Planview and Highspot have unveiled new agentic AI capabilities aimed at reshaping enterprise resource management and go-to-market (GTM) execution. The announcements highlight a broader shift toward managing AI agents as operational resources alongside human teams across portfolios, marketing, and revenue functions.

Enterprise software is entering an “agentic” phase, where artificial intelligence systems are no longer just tools but active participants in workflows. Two new announcements from Planview and Highspot underscore how quickly this shift is redefining both operational management and revenue execution.

Planview’s introduction of Agent Resource Management extends traditional portfolio and resource planning into a new domain: managing AI agents as first-class contributors to enterprise work. Historically, Strategic Portfolio Management (SPM) platforms have focused on allocating human capacity across projects. Planview is now expanding that model to include AI agents—tracking their cost, performance, and accountability alongside human resources.

The rationale is straightforward. As organizations deploy more AI agents to automate tasks, they need visibility into how those agents are used, what they cost, and whether they deliver measurable outcomes. Planview’s system provides a unified view of both human and AI resources, allowing leaders to plan, assign, and govern work across a blended workforce.

This capability arrives at a pivotal moment. According to Gartner, 40% of enterprise applications are expected to include task-specific AI agents by 2026, marking a dramatic increase in adoption. Meanwhile, Deloitte reports that more than half of CFOs are prioritizing AI agent integration, signaling growing executive focus on operationalizing AI.

Planview’s approach emphasizes governance as much as automation. The platform introduces policy enforcement, audit trails, and escalation mechanisms to ensure that AI agents operate within defined boundaries. This is critical as enterprises move from experimentation to production-scale AI deployments, where accountability becomes a central concern.

The platform also introduces scenario modeling that allows organizations to simulate different mixes of human and AI resources before committing to execution. This capability reflects a broader trend toward predictive planning, where enterprises use data and analytics to optimize workforce allocation in real time.

In parallel, Planview is launching purpose-built AI agents for portfolio delivery. These include a project management agent that generates updates and identifies blockers, a backlog agent that ensures readiness of tasks, and forecasting agents that predict delivery risks. Unlike generic AI tools, these agents are designed for specific enterprise workflows and are governed within the same system as human resources.

While Planview focuses on operational planning, Highspot is targeting a different layer of the enterprise stack: revenue execution. Its new GTM Agent aims to bridge the gap between strategy and execution in sales and marketing teams.

This gap is well documented. Organizations often invest heavily in content, training, and analytics, but struggle to translate those investments into consistent deal outcomes. Highspot’s GTM Agent addresses this by connecting signals across CRM activity, buyer engagement, content usage, and training data.

The result is a unified system that provides role-specific guidance to marketing, enablement, and revenue operations teams. Instead of relying on static reports, teams receive real-time recommendations on what actions to take to improve deal performance.

The GTM Agent builds on Highspot’s existing Deal Agent, extending its capabilities beyond individual deals to a broader, cross-functional view of revenue performance. This allows organizations to identify patterns across deals, scale successful strategies, and address performance gaps more quickly.

Integration is a key differentiator. Highspot’s platform connects with tools from Microsoft, OpenAI, and Anthropic, enabling AI agents to operate within existing workflows. This reflects a broader trend toward embedding AI into the “flow of work,” rather than requiring users to switch between systems.

The company is also introducing a GTM Maturity Model, providing a framework for organizations to assess and improve their revenue operations. This aligns with the growing emphasis on continuous optimization, where AI systems not only execute tasks but also identify opportunities for improvement.

From a market perspective, these announcements highlight the convergence of several trends: agentic AI, real-time analytics, and integrated enterprise platforms. Vendors are moving away from standalone tools toward systems that orchestrate workflows, data, and decision-making across the organization.

This shift has significant implications for marketing and MarTech teams. As AI agents become embedded in both operational and revenue processes, the ability to coordinate across systems becomes a competitive advantage. Platforms that can unify data, automate execution, and provide actionable insights are likely to play a central role in enterprise technology stacks.

However, the transition also introduces new challenges. Managing a blended workforce requires new governance models, cost structures, and performance metrics. Organizations must determine not only how to deploy AI agents, but also how to measure their impact and ensure accountability.

The competitive landscape is evolving rapidly. Established enterprise platforms such as Salesforce and Adobe are also investing in AI-driven automation and analytics, while emerging vendors are building agent-first architectures from the ground up.

Planview and Highspot’s latest releases illustrate how different segments of the enterprise software market are adapting to this new reality. One focuses on managing resources across portfolios, the other on optimizing revenue execution—but both are built on the same foundation: AI agents as active participants in business processes.

As enterprises continue to scale their AI initiatives, the ability to manage, govern, and optimize these agents will become increasingly important. The shift from tools to agents is not just a technological change—it represents a new operating model for how work gets done.

Market Landscape

The rise of agentic AI is transforming enterprise software across multiple domains, from portfolio management to sales enablement. Organizations are adopting platforms that integrate AI agents into workflows, enabling real-time decision-making and continuous optimization.

This evolution is driving demand for systems that provide visibility, governance, and scalability, as enterprises seek to manage increasingly complex, AI-driven operations.

Top Insights

• Planview introduces agent resource management, enabling enterprises to plan, track, and govern AI agents alongside human resources, providing full visibility into costs, performance, and accountability.
• Highspot’s GTM Agent connects signals across sales, marketing, and enablement, turning data into real-time, role-specific actions that improve deal execution and revenue performance.
• Both platforms reflect the rise of agentic AI, where autonomous systems actively participate in workflows rather than serving as passive tools, reshaping enterprise operating models.
• Integration with major ecosystems like Microsoft and OpenAI highlights the importance of embedding AI into existing workflows, reducing friction and improving adoption.
• The shift toward blended human-AI workforces introduces new challenges in governance, cost management, and performance measurement, requiring advanced platform capabilities.

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Exigent is consolidating its multi-brand marketing operations with the rollout of Salesforce Account Engagement, implemented in partnership with WhiteRock. The move reflects a growing enterprise trend toward centralized marketing automation systems that unify demand generation, customer data, and campaign execution across distributed business units.

Exigent’s deployment of Salesforce Account Engagement—formerly known as Pardot—marks a strategic shift in how complex, multi-entity organizations manage B2B marketing at scale. The company, which operates across six distinct business units, faced a common enterprise challenge: fragmented marketing systems that limit visibility, consistency, and performance tracking.

By standardizing its marketing infrastructure on Salesforce’s automation platform, Exigent aims to create a single source of truth for campaign execution, lead management, and customer engagement. The platform integrates directly with its existing Salesforce CRM environment, enabling seamless data flow between marketing and sales teams.

This integration is critical. In modern B2B ecosystems, disconnected systems often lead to misaligned messaging, duplicated efforts, and lost revenue opportunities. By unifying its marketing stack, Exigent can orchestrate campaigns across business units while maintaining consistent brand and data governance standards.

The implementation also signals a broader evolution in marketing technology adoption. Platforms like Salesforce Account Engagement are no longer limited to email automation—they now function as central hubs for customer journey orchestration, lead scoring, and performance analytics. This aligns with strategies adopted by major enterprise platforms such as Adobe and Microsoft, which are increasingly embedding AI and automation into marketing workflows.

WhiteRock’s role in the deployment underscores the importance of implementation partners in enterprise MarTech success. Beyond technical setup, the engagement included process standardization and cross-team enablement—often the most challenging aspects of digital transformation initiatives.

For organizations like Exigent, which operate across multiple verticals and geographic regions, aligning internal processes is as critical as selecting the right technology. Without standardized workflows, even the most advanced platforms can fail to deliver meaningful ROI.

The adoption of Account Engagement allows Exigent to centralize campaign management while still supporting the unique needs of each business unit. Marketing teams can now build targeted, data-driven campaigns using shared customer insights, improving both efficiency and relevance.

This shift toward data-driven marketing is increasingly non-negotiable. According to Forrester, companies that align marketing, sales, and customer data are 1.5 times more likely to achieve above-average revenue growth. Similarly, Gartner reports that organizations using integrated marketing automation platforms see up to a 30% improvement in campaign performance and lead conversion rates.

Exigent’s strategy reflects these findings. By consolidating its marketing data within the Salesforce ecosystem, the company gains enhanced visibility into customer behavior, campaign effectiveness, and pipeline performance. This enables more precise targeting, better lead qualification, and improved attribution modeling.

Another key advantage is scalability. As Exigent continues to expand, its unified marketing infrastructure can support new business units without requiring additional standalone systems. This reduces operational complexity and accelerates time-to-market for new campaigns.

The platform also lays the groundwork for more advanced capabilities, including AI-driven personalization and predictive analytics. Salesforce has been actively integrating generative AI and machine learning into its ecosystem, positioning tools like Account Engagement as part of a broader intelligent customer engagement framework.

For enterprise marketing teams, this evolution is significant. Marketing automation platforms are increasingly becoming the backbone of digital engagement strategies, connecting data, content, and customer interactions in real time. This convergence is blurring the lines between CRM, customer data platforms, and marketing orchestration tools.

Exigent’s deployment highlights how even traditionally industrial sectors—such as mechanical systems and infrastructure services—are adopting sophisticated MarTech stacks. Digital transformation is no longer confined to tech-native companies; it is becoming a baseline requirement across industries.

From a competitive standpoint, the ability to execute coordinated, insight-driven campaigns across multiple business units can provide a meaningful advantage. It enables organizations to respond faster to market changes, deliver more personalized experiences, and optimize marketing spend with greater precision.

However, success will depend on how effectively Exigent leverages the platform over time. Technology alone does not guarantee results—ongoing optimization, data quality management, and cross-functional alignment are essential to realizing the full value of marketing automation.

The company’s next phase will focus on expanding automation, reporting, and personalization capabilities. If executed effectively, this could position Exigent to compete more aggressively in a market where digital engagement and customer experience are increasingly key differentiators.

Market Landscape

The adoption of unified marketing automation platforms is accelerating across B2B industries, driven by the need for centralized data, scalable campaign execution, and measurable ROI. As enterprises move toward integrated MarTech stacks, platforms like Salesforce Account Engagement are becoming foundational components of digital marketing infrastructure.

This shift reflects a broader convergence of CRM, customer data platforms, and AI-driven analytics, enabling organizations to deliver more personalized and efficient customer experiences at scale.

Top Insights

• Exigent’s implementation of Salesforce Account Engagement centralizes marketing operations across six business units, enabling unified campaign execution, improved lead management, and consistent customer engagement strategies.
• Integration with Salesforce CRM creates a single source of truth for customer data, enhancing alignment between marketing and sales teams while improving visibility into pipeline performance and attribution.
• The deployment reflects a broader shift toward integrated MarTech stacks, where automation platforms act as core infrastructure for data-driven demand generation and customer journey orchestration.
• WhiteRock’s role highlights the importance of process standardization and team enablement in successful MarTech implementations, particularly for organizations with complex, multi-unit structures.
• The platform positions Exigent to scale marketing efforts, adopt AI-driven personalization, and improve campaign performance in an increasingly competitive and digitally driven B2B landscape.

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Five9 has been named to the CRN 2026 AI 100 list for the third consecutive year, reinforcing its position in the rapidly evolving AI-powered customer experience (CX) market. The recognition highlights how enterprise contact center platforms are shifting from automation tools to intelligent, AI-driven engagement systems.

Recognition lists rarely move markets on their own—but they often signal where enterprise technology is heading. Five9’s repeated inclusion in CRN’s AI 100, particularly among the “Top 20 Hottest AI Software Companies,” reflects a broader transformation underway in customer experience platforms.

The shift is clear: AI is no longer a feature layered onto contact center software—it is becoming the operational core. Five9’s Intelligent CX Platform exemplifies this evolution, embedding artificial intelligence across the entire customer journey, from initial engagement to resolution and post-interaction analytics.

This transformation is being driven by rising customer expectations. Consumers increasingly expect fast, accurate, and personalized interactions, while enterprises face pressure to reduce costs and improve efficiency. Traditional contact center models—reliant on human agents and static workflows—struggle to meet these demands at scale.

Five9’s approach centers on integrating AI directly into CX operations. Its platform uses machine learning and natural language processing to understand customer intent, automate responses, and assist human agents in real time. This allows organizations to resolve issues faster while improving the overall customer experience.

The company’s data underscores the momentum behind this shift. According to Five9 research, 81% of business decision-makers have already implemented AI in the contact center, with the majority reporting improved outcomes. These figures align with broader industry trends. Gartner predicts that by 2027, conversational AI will handle the majority of customer interactions, significantly reducing reliance on traditional support models.

At the center of Five9’s strategy is its Genius AI portfolio, which introduces AI agents capable of reasoning, decision-making, and action. Unlike earlier generations of automation—such as rule-based chatbots—these agents operate with greater contextual awareness, enabling more dynamic and personalized interactions.

This shift toward agentic AI mirrors developments across the enterprise software landscape. Platforms from Salesforce and ServiceNow are also investing heavily in AI agents that can execute tasks autonomously while integrating with broader workflows.

Five9’s differentiation lies in its focus on the contact center as a hub for customer engagement. By embedding AI into this environment, the company aims to bridge the gap between customer expectations and operational capabilities.

Real-world deployments provide insight into the platform’s impact. Healthcare company Exact Sciences has used Five9’s AI capabilities to scale patient support, achieving a 45% call containment rate and reducing patient time spent on calls. Meanwhile, financial services firm SumUp reports significant cost savings and improved self-service adoption.

These outcomes highlight a key advantage of AI-driven CX platforms: the ability to handle high volumes of interactions without proportionally increasing costs. By automating routine inquiries and enabling self-service, organizations can free human agents to focus on complex, high-value interactions.

The integration ecosystem is another critical factor. Five9’s Fusion ecosystem connects its platform with enterprise systems, including CRM and workflow tools. Partnerships with companies like Epic Systems further extend its reach into industry-specific use cases.

This interconnected approach reflects a broader trend toward unified customer data and workflow orchestration. Modern CX platforms are expected to integrate seamlessly with marketing, sales, and service systems, enabling a consistent and personalized customer journey across channels.

From a MarTech perspective, this convergence is particularly significant. Customer experience is no longer confined to service interactions—it spans the entire lifecycle, from acquisition to retention. AI-powered platforms like Five9’s are enabling organizations to unify these touchpoints, creating more cohesive and data-driven engagement strategies.

However, the rapid adoption of AI in CX also raises challenges. Ensuring data privacy, maintaining transparency in AI decision-making, and balancing automation with human empathy are ongoing concerns. Enterprises must navigate these issues carefully as they scale their AI initiatives.

The competitive landscape is intensifying. Major cloud providers such as Amazon and Google are expanding their AI capabilities, while specialized vendors continue to innovate in areas like conversational AI and analytics.

Five9’s continued recognition suggests it has maintained momentum in this crowded market. Its focus on embedding AI deeply into CX workflows, combined with measurable customer outcomes, positions it as a key player in the next phase of customer experience technology.

Ultimately, the significance of this announcement lies less in the award itself and more in what it প্রতিনিধates: the maturation of AI in enterprise CX. Organizations are moving beyond experimentation and toward operationalizing AI at scale.

As this transition accelerates, platforms that can combine automation, intelligence, and integration will define the future of customer engagement. Five9’s trajectory indicates that AI-driven CX is no longer an emerging trend—it is becoming the industry standard.

Market Landscape

The customer experience technology market is rapidly evolving as AI becomes central to engagement strategies. Enterprises are investing in platforms that combine automation, analytics, and personalization to meet rising customer expectations.

This shift is driving convergence between contact center platforms, CRM systems, and marketing technologies, creating unified ecosystems that support end-to-end customer journeys.

Top Insights

• Five9’s third consecutive recognition on CRN’s AI 100 highlights its leadership in AI-powered customer experience, reflecting the growing importance of intelligent CX platforms in enterprise operations.
• The company’s Genius AI portfolio introduces agentic AI capabilities that move beyond traditional automation, enabling more contextual, personalized, and outcome-driven customer interactions.
• Real-world deployments demonstrate measurable impact, including improved self-service rates, reduced call volumes, and significant cost savings across industries like healthcare and financial services.
• Integration with platforms like Salesforce and ServiceNow underscores the importance of connected ecosystems in delivering seamless, data-driven customer experiences.
• The broader market is shifting toward AI-native CX platforms, where automation, analytics, and human expertise combine to deliver scalable and efficient customer engagement.

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Highspot is doubling down on agentic AI with the launch of its GTM Agent, a new capability designed to connect fragmented go-to-market data and turn it into real-time, actionable guidance for sales, marketing, and revenue teams.

Enterprise revenue teams have no shortage of data. What they lack is timing.

That gap—between insight and execution—is what Highspot is targeting with its Spring Launch ’26 release. At the center is GTM Agent, a system designed to translate signals from across the revenue stack into immediate, role-specific actions.

The premise is simple but consequential: data is only valuable if teams can act on it in the moment. In many organizations, insights arrive too late—after deals stall, campaigns underperform, or opportunities are lost. Highspot’s GTM Agent attempts to collapse that lag.

The platform aggregates signals across CRM activity, buyer engagement, content usage, training progress, and meeting intelligence. It then interprets those signals to recommend next steps for different teams—whether that’s adjusting messaging, refining content, or guiding sellers on deal strategy.

This is a shift from analytics to orchestration. Traditional revenue intelligence tools focus on reporting what happened. Agentic systems like GTM Agent aim to influence what happens next.

The launch builds on Highspot’s earlier Deal Agent, which operates at the individual deal level. While Deal Agent provides in-the-moment guidance for sellers, GTM Agent expands that scope across the entire revenue organization. It connects patterns across deals, identifies what is working, and feeds those insights back into execution.

This layered approach reflects a broader evolution in enterprise AI. Systems are moving from isolated use cases to interconnected agents that operate across workflows. Platforms from Microsoft and OpenAI are also advancing agent-based architectures, embedding AI into everyday business processes.

Highspot’s differentiation lies in its focus on go-to-market performance. Rather than building a general-purpose AI layer, it is targeting a specific operational problem: aligning sales, marketing, and enablement around what actually drives revenue outcomes.

The company’s internal research highlights the scale of the issue. While 98% of leaders report having a go-to-market strategy in motion, only 10% say they execute effectively. The bottleneck is not planning—it is coordination and timing.

GTM Agent addresses this by creating a feedback loop between execution and insight. Actions taken within deals—guided by Deal Agent—feed into broader analytics, which GTM Agent then uses to refine recommendations across the organization. Over time, this creates a system that continuously learns and improves.

Integration is critical to making this work. Highspot has embedded its agentic capabilities into existing enterprise tools, including integrations with Anthropic and Microsoft Copilot. This allows AI agents to operate within the tools teams already use, reducing friction and improving adoption.

The introduction of the Highspot MCP Server further extends this capability, enabling external AI agents to access go-to-market context and contribute to decision-making. This reflects a growing trend toward open AI ecosystems, where multiple agents collaborate across platforms.

Alongside GTM Agent, Highspot is introducing a GTM Maturity Model—a framework designed to help organizations assess and improve their revenue operations. Based on data from its global customer base, the model maps a progression from fragmented, reactive execution to a more coordinated, insight-driven system.

For many enterprises, this transition is still in its early stages. According to Forrester, organizations that align sales and marketing processes can achieve up to 19% faster revenue growth and 15% higher profitability. Yet achieving that alignment remains a persistent challenge.

GTM Agent’s value proposition is to operationalize that alignment. By connecting signals across teams and translating them into actionable guidance, it aims to ensure that strategy is consistently executed at the frontline.

For marketing teams, the implications are particularly significant. Content performance, campaign effectiveness, and buyer engagement are no longer measured in isolation. Instead, they are directly tied to deal outcomes, creating a more accountable and performance-driven model.

This also changes how enablement functions operate. Training and coaching can be linked to real-world results, allowing teams to focus on the behaviors that actually drive success. Over time, this could lead to more adaptive and data-driven enablement strategies.

However, the shift toward agentic systems introduces new complexities. Organizations must manage data quality, ensure transparency in AI recommendations, and maintain alignment between automated guidance and business objectives. Without strong governance, the risk of misaligned actions increases.

Competition in this space is intensifying. Established platforms like Salesforce are expanding their AI capabilities, while newer entrants are building agent-first solutions from the ground up. The race is not just to provide insights, but to control the execution layer of enterprise workflows.

Highspot’s approach suggests that the future of go-to-market technology will be defined by systems that can bridge the gap between strategy and execution. The ability to turn data into action—in real time—may become the defining capability of next-generation revenue platforms.

Market Landscape

The go-to-market technology stack is evolving toward agentic AI systems that unify data, workflows, and execution. Enterprises are increasingly adopting platforms that can orchestrate actions across sales, marketing, and customer engagement in real time.

This shift is driving convergence between CRM, sales enablement, and marketing automation platforms, creating integrated ecosystems designed to improve revenue performance and operational efficiency.

Top Insights

• Highspot’s GTM Agent connects CRM, content, training, and engagement signals, enabling real-time, role-specific actions that improve revenue execution across sales, marketing, and enablement teams.
• The platform extends agentic AI beyond individual deals, creating a continuous feedback loop between execution and insight to drive organization-wide performance improvements.
• Integration with Microsoft, OpenAI, and Anthropic ecosystems highlights the importance of embedding AI agents directly into existing enterprise workflows and tools.
• Highspot’s research reveals a major execution gap, with only 10% of organizations effectively translating GTM strategy into consistent frontline action.
• The introduction of a GTM Maturity Model provides enterprises with a structured framework to evolve from siloed execution to coordinated, AI-driven revenue operations.

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Boardwalk Pipelines has completed its acquisition of Spire Marketing, signaling a strategic expansion into integrated natural gas marketing and trading. The newly rebranded entity, Boardwalk Continuum Marketing, reflects a broader industry shift toward end-to-end energy platforms that combine infrastructure with commercial intelligence.

Boardwalk Pipelines LP has finalized its acquisition of Spire Marketing Inc., a move that underscores how energy companies are consolidating infrastructure and commercial capabilities to compete in increasingly complex gas markets. The acquired business will now operate as Boardwalk Continuum Marketing LLC.

The deal brings together pipeline transportation, storage assets, and gas marketing operations under a unified platform. In practical terms, the company is positioning itself to offer bundled energy solutions—combining supply, logistics, and trading services for customers across the natural gas value chain.

At its core, Boardwalk Continuum Marketing functions as a gas marketing and trading business. It purchases natural gas from producers and delivers it to a wide range of end users, including utilities, industrial buyers, and retail energy providers. By integrating these capabilities with its existing pipeline and storage network, Boardwalk is aiming to create a more responsive, data-driven supply model.

The timing reflects broader shifts in energy demand. Growth in LNG exports and gas-fired power generation is reshaping North American gas flows, increasing the need for flexible supply and transportation solutions. Companies that can connect upstream production with downstream demand—while optimizing pricing and logistics—are gaining strategic advantage.

CEO Scott Hallam framed the acquisition as part of a longer-term transition. The company, he noted, is moving beyond asset ownership toward solution delivery. That distinction is becoming more relevant as energy markets grow more volatile and interconnected, requiring real-time decision-making across supply chains.

This is where marketing and trading capabilities play a larger role. Historically, pipeline operators focused on capacity and throughput. Today, the ability to manage contracts, forecast demand, and optimize flows using advanced analytics is just as critical. While not branded as a technology platform in the traditional SaaS sense, integrated energy marketing operations increasingly rely on digital systems that resemble enterprise data platforms—processing large volumes of market, pricing, and operational data.

Leadership continuity appears to be a priority. Pat Strange, who previously led Spire Marketing, will continue as president of the new entity. That decision suggests an effort to retain institutional knowledge and maintain customer relationships during the transition.

From a competitive standpoint, the move aligns with a wider industry trend. Energy companies are building vertically integrated platforms that combine physical assets with commercial and analytical capabilities. This mirrors transformations seen in adjacent sectors, where companies like Amazon and Microsoft have integrated infrastructure with data-driven services to create scalable ecosystems.

For enterprise customers, particularly large industrials and utilities, the value proposition is straightforward. A single provider capable of handling supply procurement, transportation logistics, and storage can reduce operational complexity and improve cost predictability. In markets where pricing can shift rapidly, access to integrated services also enables faster response times.

The rebranding to “Continuum” is more than symbolic. It reflects the continuous flow of natural gas across supply, transportation, storage, and demand nodes. More importantly, it signals a shift toward continuous optimization—where data, analytics, and trading decisions are tightly linked.

Industry data supports the strategic direction. According to International Energy Agency, global natural gas demand is expected to grow steadily through the decade, driven by power generation and industrial use. Meanwhile, McKinsey & Company notes that energy companies investing in integrated value chains and digital capabilities are better positioned to manage volatility and capture margin opportunities.

Still, execution will be key. Integrating a marketing business into an infrastructure-heavy organization requires alignment across systems, processes, and culture. Data integration, in particular, will be critical for delivering the real-time insights needed to optimize trading and logistics.

The deal also highlights a subtle but important shift in how energy companies are thinking about growth. Rather than expanding purely through new infrastructure projects, many are focusing on maximizing the value of existing assets by layering on commercial and analytical capabilities.

As energy markets become more dynamic—shaped by geopolitical factors, regulatory changes, and evolving demand patterns—companies that can operate across the full value chain will likely have an edge. Boardwalk’s acquisition of Spire Marketing, and the launch of Boardwalk Continuum Marketing, positions it squarely within that emerging model.

Market Landscape

The natural gas sector is undergoing a structural transformation, with companies increasingly blending physical infrastructure and commercial intelligence. Integrated platforms that combine pipelines, storage, and marketing are becoming the norm, particularly as LNG exports and power generation demand reshape global gas flows.

At the same time, digitalization is quietly influencing the sector. Trading desks and marketing units are adopting analytics platforms, forecasting tools, and data integration systems similar to those used in enterprise SaaS environments. This convergence of energy and data infrastructure is redefining competitive dynamics across the industry.

Top Insights

• Boardwalk’s acquisition integrates pipeline, storage, and marketing capabilities into a unified platform, enabling bundled natural gas solutions that improve efficiency and responsiveness for industrial and utility customers.
• The launch of Boardwalk Continuum Marketing reflects a broader shift toward data-driven energy trading, where analytics and real-time market insights play a central role in supply optimization.
• Growing demand from LNG exports and gas-fired power generation is driving the need for flexible, end-to-end gas supply solutions across North America.
• Leadership continuity under Pat Strange signals stability for existing customers while enabling expansion through Boardwalk’s larger infrastructure and market reach.
• The deal aligns with industry trends where energy companies are maximizing asset value by integrating commercial, logistical, and analytical capabilities into cohesive platforms.

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At Interpack 2026, Rockwell Automation Inc. is presenting a fully virtualized manufacturing line that illustrates how digital twin technology is reshaping industrial operations. The demonstration highlights how food and beverage manufacturers can design, simulate, and optimize entire production systems before physical deployment.

At a time when manufacturers are under pressure to increase efficiency while reducing operational risk, Rockwell Automation is using Interpack 2026 to make a case for digital-first production design. The company’s showcase centers on a virtual cookie production and packaging line—a detailed simulation that mirrors real-world industrial environments from raw material processing to final palletizing.

The concept is straightforward but powerful: a digital twin is a virtual replica of a physical system that allows manufacturers to test, validate, and optimize operations in a simulated environment. In Rockwell’s implementation, the entire production lifecycle—mixing, baking, cooling, packaging, and logistics—is modeled as a unified, data-connected system.

This approach addresses a long-standing challenge in manufacturing. Processing and packaging have traditionally been engineered as separate systems, often leading to inefficiencies, integration issues, and delayed time-to-market. By contrast, a digitally connected environment enables these functions to be designed as a single operation from the outset.

The demonstration is powered by Emulate3D, Rockwell’s digital twin platform, combined with hardware innovations such as iTRAK intelligent track system. Together, these technologies simulate real machines and production assets, allowing engineers to evaluate system performance under various conditions before installation.

In practical terms, virtualization changes how manufacturing projects are executed. Engineering teams can work in parallel rather than sequentially, validating mechanical, electrical, and automation designs simultaneously. This reduces costly rework, shortens commissioning timelines, and improves collaboration between engineering, operations, and maintenance teams.

The system also reflects a broader shift toward open, scalable architectures. Rockwell’s virtual production line integrates equipment from multiple OEMs—including processing, packaging, and palletizing providers—into a single digital environment. This multi-vendor approach mirrors real factory conditions, where interoperability is often a critical barrier to efficiency.

At the core of this integration is a unified data layer. Platforms like FactoryTalk Optix act as a central source of truth, enabling data from different machines and systems to be standardized and shared. This data foundation supports not only simulation but also real-time monitoring, quality control, and traceability.

Cybersecurity is another critical component. As manufacturing systems become more connected, the attack surface expands. Rockwell addresses this with solutions such as SecureOT platform, which embeds layered protections into the operational technology stack.

The implications extend beyond engineering efficiency. A connected, data-enabled production line creates the conditions for advanced analytics and AI applications. Once systems are unified, manufacturers can apply machine learning models to optimize throughput, predict maintenance needs, and improve product quality.

This convergence of digital twins, data platforms, and AI is part of a larger industrial transformation. Companies like Microsoft and Amazon have already demonstrated how cloud-based architectures can scale data-driven operations. In manufacturing, similar principles are now being applied to physical production environments.

According to Gartner, digital twins are expected to become a foundational element of industrial operations, with a growing percentage of large manufacturers adopting the technology to improve asset performance and operational resilience. Meanwhile, IDC estimates that investments in digital transformation technologies—including simulation and AI—will continue to grow at double-digit rates across industrial sectors.

For enterprise manufacturers, the value proposition is increasingly clear. Digital twins reduce risk by allowing systems to be tested before deployment. They accelerate time to value by shortening development cycles. And they enable continuous optimization by providing a real-time feedback loop between physical and digital environments.

Still, adoption is not without challenges. Building accurate digital twins requires high-quality data, standardized interfaces, and collaboration across multiple stakeholders. Many organizations also need to modernize legacy systems to fully integrate with digital platforms.

Rockwell’s demonstration at Interpack suggests that these barriers are gradually being addressed. By combining simulation, data architecture, and partner ecosystems into a cohesive offering, the company is positioning digital twins not as a niche tool, but as a central component of modern manufacturing strategy.

As the industry moves toward more connected and intelligent operations, the ability to design and optimize production systems in a virtual environment may soon become a baseline requirement rather than a competitive advantage.

Market Landscape

Digital twin technology is rapidly gaining traction across industrial sectors, particularly in food and beverage manufacturing where efficiency, traceability, and compliance are critical. Vendors are increasingly integrating simulation tools with data platforms and AI capabilities, creating unified environments that support the entire production lifecycle.

This trend aligns with the broader shift toward Industry 4.0, where physical operations are augmented by digital intelligence. As manufacturers adopt cloud, IoT, and advanced analytics, digital twins are emerging as the bridge between design, execution, and optimization.

Top Insights

• Rockwell Automation’s virtual production line demonstrates how digital twins can unify processing and packaging, enabling manufacturers to design, simulate, and optimize entire operations before deployment.
• The use of Emulate3D and iTRAK systems highlights how simulation and smart hardware integration can reduce engineering risk and accelerate commissioning timelines in complex manufacturing environments.
• Open, scalable data architectures powered by platforms like FactoryTalk Optix enable seamless integration of multi-vendor systems, improving interoperability and operational visibility.
• Embedded cybersecurity through SecureOT reflects the growing importance of protecting connected industrial systems as data-driven manufacturing expands.
• Digital twins are becoming central to Industry 4.0 strategies, enabling AI-driven optimization, predictive maintenance, and real-time performance monitoring across production lines.

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Wayward has launched Wayward Boost™, a new platform designed to transform partnership media—long considered effective but difficult to scale—into a measurable, performance-driven advertising channel. The move reflects a broader shift in digital marketing toward creator-led, trust-based media amplified through paid distribution.

Wayward’s introduction of Wayward Boost signals an attempt to formalize what has historically been an informal and fragmented segment of digital advertising. Partnership media—ads built around influencers, publishers, and third-party endorsements—has consistently delivered strong engagement, yet remains underutilized due to operational complexity and lack of infrastructure.

Wayward Boost aims to change that by providing a unified platform where brands can discover partners, create co-branded content, and deploy campaigns across paid media channels. In effect, the company is positioning partnership media as a repeatable, scalable alternative to traditional performance marketing.

At its core, the platform functions as an end-to-end partnership media infrastructure. It integrates partner discovery, content production, creative generation, and campaign distribution into a single workflow. This contrasts with the current landscape, where influencer marketing, affiliate programs, and publisher partnerships often operate in silos.

The platform’s differentiation lies in its ability to convert partner-generated content into performance advertising. Using AI-powered creative tools, Wayward Boost enables brands to transform influencer posts or editorial mentions into co-branded ad units that can be distributed across the open web, retail media networks, and owned digital properties.

This approach aligns with a key industry insight: consumers tend to trust recommendations from creators and publishers more than direct brand messaging. By layering paid distribution on top of that trust, Wayward is effectively merging influencer marketing with programmatic advertising principles.

Ali Marino, co-founder and CEO of Wayward, framed the launch as an infrastructure play rather than a feature update. The challenge, he noted, has never been proving the effectiveness of partnership media—it has been scaling it with the same precision and efficiency as other paid channels.

That distinction matters in an industry increasingly dominated by large ecosystems such as Google, Amazon, and Meta. These platforms have set expectations for measurement, targeting, and automation, forcing newer channels to match their performance standards.

Wayward Boost attempts to meet those expectations through its targeting layer, Wayward Boost Intelligence™, which applies audience segmentation and optimization techniques to partnership campaigns. This effectively brings data-driven decision-making into a space that has traditionally relied on manual coordination and qualitative assessment.

The platform also reflects the growing influence of retail media networks. By enabling campaigns that direct traffic to marketplaces such as Amazon and Walmart, Wayward is tapping into a fast-expanding segment of digital advertising where purchase intent is high and attribution is more direct.

From a market perspective, the timing is notable. According to Statista, global influencer marketing spend is expected to exceed $30 billion in the coming years, while Gartner reports that marketers are increasingly reallocating budgets toward channels that combine authenticity with measurable ROI.

Despite this growth, fragmentation remains a core challenge. Brands often manage influencer campaigns, affiliate programs, and publisher partnerships through separate tools and teams. This creates inefficiencies and limits the ability to scale campaigns across multiple collaborators.

Wayward Boost’s unified approach could address this gap, particularly for enterprise marketing teams seeking to consolidate their martech stacks. By bringing partnership workflows into a single platform, the company is effectively positioning itself alongside established marketing automation and adtech solutions.

The broader implication is the emergence of what Wayward calls “Partnership Media” as a distinct category. Rather than treating influencer marketing and affiliate programs as standalone tactics, the model integrates them into a performance marketing framework that emphasizes scalability, measurement, and cross-channel activation.

For brands, this could change how budgets are allocated. If partnership media can deliver consistent performance at scale, it may begin to compete more directly with search, social, and display advertising for spend.

For creators and publishers, the model introduces new monetization opportunities. By connecting their content to paid media budgets, Wayward Boost enables them to participate more directly in performance-driven campaigns, rather than relying solely on fixed sponsorship deals.

Still, questions remain around adoption. Success will depend on how effectively Wayward can integrate with existing adtech ecosystems and demonstrate consistent ROI across different industries. The platform’s patent-pending status also suggests that differentiation will be a key factor as competitors move into the space.

What is clear is that the line between organic and paid media continues to blur. As the creator economy matures, platforms that can bridge authenticity and scale are likely to play a larger role in the future of digital advertising.

Market Landscape

The rise of partnership media reflects a broader transformation in advertising, where trust and authenticity are becoming as important as reach and frequency. Influencer marketing, affiliate programs, and publisher collaborations are converging into unified strategies that prioritize performance and accountability.

At the same time, the dominance of walled gardens is pushing brands to explore alternatives across the open internet. Platforms that can offer scalable, data-driven solutions outside of these ecosystems are gaining attention, particularly as marketers seek diversification and greater control over their media investments.

Top Insights

• Wayward Boost introduces a unified infrastructure that transforms influencer and publisher collaborations into scalable, performance-driven advertising campaigns across paid and owned media channels.
• The platform leverages AI-powered creative and audience targeting to bring measurement, automation, and efficiency to historically fragmented partnership marketing workflows.
• Integration with retail media ecosystems like Amazon positions partnership media as a high-intent performance channel capable of driving measurable conversions.
• The launch reflects growing demand for alternatives to walled gardens, as brands seek scalable advertising solutions across the open internet.
• By merging authenticity with paid distribution, Wayward is defining “Partnership Media” as a new category within the evolving adtech and martech landscape.

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Redwood Software is set to showcase its agentic orchestration platform at SAP Sapphire 2026, positioning automation as the missing execution layer in enterprise AI adoption. The company’s RunMyJobs platform aims to help organizations operationalize AI across complex SAP and hybrid cloud environments.

As enterprises accelerate investments in artificial intelligence and cloud platforms, a persistent gap remains between experimentation and execution. Redwood Software is using SAP Sapphire 2026 to address that gap, introducing what it describes as an “agentic orchestration” approach—designed to move AI from isolated use cases into fully operational business processes.

At the center of the announcement is RunMyJobs by Redwood, which acts as an execution layer across hybrid cloud systems. The platform enables organizations to coordinate workflows between AI agents, enterprise applications, and data systems—essentially turning AI outputs into actionable, governed processes.

The concept of agentic orchestration refers to the ability of AI agents to not only generate insights but also execute tasks autonomously within defined enterprise controls. In Redwood’s model, this includes multi-agent coordination, where different AI systems communicate and collaborate to complete complex workflows.

This capability is becoming increasingly relevant as enterprises adopt a growing ecosystem of SAP technologies, including SAP Business Technology Platform, SAP Cloud ERP, and AI-driven tools like SAP Joule. While these platforms provide powerful capabilities, integrating them into cohesive, end-to-end processes remains a challenge.

Redwood’s approach focuses on bridging that integration gap. By exposing existing business logic to AI systems through open protocols such as Model Context Protocol (MCP), the platform allows enterprises to connect large language models and third-party agents directly to operational workflows. This creates a structured pathway for AI-driven automation while maintaining enterprise-grade governance.

Governance is a central theme in Redwood’s positioning. According to Gartner, more than 70% of IT leaders cite concerns around the proliferation of AI agents, particularly in areas such as security, compliance, and auditability. Redwood’s platform addresses this by embedding observability, policy controls, and monitoring into every stage of execution.

This is particularly important for enterprises operating in regulated industries or managing mission-critical systems. Without proper oversight, autonomous AI workflows can introduce risk rather than efficiency. Redwood’s orchestration layer is designed to ensure that AI-driven actions remain transparent, traceable, and aligned with business rules.

Another key aspect of the platform is its integration with SAP’s broader ecosystem. As an SAP-endorsed application for workload automation, RunMyJobs is deeply embedded within SAP environments, enabling capabilities such as automation actions within SAP Business Accelerator Hub and enhanced observability through SAP Cloud ALM.

This level of integration positions Redwood as more than just an automation vendor. It places the company within the broader enterprise infrastructure stack, alongside major ecosystems like Microsoft and Google, where orchestration layers are becoming critical for managing distributed systems.

The platform also addresses a common pain point in SAP transformations: technical debt. As organizations migrate to cloud-based ERP systems through initiatives like RISE with SAP, they often encounter legacy integrations, redundant infrastructure, and fragmented workflows. Redwood’s unified orchestration model aims to simplify these transitions by standardizing automation and reducing the need for custom workarounds.

From a market perspective, the timing aligns with broader enterprise trends. According to IDC, global spending on AI and automation technologies continues to grow at a double-digit pace, driven by demand for operational efficiency and digital transformation. However, the ability to operationalize AI at scale remains a key differentiator between early adopters and mature organizations.

Redwood’s focus on agentic execution reflects a shift in how enterprises are thinking about AI. Rather than viewing it as a standalone capability, organizations are increasingly treating AI as part of a larger system that includes data pipelines, business logic, and automation frameworks.

For enterprise IT and marketing operations teams—particularly those managing complex customer data, campaign automation, and analytics workflows—the implications are significant. Agentic orchestration could enable more dynamic, real-time decision-making across systems, reducing manual intervention and improving responsiveness.

Still, adoption will depend on execution. Integrating AI agents with legacy systems, ensuring data consistency, and maintaining governance at scale are non-trivial challenges. Platforms like RunMyJobs will need to demonstrate not only technical capability but also measurable business outcomes.

Redwood’s showcase at SAP Sapphire suggests that the next phase of enterprise AI will be defined less by model innovation and more by orchestration. In that context, the ability to connect, govern, and execute across systems may become the foundation of the autonomous enterprise.

Market Landscape

Agentic orchestration is emerging as a critical layer in enterprise technology stacks, sitting between AI models and business operations. As organizations adopt multiple AI tools and cloud platforms, the need for coordination, governance, and execution is becoming more pronounced.

Vendors across the ecosystem are moving to address this gap, integrating orchestration capabilities into cloud, automation, and data platforms. This trend reflects a broader shift toward autonomous enterprise models, where workflows are increasingly driven by AI but governed by structured systems.

Top Insights

• Redwood Software introduces agentic orchestration to operationalize AI across SAP environments, enabling autonomous workflows with built-in governance, observability, and enterprise-grade security controls.
• RunMyJobs acts as an execution layer connecting AI agents, business logic, and enterprise systems, addressing the gap between AI experimentation and real-world operational deployment.
• Deep integration with SAP platforms like BTP, Cloud ERP, and Joule positions Redwood within the core enterprise stack, supporting complex hybrid cloud and data environments.
• Governance remains a key challenge, with over 70% of IT leaders concerned about AI agent proliferation, making controlled orchestration a critical enterprise requirement.
• The platform aims to reduce technical debt in SAP transformations by standardizing automation and enabling unified orchestration across SAP and non-SAP systems.

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