriented marketing strategies. However, the findings also reveal that successful AI adoption depends as much on talent and operational maturity as it does on technology investment.

Artificial intelligence is rapidly changing how marketing organizations allocate resources, measure performance, and engage customers. As companies seek new growth opportunities in increasingly competitive markets, many marketing leaders are reallocating budgets toward digital channels and customer acquisition initiatives that can be optimized using AI-driven tools and analytics.

New findings from Gartner's 2026 CMO Spend Survey illustrate the scale of this transformation.

The survey, conducted between January and March 2026 among 401 chief marketing officers and senior marketing executives across North America, the United Kingdom, and Europe, found that awareness and conversion activities now account for 62.6% of total media spending. The figure represents a significant increase from 2024 and reflects a broader industry shift toward measurable growth initiatives.

At the same time, investment in customer loyalty and retention programs has declined substantially. Spending on retention-focused marketing has fallen by nearly 29% since 2024 and now represents less than 15% of total media budgets.

The data suggests that many organizations are prioritizing customer acquisition over long-term relationship building as they seek immediate growth opportunities.

This trend coincides with a broader migration toward digital channels.

According to the survey, digital media now represents more than two-thirds of total marketing media investments, marking an 18% increase since 2024. AI is playing a major role in this shift, with marketing leaders citing enhanced personalization capabilities, automation opportunities, and optimization efficiency as key drivers behind channel selection decisions.

As AI-powered tools become more integrated into advertising platforms, marketing teams are increasingly favoring channels where performance can be measured, optimized, and scaled through automated processes.

The trend is evident across major digital ecosystems, including platforms operated by Google, Meta, Microsoft, and Amazon, all of which continue expanding AI-powered campaign management capabilities.

However, Gartner's findings also raise important questions about whether organizations may be over-optimizing for short-term performance.

Interestingly, the research indicates that the most AI-mature marketing organizations allocate a larger portion of their budgets to customer loyalty and retention than their less mature counterparts. These organizations also spend relatively less on digital channels, suggesting a more balanced approach to customer lifecycle management.

This distinction highlights a growing concern among marketing leaders: the risk that AI may unintentionally encourage investment in activities that are easiest to automate and measure while undervaluing initiatives that build long-term customer relationships.

Performance marketing channels often provide immediate visibility into return on investment, making them attractive targets for AI optimization. In contrast, brand-building, loyalty programs, and customer experience initiatives typically generate results over longer time horizons and can be more difficult to quantify.

As a result, organizations that focus exclusively on short-term optimization may risk sacrificing future customer value for near-term efficiency gains.

The survey also challenges a common assumption about AI's impact on workforce costs.

Despite widespread expectations that AI would reduce staffing requirements, labor costs are actually consuming a larger share of marketing budgets.

Labor represented 24.5% of total marketing spending in 2026, up from 21.9% in 2025. Rather than replacing employees, many organizations appear to be investing in new capabilities, specialized expertise, and operational resources needed to effectively deploy AI technologies.

This reflects a broader reality emerging across enterprise AI adoption initiatives.

Technology alone does not create business value. Organizations must also develop the processes, governance structures, and workforce capabilities required to integrate AI into day-to-day operations.

The survey reveals that many marketing departments are still struggling with this transition.

Seventy percent of respondents reported that their internal marketing processes lack the maturity needed to effectively scale AI initiatives. Additionally, only 30% of surveyed organizations described their AI readiness capabilities as mature or fully developed.

Talent shortages remain another significant obstacle.

Nearly four in ten marketing leaders identified a lack of AI expertise and internal skills as the primary barrier preventing them from achieving greater efficiency through AI adoption.

These findings suggest that AI implementation challenges are increasingly organizational rather than technological.

While AI platforms continue advancing rapidly, many enterprises are discovering that successful adoption requires workforce development, operational transformation, and leadership alignment alongside technology investments.

The implications extend beyond marketing departments.

As AI becomes embedded across customer acquisition, analytics, personalization, content creation, and campaign optimization, organizations will need to rethink how teams are structured, how decisions are made, and how performance is measured.

For CMOs, the challenge is no longer whether to invest in AI. The more pressing question is how to balance short-term efficiency gains with long-term customer value while building the capabilities required to sustain competitive advantage.

The organizations that succeed may ultimately be those that view AI not as a replacement for marketing expertise but as a force multiplier for well-developed people, processes, and strategic execution.

Market Landscape

The Gartner findings reflect several major trends reshaping modern marketing organizations:

• Rapid adoption of AI-powered marketing technologies.
• Increased investment in digital advertising and acquisition channels.
• Growing emphasis on measurable marketing performance.
• Rising demand for AI-related marketing skills and expertise.
• Renewed focus on balancing acquisition efficiency with customer lifetime value.

Industry analysts increasingly view operational readiness, governance, and workforce capabilities as critical success factors for enterprise AI adoption.

Top Insights

• Awareness and conversion activities now account for 62.6% of total media spending among surveyed CMOs.
• Digital media represents more than two-thirds of marketing investments, rising 18% since 2024.
• Spending on customer loyalty and retention has declined by 29% since 2024.
• Labor's share of marketing budgets increased from 21.9% in 2025 to 24.5% in 2026.
• Seventy percent of marketing leaders report their organizations are not mature enough to effectively scale AI initiatives.
• Lack of AI expertise remains the leading barrier to achieving AI-driven efficiency gains.

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As enterprises accelerate investments in AI agents, many are encountering a critical challenge: how to scale autonomous systems while maintaining governance, compliance, reliability, and cost control. At PegaWorld 2026, Pegasystems unveiled new agentic AI capabilities designed to address these concerns, including support for the emerging Model Context Protocol (MCP) standard that allows third-party AI agents to securely discover and execute enterprise workflows within the Pega platform.

Article

The enterprise AI market is entering a new phase.

While organizations have spent the past two years experimenting with generative AI assistants and copilots, attention is increasingly shifting toward AI agents capable of independently executing tasks, coordinating workflows, and making operational decisions. These agentic systems promise substantial gains in productivity and automation, but they also introduce new challenges around governance, predictability, compliance, and operational risk.

Pega's latest announcement reflects growing demand for enterprise-grade orchestration platforms capable of managing AI agents at scale.

The company has introduced support for the open Model Context Protocol (MCP), enabling AI agents built on platforms such as OpenAI, Anthropic, Google, and Amazon Web Services to discover and execute workflows running within Pega's business orchestration environment.

The development highlights a broader industry trend toward interoperability in agentic AI systems.

Rather than operating as isolated assistants, modern AI agents increasingly require access to enterprise applications, business processes, customer data, and workflow automation platforms. Model Context Protocol has emerged as one of the key frameworks designed to standardize how AI systems interact with enterprise software environments.

For organizations pursuing large-scale AI adoption, this interoperability is becoming increasingly important.

Many enterprises are deploying multiple AI models, agent frameworks, and automation technologies simultaneously. Without a common orchestration layer, these systems can create fragmented workflows, inconsistent outcomes, and escalating operational complexity.

Pega's strategy centers on positioning business processes as the control mechanism for agent execution.

Traditional agent architectures often require AI systems to repeatedly reason through complex workflows at every decision point. While flexible, this approach can introduce variability in outcomes, increase token consumption, and create governance challenges.

Pega's Business Orchestration and Automation Technology (BOAT) platform takes a different approach by allowing AI agents to execute predefined workflows that guide actions through structured process steps.

The result is intended to provide greater consistency, auditability, and cost predictability for mission-critical business operations.

The launch comes amid growing concerns about the economics of agentic AI.

According to Gartner, more than 40% of agentic AI projects could be canceled before the end of 2027 because of escalating costs, insufficient governance, and unclear business value. As organizations move beyond pilot programs, executives are increasingly demanding measurable outcomes and stronger operational controls before approving broader deployments.

This reality is driving demand for orchestration platforms capable of balancing innovation with enterprise governance requirements.

Beyond MCP support, Pega also introduced new pre-built AI agents aimed at automating common business processes.

One of the new capabilities, the agentic assignment agent, is designed to proactively engage employees or customers when additional information, approvals, or actions are required to complete a workflow. Rather than relying on manual follow-up, the agent can initiate communications through email, chat, or telephony channels to keep processes moving forward.

The company also unveiled a new document agent focused on intelligent document processing.

The solution can analyze, categorize, segment, score, and route documents for downstream workflows while enabling employees to interact with PDFs, images, and other files through conversational interfaces. These capabilities align with a growing enterprise focus on automating document-heavy processes such as claims management, customer onboarding, compliance reviews, and financial operations.

The announcement further strengthens Pega's position in the emerging market for agent orchestration.

Industry analysts increasingly view orchestration as one of the most important layers within enterprise AI architectures. While foundational models generate intelligence, orchestration platforms determine how that intelligence is applied within real business environments.

Major enterprise software providers including Microsoft, Salesforce, ServiceNow, and IBM are similarly investing in orchestration technologies that connect AI agents with business systems and operational workflows.

For enterprise leaders, the key challenge is no longer building AI agents but ensuring those agents operate reliably within regulated, high-stakes environments.

Industries such as banking, insurance, healthcare, telecommunications, and government require strict controls over how decisions are made, how actions are executed, and how outcomes are audited. Agentic systems that cannot meet those requirements are unlikely to achieve large-scale adoption.

Pega's MCP-enabled orchestration model addresses this challenge by placing business processes at the center of AI execution. Instead of allowing agents to independently navigate every task, organizations can define structured pathways that maintain compliance while still benefiting from AI-driven automation.

As enterprises move from AI experimentation to operational deployment, orchestration platforms are emerging as a critical layer for turning autonomous agents into trusted business systems. The organizations that succeed may not be those with the most agents, but those with the strongest ability to govern, coordinate, and scale them effectively.

Market Landscape

The enterprise agentic AI market is evolving rapidly as organizations seek to operationalize autonomous systems while maintaining governance and cost controls. Key trends include:

• Growing adoption of Model Context Protocol (MCP) for AI interoperability.
• Increased investment in AI orchestration platforms.
• Rising demand for enterprise-grade governance and compliance controls.
• Expansion of intelligent document processing and workflow automation.
• Greater focus on predictable AI outcomes and cost management.

Industry analysts predict orchestration and governance technologies will become foundational components of enterprise AI architectures as agent deployments scale.

Top Insights

• Pega has added MCP support, enabling third-party AI agents to discover and execute enterprise workflows.
• The integration supports agents built on platforms including OpenAI, Anthropic, Google Gemini, and AWS ecosystems.
• Pega's orchestration approach aims to improve predictability, auditability, compliance, and cost control.
• New agentic assignment and document-processing agents expand automation capabilities across enterprise workflows.
• Agent orchestration is emerging as a critical layer for scaling AI adoption in regulated and mission-critical environments

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As hybrid work environments become the norm across industries, IT teams face growing pressure to securely provision and manage devices across distributed workforces. Mobile Device Management (MDM) platforms are increasingly becoming a critical component of enterprise IT infrastructure, enabling organizations to automate onboarding, enforce security policies, and maintain compliance at scale. Against this backdrop, AppTec has highlighted new capabilities within its AppTec360 Mobile Device Management platform aimed at streamlining device provisioning and centralized endpoint management for modern enterprises.

The shift toward hybrid and remote work has fundamentally changed how organizations deploy and manage employee devices. Enterprises today must support users working across offices, homes, and mobile environments while ensuring consistent security controls and seamless access to business applications.

This operational challenge has elevated Mobile Device Management from a niche IT function to a strategic component of enterprise digital workplace initiatives.

AppTec, a Switzerland-based provider of enterprise mobility management solutions, is positioning its AppTec360 Mobile Device Management platform as a centralized solution for device enrollment, configuration, policy enforcement, and application deployment. The platform is designed to help organizations reduce manual provisioning tasks while maintaining governance across increasingly diverse endpoint ecosystems.

At the core of the platform is automated device enrollment.

Traditionally, onboarding new devices required IT administrators to manually configure settings, install applications, and apply security controls. In distributed work environments, this process often creates delays and increases administrative overhead. AppTec360 automates much of this workflow by enabling devices to be enrolled and configured remotely, allowing employees to access corporate resources more quickly after receiving their hardware.

The platform also provides centralized policy management, enabling organizations to apply consistent security settings and operational controls across managed endpoints.

This capability is becoming increasingly important as enterprises navigate evolving cybersecurity risks. According to Gartner, endpoint devices remain one of the most common attack surfaces for enterprise environments, particularly as organizations expand support for remote and hybrid work models. Consistent policy enforcement helps reduce vulnerabilities while ensuring compliance with internal governance requirements and industry regulations.

Remote configuration management is another area gaining attention among IT leaders.

Rather than requiring physical access to devices, administrators can deploy configurations, update settings, and troubleshoot endpoints remotely. For organizations operating across multiple locations or supporting global workforces, remote management significantly reduces support costs and accelerates device readiness.

The platform also addresses software deployment challenges through centralized application management capabilities.

As enterprises continue adopting SaaS applications and cloud-based productivity tools, IT teams must ensure employees have timely access to approved software while maintaining visibility into application usage. AppTec360 enables organizations to distribute and manage business-critical applications from a centralized console, helping standardize user experiences across device fleets.

The broader endpoint management market is evolving rapidly as organizations seek unified approaches to device administration.

Industry leaders such as Microsoft, VMware, IBM, and Cisco continue investing in unified endpoint management (UEM) technologies that combine device management, security, identity, and compliance capabilities.

AppTec's strategy aligns with this broader trend toward centralized endpoint administration.

The company reports that its AppTec360 platform currently supports more than 6,400 organizations across 107 countries. As enterprises increasingly manage smartphones, tablets, laptops, and other connected devices within a single operational framework, centralized management platforms are becoming essential for maintaining operational consistency.

The market opportunity remains significant.

According to IDC, global spending on enterprise mobility and endpoint management technologies continues to grow as organizations prioritize digital workplace modernization, cybersecurity resilience, and workforce productivity. Similarly, Forrester research indicates that hybrid work models are driving sustained demand for cloud-based device management solutions that reduce IT complexity while improving employee experiences.

For enterprise IT leaders, the challenge extends beyond device deployment.

Modern endpoint strategies must balance security, compliance, usability, and operational efficiency. Employees expect frictionless access to applications and resources regardless of location, while organizations must maintain visibility and control over corporate data and infrastructure.

This balancing act has accelerated adoption of automation-driven management platforms that reduce administrative workloads while supporting increasingly complex device ecosystems.

AppTec360's focus on automated provisioning, centralized policy enforcement, remote management, and application deployment reflects the industry's broader move toward scalable endpoint management architectures designed for hybrid work environments.

As organizations continue investing in digital workplace transformation, the ability to securely provision and manage devices at scale is becoming a competitive necessity rather than an operational convenience. Mobile Device Management platforms are increasingly serving as the foundation that enables secure, productive, and flexible work environments across modern enterprises.

Market Landscape

The enterprise Mobile Device Management market is being shaped by several key trends:

• Growth of hybrid and remote work environments.
• Rising demand for unified endpoint management platforms.
• Increased focus on cybersecurity and device compliance.
• Expansion of cloud-based device provisioning and management.
• Greater reliance on automation to reduce IT operational complexity.

According to IDC, endpoint management remains a strategic investment area as organizations modernize workplace infrastructure and support increasingly distributed workforces.

Top Insights

•  AppTec360 enables automated device enrollment, reducing manual configuration efforts and accelerating employee onboarding across distributed workforces.
• The platform centralizes policy enforcement, helping organizations maintain consistent security and compliance standards across managed endpoints.
• Remote configuration capabilities allow IT teams to provision and manage devices without requiring physical access, supporting hybrid workplace operations.
• Centralized application deployment simplifies software distribution while ensuring employees have secure access to business-critical tools.
• Growing demand for endpoint management solutions reflects broader enterprise investments in digital workplace modernization and cybersecurity resilience.

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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.

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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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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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Inventory management remains one of the biggest operational challenges for home medical equipment (HME) providers, many of which continue to rely on spreadsheets, manual inventory checks, and disconnected ordering systems. HME360 is aiming to address those inefficiencies with an expanded PAR Optimization solution that introduces automated replenishment workflows, inventory exception monitoring, and advanced inventory performance reporting designed to improve stock visibility, reduce excess inventory, and strengthen operational control across healthcare equipment networks.

As healthcare providers face growing pressure to improve operational efficiency while controlling costs, inventory optimization is emerging as a critical area of digital transformation. For home medical equipment (HME) organizations, maintaining the right balance of inventory across warehouses, branch locations, delivery fleets, and consignment environments can directly impact patient service levels, cash flow, and business performance.

HME360, an inventory optimization platform built specifically for HME providers, has announced an expansion of its PAR Optimization capabilities aimed at automating replenishment processes and improving inventory visibility across distributed operations.

The update reflects a broader trend across healthcare operations where organizations are increasingly adopting automation and analytics tools to modernize supply chain management and reduce reliance on manual workflows.

At the center of the release is a new Automated PAR Recommendation workflow designed to help organizations maintain appropriate inventory levels based on actual demand patterns rather than manual estimates or periodic reviews.

PAR, or periodic automatic replenishment, is widely used to establish minimum and maximum inventory levels across locations. However, many healthcare providers still manage PAR settings through spreadsheets and manual inventory assessments, making it difficult to respond quickly to changing utilization patterns or supply chain disruptions.

HME360's automation capabilities seek to address this challenge by using operational data to recommend inventory levels based on utilization rates, days-on-hand targets, lead times, and inventory buffers. The goal is to ensure products remain available where needed while reducing excess stock and minimizing unnecessary purchasing activity.

The expansion also introduces Inventory PAR Exception Monitoring, a capability designed to provide real-time visibility into inventory imbalances across an organization's network.

For many HME providers, inventory is distributed across multiple facilities, field service vehicles, branch locations, and customer-facing environments. This complexity often creates blind spots that result in stockouts, excess inventory accumulation, emergency transfers, or inefficient procurement decisions.

The new monitoring functionality identifies inventory exceptions such as below-minimum stock levels, excess inventory, overstock conditions, transfer opportunities, and replenishment issues before they become operational disruptions.

By proactively identifying these exceptions, organizations can reduce the reactive "firefighting" approach that often consumes warehouse and operations teams.

The third major enhancement focuses on inventory performance analytics. HME360 has introduced Inventory Turns and Inventory Strategy reporting designed to help providers better understand asset utilization, inventory investment performance, and equipment movement trends.

Inventory turns remain a key operational metric because they measure how effectively organizations convert inventory investments into revenue-generating activity. Low inventory turnover often indicates excess stock, underutilized assets, or inefficient purchasing practices that can tie up working capital.

The new reporting tools aim to provide greater visibility into days-on-hand metrics, slow-moving inventory, utilization performance, and broader inventory investment strategies.

For healthcare providers operating in increasingly cost-sensitive environments, these insights can play an important role in improving financial performance while maintaining service quality.

The enhancements arrive at a time when healthcare supply chain management is undergoing significant modernization. According to Gartner, healthcare organizations continue to increase investments in automation technologies, analytics platforms, and digital supply chain solutions to improve operational resilience and cost control. Meanwhile, IDC research highlights growing adoption of AI-powered inventory management systems designed to improve forecasting accuracy and resource utilization.

Although many healthcare technology discussions focus on patient-facing innovation, back-office operational systems are becoming equally important as providers seek efficiencies that support sustainable growth.

HME360's latest release reflects this shift toward operational intelligence and workflow automation. Rather than relying on manual inventory oversight, organizations are increasingly seeking systems capable of generating actionable recommendations, identifying risks early, and supporting data-driven decision-making.

Company executives describe the enhancements as part of a broader strategy to reduce manual administrative work while improving visibility into inventory performance throughout the business.

For HME providers, the stakes are significant. Excess inventory can lock up capital needed for expansion initiatives, while stock shortages can affect patient service delivery and operational efficiency. Striking the right balance requires accurate forecasting, continuous monitoring, and integrated inventory controls.

As healthcare supply chains become more complex and distributed care models continue to expand, inventory optimization platforms are evolving from simple tracking tools into strategic operational systems. Solutions capable of automating replenishment, improving asset visibility, and supporting financial decision-making are increasingly becoming essential infrastructure for healthcare equipment providers.

The latest HME360 enhancements position the platform within this growing category of healthcare operations technology, where automation and analytics are helping organizations transform inventory management from a reactive process into a strategic advantage.

Market Landscape

Healthcare supply chain technology is undergoing rapid transformation as providers seek greater efficiency, visibility, and resilience across operations. According to Gartner and IDC, healthcare organizations are increasingly investing in inventory automation, predictive analytics, and digital workflow platforms to address labor shortages, rising operational costs, and growing service demands. Home medical equipment providers face additional challenges due to distributed inventory environments spanning warehouses, branch offices, delivery fleets, and consignment locations. As a result, inventory optimization platforms are becoming critical tools for improving asset utilization, reducing excess inventory, and supporting scalable growth strategies.

Top Insights

• HME360 expanded its PAR Optimization platform with automated replenishment workflows designed to reduce manual inventory management and improve ordering accuracy.
• New inventory exception monitoring capabilities help providers identify stock shortages, excess inventory, transfer opportunities, and replenishment issues earlier.
• Advanced inventory analytics offer visibility into inventory turns, days-on-hand metrics, utilization trends, and overall inventory investment performance.
• The enhancements support healthcare organizations seeking to reduce excess inventory while improving service levels across distributed operational environments.
• The release reflects broader healthcare technology trends toward automation, predictive analytics, and data-driven supply chain management.

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