GTM + AI in 2026: What CMOs Predicted at Hard Skill Exchange’s Agentic Singularity Summit

At the HSE’s Agentic Singularity Summit, the CMO Predictions Panel agreed that by 2026, AI will change how GTM teams operate, not just the tactics they use. The panel explained that progress in go-to-market engineering, content engineering, unified data foundations, and coordinated agents will separate teams that grow consistently from those that only experiment.

Jonathan Metrick, an experienced CMO and growth leader, led a session with experts from marketing, AI, revenue enablement, and revenue intelligence. While their predictions on hiring, channels, data, and automation varied, they all agreed that the next stage of AI adoption will be driven by operating structure rather than new tools alone.

‍Watch the CMO Predictions Panel from HSE’s Agentic Singularity Summit

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2026 Is the Year GTM Stops “Trying AI”

During the discussion, it became clear that most GTM teams are no longer in the early experimentation phase. Many now use generative AI for content creation, research, analysis, and automation. Some teams have deployed agents in limited ways, and many are actively testing new workflows across marketing and sales.

However, a large-scale rethinking of how the go-to-market work is designed has not yet happened. Panelists emphasized that simply adding AI to existing workflows delivers only incremental gains. The real opportunity in 2026 is to rethink GTM as a system, one that can be measured, managed, and continuously improved. Successful teams will not be those with the most AI tools, but those that redesign how decisions are made and executed across the funnel.

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Go-to-Market Engineering Becomes a Core Operating Capability

One of the strongest themes was the rise of go-to-market engineering. While the term itself is relatively new, the underlying need has existed for a long time.

Historically, GTM execution relied on manual coordination, repeated handoffs, and human interpretation of fragmented data. AI changes this model by enabling automated workflows, more consistent decision-making, and real-time coordination across teams. As a result, GTM can now be intentionally designed rather than assembled reactively.

This capability does not belong to a single team. It spans marketing, RevOps, sales, and customer success. Marketers, in particular, will need to understand how workflows are built and how AI systems behave, not to replace creativity, but to accelerate it.

This shift does not require marketers to become engineers, but it does raise expectations. Creativity without execution speed stalls. Speed without system understanding breaks at scale. GTM engineering connects these two realities.

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The Return of the Generalist Marketer

Jonathan Metrick opened the panel by predicting the return of the generalist marketer, a theme that resonated throughout the discussion.

As AI reshapes channels, formats, and workflows simultaneously, narrow specialization becomes risky. Roles focused on a single tool or channel struggle to keep up as the stack evolves rapidly. In contrast, generalists, those who can move across strategy, execution, experimentation, and measurement, help teams maintain momentum.

This shift does not reduce the importance of specialists. Instead, it changes the balance. Specialists provide depth and acceleration, while generalists provide continuity and cohesion. Together, they allow GTM teams to adapt without constant reorganization.

In 2026, adaptability itself becomes a competitive advantage.

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ChatGPT, Answer Engines, and the Shift Toward Content Engineering

One of the boldest predictions was that ChatGPT and other large language models will become primary surfaces for discovery and influence.

Panelists expect AI platforms to introduce advertising models while also expanding premium, ad-free experiences for power users. This creates a fundamentally different GTM landscape, one where buyers increasingly interact with synthesized answers rather than lists of links.

This shift elevates a discipline discussed repeatedly during the session: content engineering.

Content engineering treats content as something designed for both humans and machines. The focus moves away from volume and toward clarity, structure, and information density. Instead of asking, “What should we publish next?”, teams begin asking, “What do AI systems need in order to explain our category and value accurately?”

In this model,content becomes foundational infrastructure for AI-driven discovery. It must be consistent, explainable, and continuously refreshed as buyer questions evolve.

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In-Housing Accelerates as Speed and Context Matter More Than Scale

Another key prediction was a gradual shift away from heavy reliance on agencies for core GTM execution.

This is not a critique of agencies, but a reflection of how AI changes execution dynamics. As cycle times compress, coordination overhead increases. In-house teams that deeply understand their product, data, and customers often move faster than externally coordinated teams.

Panelists described a growing pattern where a small number of highly capable in-house operators, supported by AI workflows, outperform larger outsourced models. Agencies will continue to play an important role, but increasingly at the edges rather than at the center. Speed is the primary driver of this shift. When environments change quickly, proximity to context becomes a strategic advantage.

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Trust Becomes the Primary Constraint on AI ROI

Despite widespread enthusiasm for AI, the panel identified trust as the biggest constraint on its impact.

Teams hesitate to rely on AI outputs when accuracy, privacy, or explainability is unclear. This slows adoption and can push usage into unofficial tools, increasing risk rather than reducing it. By 2026, CMOs will need to own not just growth outcomes, but also trust in AI systems. This means demonstrating that tools are safe, outputs are reliable, and decision logic can be explained.

AI that is not trusted does not compound. It fragments workflows and erodes confidence.

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Unified Data Foundations Enable Agentic GTM

As agentic AI expands across the GTM stack, data fragmentation becomes a critical point of failure.

Unlike humans, agents rely entirely on the context they are given. When marketing, sales, and customer success operate on disconnected data models, agents act on incomplete information. This leads to inconsistent messaging, poor prioritization, and operational noise.

The panel stressed that by 2026, organizations will need to invest in shared context, such as identity resolution, signal interpretation, and consistent definitions of funnel stages and outcomes. This does not require replacing every system, but it does require alignment.

Without a unified data foundation, agents amplify confusion rather than insight.

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From Linear Funnels to Continuous Customer Orchestration

Several panelists questioned the relevance of linear funnel models in a world driven by real-time signals.

Buyers no longer move step by step from awareness to purchase. They express intent continuously through conversations, product usage, feedback, sentiment, and engagement across channels. AI makes it possible to respond dynamically, but only if teams share information and work together.

The panel described a model of continuous customer orchestration, where insights flow seamlessly across marketing, sales, product, and customer success. Teams act on signals immediately, rather than waiting for delayed reports.

In this approach, customer insight becomes part of the operating system, not an afterthought.

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The Risk of Uncoordinated Agents

As more AI agents are deployed, the panel warned about a new risk: uncoordinated automation.

Without shared context, guardrails, and orchestration, agents may work at cross-purposes—triggering excessive outreach, contradicting brand messaging, or acting on the wrong signals. Preventing this requires more than security controls.

It requires business-level guardrails, clear definitions of success, and systems that allow humans to monitor and intervene when necessary. The future is not about deploying more agents, but about deploying agents that work together.

What Will Not Change in 2026

Even with significant change, the panel agreed on several constants:

• Authentic customer experience will matter even more as synthetic content increases
• Brand, taste, and human connection remain differentiators
• Strong marketing and sales fundamentals still win
• AI amplifies strengths but does not fix weak foundations

In-person interaction, honest storytelling, and real customer outcomes will continue to anchor GTM success.

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What CMOs Should Internalize Heading Into 2026

The panel’s predictions point to clear priorities for GTM leaders:

• Redesign workflows around coordination, not just automation
• Invest in shared context and data trust before scaling agents
• Build GTM engineering capability across teams, not in silos
• Treat content as AI-readable infrastructure, not just output
• Start early- learning velocity compounds faster than perfection

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Final Takeaway

The core message from the Agentic Singularity Summit was unambiguous. 2026 will not reward teams with the most AI. It will reward teams with the most coordinated AI, grounded in shared truth, governed by clear rules, and embedded into how revenue is actually generated. The winners will not wait for certainty. They will redesign their operating models, test aggressively, and compound advantage while others are still deciding.

In the next phase of GTM, AI will not be an add-on. It will be the foundation.