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Three martech moves reshaping how enterprise teams buy, automate, and engage in 2026

Outreach, Tenon, and Sprinklr have announced major product developments that will influence how enterprise teams approach buying, automation, and customer engagement. These updates aim to enhance the efficiency of revenue operations, IT processes, and customer experience management. By adopting these advancements, businesses can streamline their operations and improve their interactions with customers.

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By MarketScale Newsroom · OutreachTenonSprinklrServicenow
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Three martech moves reshaping how enterprise teams buy, automate, and engage in 2026

Key takeaways

01

Outreach, Tenon, and Sprinklr unveiled new product developments impacting buying, automation, and engagement tactics.

02

The updates are designed to improve revenue operations, IT efficiencies, and customer experience strategies.

03

Adopting these new technologies can help businesses streamline operations and improve customer interactions.

Three martech vendors moved on the same day this week, each targeting a different layer of the enterprise stack. Outreach introduced an AI Maturity Model for sales organizations, Tenon embedded marketing automation directly into the ServiceNow AI Platform, and Sprinklr released AI capabilities built around real-time customer action. The announcements, all dated July 16, 2026, arrived via BusinessWire and were reported by MarTech Cube.

Outreach gives revenue ops a maturity benchmark

Outreach, the sales execution platform, introduced what it calls an AI Maturity Model, a structured framework intended to help sales and revenue operations teams assess where they stand on AI adoption. The underlying premise is practical: most organizations have deployed some AI-assisted tooling, but few have a consistent way to measure whether that deployment is delivering results or how to move to the next stage.

For a VP of Sales Operations or a CRO evaluating their current stack, the model offers something vendors rarely provide: a diagnostic lens rather than just a product pitch. The ability to benchmark current AI use against a defined progression matters especially for teams that have accumulated point solutions and need to decide what to consolidate, extend, or retire.

A maturity model shifts the conversation from 'are we using AI' to 'how well is AI working for us', and that is a question revenue ops teams have needed a shared language for.

Outreach has not published the full model publicly as of this writing, but the announcement signals the company is positioning itself as a strategic partner in AI adoption planning, not just a tooling vendor. That framing is increasingly common among enterprise software players competing for expansion budgets in a market where procurement teams are scrutinizing AI ROI more carefully.

Tenon brings marketing automation into ServiceNow's platform

Tenon's announcement is the most structurally significant of the three for IT and enterprise architecture teams. The company is expanding its marketing automation capabilities natively into the ServiceNow AI Platform, meaning marketing workflows can now live inside an environment that IT departments already provision, govern, and audit.

The operational implication is real. Marketing automation has historically required its own integrations, credentials, and governance processes, a friction point for IT teams managing sprawling tool inventories. By running inside ServiceNow, Tenon's capabilities inherit that platform's access controls, workflow logic, and AI infrastructure. For organizations that have already standardized on ServiceNow for IT service management or HR workflows, adding marketing automation in the same environment is a materially different proposition than deploying a standalone martech platform.

Procurement teams evaluating marketing automation renewals or new vendor selections should treat this integration as a legitimate consolidation signal. If ServiceNow is already in the stack, Tenon's expansion reduces the integration burden and potentially the vendor count.

Sprinklr targets the moment of customer interaction

Sprinklr's new AI capability targets a specific operational gap in customer experience management: the lag between when a customer signal appears and when a company can act on it. The platform's updated offering is designed to surface insights and trigger responses in real time, during an active customer interaction rather than in post-session analytics.

For contact center directors and CX operations leads, this distinction matters. Batch analysis has been the default in most enterprise CX stacks; real-time AI surfaces the same information while an agent is still engaged, or before a customer reaches an agent at all. The practical use cases include routing adjustments, live agent prompts, and automated escalation triggers, all areas where timing determines outcome quality.

Sprinklr already operates across a unified customer experience platform spanning social, messaging, and contact center channels. Adding real-time AI action to that footprint means the tool can close the loop between signal detection and response within a single platform, which reduces handoff points and the latency that comes with them.

What this means for your team

  • Revenue ops and sales leadership: ask your Outreach rep for access to the AI Maturity Model and run it against your current toolset before your next QBR. Use the output to frame any AI expansion conversations internally.
  • IT and enterprise architecture: if ServiceNow is already in your environment, Tenon's native integration warrants a direct evaluation. Map which marketing automation capabilities you currently run outside ServiceNow and whether they could consolidate.
  • CX and contact center operations: audit your current AI deployment to determine where analysis is batch versus real-time. Sprinklr's update is worth a scoping call if your team is losing response quality to lag between signal detection and agent action.
  • Procurement: all three announcements indicate platform vendors are deepening AI integration rather than releasing standalone tools. When reviewing martech contracts this cycle, weight platform depth and AI governance capability alongside feature lists.

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