Is your martech stack ready for AI agents?

Fragmented martech stacks are driving vendor consolidation

68% of CIOs plan to consolidate vendors, according to ADAPT’s CIO Edge Survey 2025. Many are targeting a 20% reduction. The number points to a structural problem in enterprise technology: companies have accumulated more systems than they can efficiently connect, govern and operate.

Most martech stacks developed through incremental buying over two decades. Marketing automation solved campaign needs. Content management supported websites. Customer data platforms unified selected customer records. Other products handled analytics, digital assets, ecommerce and customer feedback. Sales, marketing, service and IT often made these decisions at different times and for different priorities.

Many of those investments delivered useful capabilities. The cumulative architecture is the problem. Product categories expanded and began to overlap. Several applications can now generate content, automate workflows, analyze customers or provide conversational interfaces. At the same time, important customer data and business processes remain spread across separate systems.

Every additional platform also creates integration work. Customer information must move through APIs, connectors, middleware or custom code. Cloud services made these connections easier to build, but enterprises continued adding systems. Integration capacity did not remove the underlying complexity.

The constraint is now the ability to move trusted data and actions across the business. A fragmented stack makes that harder. It also increases administration, security reviews, governance, employee training and maintenance. Executives therefore need to evaluate the full operating cost of each system and the complexity it adds to the wider architecture.

Vendor consolidation should focus on removing duplicated capability and establishing clear systems of record. A system of record is the authoritative platform for a defined set of business data or processes. Enterprises need clear ownership for customer data, content, engagement and other critical functions. Specialist products still deserve a place when their differentiated value exceeds the integration and governance cost they create.

This changes the executive objective. A lower vendor count can reduce complexity, but the stronger measure is whether customer information and workflows can move reliably between the platforms that remain. That capability becomes especially important as enterprises deploy AI across the customer journey.

AI requires a data- and AI-centric martech architecture

AI changes the economics of martech consolidation. Traditional rationalization programs focused on software spending, redundant licenses and vendor management. Those benefits remain valuable. AI adds a more fundamental requirement: systems must provide machines with consistent access to data, context, rules and actions.

Recommendation engines, personalization systems, conversational assistants and AI agents depend on accurate customer profiles and recent interaction history. They also need business rules and current context. When those inputs sit across disconnected systems, AI has an incomplete view of the customer. The quality of its recommendations and decisions can then deteriorate.

AI agents raise the requirement further because they can execute business processes. An agent may need to retrieve customer information, interpret an account’s status, apply company rules and trigger an action in another platform. Reliable execution requires consistent identities, permissions and interfaces across those systems.

This is pushing enterprises toward a data- and AI-centric architecture. Individual applications become components of a wider operating environment. The executive priority becomes coordination across marketing, sales, commerce, customer service and analytics. Customer context must remain usable as it moves between those functions.

Platform evaluation must change with it. Feature lists and embedded AI assistants provide limited evidence of architectural value. Executives should examine whether a platform can expose its data and permitted actions through stable interfaces, participate in automated workflows and support enterprise governance. Data quality, interoperability, security and architectural flexibility become core purchasing criteria.

The rapid spread of copilots and agents makes this work urgent. Enterprises can have separate AI systems operating in marketing, sales, service, commerce and analytics. These systems may use different data, permissions and business rules. Without coordination, they can generate conflicting recommendations or make inconsistent decisions about the same customer.

AI therefore gives consolidation a clear technical goal: create an environment where trusted customer context and governed actions can move across systems. This does not require placing every capability with one vendor. It requires defining authoritative data sources, standardizing access and permissions, and choosing platforms that can participate cleanly in enterprise workflows.

For C-suite leaders, the resulting investment test is practical. Each platform should demonstrate measurable business value while supporting connected data, AI workloads and governance. Systems that duplicate generic functions become stronger consolidation candidates. Platforms that provide differentiated capabilities and integrate cleanly into the wider architecture remain strategic assets.

AI agents expose the weaknesses of disconnected systems

AI agents put enterprise integration under greater pressure because they need to work across systems. An agent may read a customer profile, check recent interactions, apply business rules, assess permissions and trigger an action. Each step depends on accurate data and controlled access.

Fragmented martech stacks make this difficult. Customer identity may sit in a customer data platform, purchase history in an ecommerce system, campaign activity in marketing automation and service history in a CRM or support platform. An AI agent needs a consistent view across these systems to make reliable decisions. Conflicting records or delayed updates can change the outcome.

Permissions create another constraint. Giving an agent access to information is different from authorizing it to take action. An enterprise needs to define which systems an agent can access, which records it can change, what actions it can execute and when human approval is required. These controls become harder to manage as the number of platforms grows.

Sandip Patel, enterprise AI and cloud security expert and senior cloud solution architect at Microsoft, described the problem clearly: “AI agents need to read and act across the entire customer journey, and they break the moment data and permissions are scattered across 40 point tools. Every fragmented tool is a surface an agent either cannot see or cannot be trusted to touch.”

The growth of multiple enterprise agents adds another challenge. Marketing, sales, commerce, service and analytics teams can each deploy their own copilots or agents. These systems may use different customer records and follow different rules. They can then produce conflicting recommendations or execute poorly coordinated actions.

Executives should therefore treat agent readiness as an architecture question. A platform needs clean ways to expose its data and permitted actions to an AI orchestration layer, which coordinates work across systems. Identity management, access controls, data quality and business rules must also remain consistent enough for the agent to operate safely.

This provides a practical test for consolidation. Systems that contain essential data or unique capabilities should become easier for governed AI systems to access. Platforms that duplicate functions while creating additional data and permission boundaries carry a growing operational cost. AI agents make that cost much easier to see.

AI governance is becoming a consolidation driver

AI governance becomes more important as enterprises give AI systems greater responsibility. A content assistant may generate a draft for review. An AI agent can read customer data and execute actions across production systems. That capability creates a stronger requirement for consistent security, privacy, data quality and oversight.

The core governance issue is control over decisions and actions. Enterprises need to know what data an AI system can use, which actions it can perform, which policies apply and who remains accountable. They also need controls for regulatory compliance and human review where the risk requires it.

Fragmentation increases the effort required to enforce these controls. Every additional platform introduces its own permissions, security model, data structures and administrative processes. An enterprise running many point solutions must reconcile these differences before an AI agent can operate safely across them.

Consolidation can reduce this governance burden. Fewer overlapping systems can simplify identity management, access controls and policy enforcement. It can also make customer data easier to manage consistently. The strongest architecture still requires enterprise-wide governance standards because multiple platforms will continue to support specialized business functions.

Governance should therefore shape AI architecture before broad agent deployment. Executives need clear standards for data access, security, privacy, compliance and human oversight. Teams also need defined authority for approving AI actions and managing changes to those permissions.

Governance has a direct economic effect as well. Each platform requires administration, integration, security reviews and AI controls. These costs accumulate alongside software licenses. A tool with limited differentiated value becomes harder to justify when its full governance and operational burden is included in the decision.

For C-suite leaders, consolidation and governance should be addressed together. The target is a controlled environment in which AI can access trusted information and execute authorized actions across customer-facing systems. Enterprises that establish those controls early will have a stronger foundation for scaling agents while keeping customer data and business decisions under clear organizational control.

SaaS costs and operational overhead are driving consolidation

Software licenses are only one part of martech spending. Every platform also requires integration, administration, security reviews, governance, maintenance, employee training and adoption support. These costs continue throughout the product’s life and increase as the stack becomes more fragmented.

Years of SaaS purchasing have left many enterprises with overlapping capabilities. Multiple systems may now support content generation, analytics, workflow automation, conversational interfaces or customer insights. Keeping those products creates recurring expenditure and operational work even when their functions have become available elsewhere in the stack.

Alys Reynders, CMO at Quickbase, sees SaaS spending as a central force behind consolidation. She told CMSWire: “Martech consolidation has long been a story of convenience, and the potential for operational simplicity to deliver results at lower costs. While AI has become a key component in delivering simplicity, it’s not the underlying cause of the trend. The dominant share of pressure stems from bloated spending throughout the SaaS landscape.”

AI adds to this financial pressure because each platform creates another environment that must be prepared for AI use. Enterprises need to determine how agents access its data, what permissions apply, which actions they can execute and how those activities are governed. Supporting AI across dozens of separate products can therefore increase the cost of integration, security and control.

Executives should assess the total cost of ownership at platform level. That calculation should include subscriptions, integration work, administration, security, governance, training and maintenance. It should also account for duplicated functionality elsewhere in the stack. A product’s business contribution needs to justify this full operating burden.

This makes capability mapping an important part of consolidation. Leaders should identify where products perform similar work, which system owns authoritative data and which capabilities generate measurable business value. Generic functionality can increasingly be concentrated in broader strategic platforms when doing so reduces complexity without weakening critical capabilities.

The objective is disciplined spending tied to architectural value. Removing redundant systems can lower recurring costs while reducing the number of integrations, permissions and governance processes the enterprise must maintain. That creates a simpler environment for both employees and AI systems.

“Decision-grade” platforms will earn their place in the stack

AI is raising the standard enterprises use to decide which platforms remain strategic. Basic functionality has become easier to replicate as vendors add similar AI features for content generation, predictive analytics, automation, conversational interfaces and customer insights.

This changes the value of differentiation. A platform becomes strategically important when it contributes information or capabilities that improve consequential customer and business decisions. Proprietary data, specialized domain knowledge and measurable outcomes can provide that differentiation.

Jessica Arredondo Murphy, co-founder and CEO at True Fit, describes these systems as “decision-grade.” She told CMSWire that “AI is raising the bar for what belongs in the stack.” Her assessment emphasizes trusted proprietary data, measurable outcomes and specialized intelligence as characteristics that can make a technology a durable strategic investment.

For executives, the distinction matters because AI can reduce the value of generic software features. If several existing platforms can perform a similar task, maintaining a dedicated product for that function becomes more difficult to justify. A specialist platform has a stronger case when its unique data or expertise materially improves an important decision.

Measurable outcomes are central to this assessment. A platform should connect its capabilities to customer or financial impact where possible. That might include better conversion, retention, customer engagement, operating efficiency or decision quality, depending on the system’s purpose. The relevant metric should reflect the business outcome the platform is expected to influence.

Data quality also matters. AI systems depend on reliable inputs. A platform with differentiated data can become more valuable when that information improves decisions elsewhere in the enterprise. That value increases further when the data can move through governed interfaces into analytics, automation and AI workflows.

Technology leaders should therefore classify platforms according to their strategic role. Some will serve as authoritative systems of record. Others will provide specialized intelligence or execution capabilities. Products with duplicated features and limited differentiated data become stronger candidates for consolidation.

This approach gives consolidation a business test rather than an arbitrary target for reducing vendor count. The platforms that remain should contribute measurable value, trusted data or specialized capability while fitting into the wider architecture. In an AI-driven stack, those qualities provide a stronger basis for investment than an expanding list of features.

AI integration and orchestration should drive platform decisions

AI changes how enterprises should evaluate martech platforms. An embedded AI feature can improve productivity within one application. Enterprise value depends more heavily on whether the platform can expose its data and permitted actions to systems operating across the wider customer journey.

This requires interoperability. A platform should provide stable APIs or equivalent interfaces that allow approved systems to retrieve data, initiate actions and participate in automated workflows. It also needs appropriate identity, permission and governance controls. These capabilities determine whether AI agents can use the platform safely as part of a coordinated process.

Kuber Sharma, senior director of product marketing at UiPath, told CMSWire: “The question I’d start with is not whether a tool has an AI feature. That’s marketing. The real question is whether it can expose its data and actions to an AI layer cleanly. Can it serve as a node in an orchestrated workflow? If yes, integrate. If it’s a closed environment that only works through its own interface, you’re looking at eventual replacement.”

Orchestration is important because customer processes routinely cross application boundaries. A marketing interaction can affect a sales opportunity, commerce transaction or service request. An AI agent working across these processes needs access to current customer context and a defined set of actions in each relevant system.

Closed platforms create a structural constraint. When data and functions are difficult to access outside the vendor’s interface, enterprises need additional integration work or manual processes. This increases operating costs and restricts how easily the organization can introduce new AI systems. Over time, these restrictions can become a reason to replace the platform.

Executives should therefore make interoperability a formal procurement and retention criterion. Evaluation should cover data accessibility, API quality, event support, identity controls, permission models, workflow integration and governance. Architectural flexibility matters because AI requirements will continue to change.

This approach also changes how leaders think about AI readiness. The strongest platform does not need to perform every AI task itself. It needs to make its relevant data and actions available within a secure, governed enterprise architecture. That gives organizations more freedom to change AI models, orchestration technologies and workflows without redesigning the entire stack.

Consolidation can increase vendor lock-in

Consolidation can improve data consistency, simplify governance and reduce the number of integrations an enterprise must maintain. It can also concentrate customer data, workflows and AI capabilities in fewer platforms. That concentration creates greater dependence on individual vendors.

The risk grows as more business processes become tied to one provider’s architecture. Pricing changes, product decisions, reduced interoperability or shifts in the vendor’s AI strategy can then have a wider enterprise impact. Moving away can require data migration, workflow redesign, retraining and replacement of integrations.

This creates an important executive tradeoff. Consolidating duplicated systems can deliver clear operational gains. Concentrating too many critical capabilities with one provider can raise long-term switching costs and reduce architectural flexibility. Each consolidation decision should account for both effects.

Wanda Cadigan, CMO at WandaGTM, told CMSWire: “Every tool promises value. Every tool also creates a tax.” She identifies integration, governance, maintenance and adoption as recurring overhead associated with each additional platform. The executive task is to determine whether a product’s unique contribution justifies that cost.

The same economic discipline should apply to large strategic vendors. A broad platform can eliminate several smaller systems and reduce integration work. Its long-term value also depends on data portability, interoperability and the enterprise’s ability to introduce specialist technology when business requirements change.

Leaders should therefore examine switching costs before consolidating further. Contract terms, data export options, API access, identity architecture and the portability of workflows all affect future flexibility. Critical customer data should remain accessible in usable formats, and enterprise processes should avoid unnecessary dependence on proprietary interfaces where practical.

AI makes these decisions more consequential. As agents become connected to customer records and business processes, changing an underlying platform can affect data access, permissions and automated actions across multiple functions. Architectural choices made during consolidation can therefore shape future AI options.

The objective is controlled simplification. Enterprises can reduce duplicated technology while preserving the ability to integrate new capabilities and change strategic vendors when required. That balance allows consolidation to improve today’s operating model without unnecessarily restricting tomorrow’s technology choices.

The 2026 martech stack will be defined by connected customer context

The defining measure of a modern martech stack is how effectively its platforms share trusted customer context. Platform count remains useful for cost management. AI readiness depends more directly on whether data, content, business rules and permitted actions can move reliably across the enterprise.

This requires clear systems of record. Enterprises need to identify which platforms hold authoritative customer data, manage content, control engagement and support AI-driven decisions. Other applications should consume and update that information through governed interfaces. Clear ownership reduces conflicting customer records and gives AI systems more reliable inputs.

Customer context also needs to move across organizational boundaries. Marketing activity can affect a sales conversation. A commerce transaction can change the next service interaction. An unresolved support case can influence the message a customer should receive. Connecting marketing, sales, commerce, service and analytics allows each function to work from relevant and current information.

AI makes this coordination more important. Agents, recommendation engines and automated decision systems depend on customer identity, interaction history, content and business rules. When those inputs remain consistent across systems, AI can make more reliable recommendations and execute better-coordinated actions. Poor data quality or delayed synchronization weakens that capability regardless of how advanced the AI model is.

Point solutions will continue to have a role. Specialized software can justify its position when it provides proprietary data, domain expertise or a capability with measurable business value. Its architectural obligations will become stricter. It should integrate cleanly, respect enterprise permissions and make relevant information available to governed workflows.

This means enterprises should design around shared context rather than pursue the smallest possible application portfolio. Some organizations will still require a substantial number of systems because their customer journeys, markets and operating models are complex. The critical question is whether those systems function as a coordinated environment.

Executives should prioritize a small set of architectural requirements: authoritative data ownership, consistent customer identity, governed access, reliable interfaces and portable workflows. These foundations give enterprises more freedom to add or replace AI capabilities as technology changes.

The 2026 martech stack will therefore become more intentional. Consolidation can remove duplication and reduce operating costs. Connected architecture creates the larger strategic benefit. Enterprises that can deliver trusted customer context across marketing, sales, commerce, service and analytics will be better prepared to scale AI agents, automation and decision systems as customer expectations and AI capabilities evolve.

Final thoughts

AI is forcing a more useful question about martech. The issue is no longer how many platforms an enterprise owns. Leaders need to know whether those platforms can share trusted data, expose governed actions and support AI across the customer journey.

The 68% of CIOs planning vendor consolidation have an opportunity to address more than SaaS spending. Remove duplicated capabilities. Establish clear systems of record. Keep specialist platforms that provide measurable value. Make interoperability, data quality, permissions and governance core requirements for every platform that remains.

AI agents make these decisions more urgent because they depend on connected systems to act reliably. A fragmented permission model or inconsistent customer record can become a direct constraint on automation. At the same time, excessive consolidation can increase vendor lock-in and limit future choices.

The right target is a controlled, flexible architecture. Every platform should justify its operating cost, contribute distinctive value and work cleanly within enterprise workflows. Companies that make those choices now will have a stronger foundation for scaling AI agents as their capabilities and responsibilities expand.