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What new evaluation criteria should CIOs use for AI?ready HR and finance platforms?

The table-stakes tier hasn¡¯t changed, but it¡¯s no longer enough.

When 300 CIOs were asked which capabilities matter most when evaluating an ERP and HCM platform, must-haves included security, governance, and compliance (87%); user experience (85%); and integration capabilities (84%). These are essentially non-negotiable. Any vendor that can¡¯t demonstrate strength across all three won¡¯t make the shortlist.

But the same research reveals something beneath that consensus; AI and agent capabilities (71%) and platform extensibility and APIs (66%) are increasingly important. CIOs may be underweighting them not because they don¡¯t matter, but because they can¡¯t yet evaluate vendors on AI maturity with confidence. The relatively lower ranking of AI capabilities doesn¡¯t reflect low interest. It reflects evaluation uncertainty. That is the gap the CIO must close.

The evaluation has split into two tiers.

The CIOs who get this decision right evaluate both tiers simultaneously. The ones who optimize only for tier 1 end up with a platform that¡¯s secure and integrated but architecturally unable to support the AI mandate the board is demanding.

Where does execution usually break down when CIOs modernize HR and finance platforms?

The Jasper Colin research surfaces a critical tension: not all high-priority technology initiatives are equally achievable. Cybersecurity, data privacy, platform reliability, and data quality all rank as top priorities and top challenges¡ªconfirming they¡¯re genuinely hard. But a second category of priorities creates disproportionate execution bottlenecks: data integration across systems, showcasing the value of IT investments, reducing technical debt, and modernizing legacy infrastructure. These rank lower as priorities but punch well above their weight in difficulty.

What execution risks should CIOs evaluate when choosing an AI?ready platform?

For the platform evaluation in the AI era, this means CIOs should stress-test vendors not on what they promise, but on where execution has historically broken down. The questions that matter most:

• How does the platform handle data consistency across modules¡ªby design or by workaround?
• Do integrations survive upgrades, or do they break with every release?
• Can you demonstrate AI value in terms the CFO will accept?
• How does the platform reduce¡ªrather than add to¡ªyour integration and technical debt?
• Who owns the deployment risk?
• How is the cultural transition managed?
• Does the partnership end at go-live?
• How safe is the transition itself?

These are not just technology problems; they are execution problems. The platform evaluation has to extend beyond the software to include the deployment methodology. A vendor¡¯s professional services ecosystem is what actually ensures integrations don¡¯t break, technical debt is retired rather than relocated, and the transition to an AI-ready architecture doesn¡¯t stall in implementation. Services are not an afterthought to platform selection. It is the risk-mitigation layer that protects every other architectural choice you make.

How can CIOs keep IT in control while AI delivers real outcomes?

What governance gaps expose CIOs to risk with AI agents?

Security is the dominant concern for enterprise-wide AI adoption, cited by 39% of CIOs. But the concern isn¡¯t abstract¡ªit¡¯s structural. Most organizations are running dozens of disconnected AI experiments across different teams, models, and vendors. There¡¯s no unified view of what¡¯s deployed, who is using it, what data it can access, or whether it¡¯s delivering value.

The result is what some IT leaders have started calling ¡°lawless agents¡±¡ªpowerful AI connected to enterprise data without guardrails, compounding compliance and security risk with every new deployment. The governance challenge is further compounded by fragmentation; no single role owns AI governance across the enterprise. The Jasper Colin research found ownership split across CISOs (27%), CIOs (20%), CDOs (17%), and dedicated AI ethics boards (16%).

For the platform buyer, the implication is clear: AI governance cannot be a customer problem layered on top of the technology. It has platform capability built into the architecture¡ªand it has to make AI accountable by design.

What AI governance capabilities should CIOs look for in HR and finance platforms?

When assessing how a platform handles AI governance, CIOs should evaluate across six dimensions:

• Agent identity and lifecycle management. Does the platform provide a centralized system for registering, permissioning, monitoring, and retiring AI agents¡ªregardless of which vendor built them? Can agents be managed alongside employees in the organizational structure, with the same rigor applied to access controls and accountability?
• Auditability and compliance. Every agent action that touches people or money must be logged, traceable, and auditable. Can the platform enable compliance with evolving AI regulations across regions¡ªfrom the EU AI Act to state-level and federal legislation in the U.S.¡ªwithout requiring customers to build an extensive compliance and reporting layer?
• Security model consistency. Are AI agents governed by the same security model as the core product? Or do they require separate security reviews, access controls, and compliance validation for each new deployment?
• Multi-vendor agent support. No enterprise runs a single-vendor AI stack. Can the platform govern agents regardless of where they were built¡ªacross cloud infrastructure providers, collaboration platforms, and third-party partner ecosystems? The 43% of CIOs who believe agents should be managed in a central layer regardless of origin are signaling a clear requirement.
• Permission-aware data access. AI agents need data to be useful. But they should never get a raw, unfiltered view of enterprise data. Does the platform enforce least-privilege access for every agent interaction, with data filtered by the same security model that governs human users?
• Governance-first design. Was governance built before the agent portfolio was scaled, or is it being bolted on after the fact? A governance-first architecture means every agent inherits controls by default. A governance-later approach means every deployment is a bespoke security project.

The ²ÝÝ®ÊÓƵ approach.

Having an accurate system of record is necessary. In an agentic world, it¡¯s not sufficient. CIOs shouldn¡¯t ask ¡°Is my data accurate?¡± but instead, ¡°Can I empower an agent to act on that data without messing up my controls?¡±

Within the ²ÝÝ®ÊÓƵ platform, agent actions are backed by enterprise data, while deterministic guardrails ensure every action stays inside the customer¡¯s configured policies and controls.

That foundation rests on three architectural pillars that generic AI stacks struggle to deliver.

These pillars are enforced through three interlocking capabilities.

²ÝÝ®ÊÓƵ¡¯s governance practices are validated beyond self-attestation. ²ÝÝ®ÊÓƵ is the only major HR and finance platform provider to achieve third-party certification for both ISO 42001 and the NIST AI Risk Management Framework.

The result for CIOs: A single place to manage the entire agent workforce, with full audit rails, permission-aware access, and demonstrable regulatory compliance across jurisdictions.

Governance in practice requires expert change management.

A governed architecture is only as effective as its deployment. ²ÝÝ®ÊÓƵ Services and preferred implementation partners ensure centralized controls, identity frameworks, and compliance measures are configured correctly from day one¡ªeliminating the execution risk that typically derails enterprise-wide AI rollouts. 95% of ²ÝÝ®ÊÓƵ deployments complete on time.

How should CIOs evaluate the technical architecture for an agentic enterprise?

How has the platform architecture question changed in the age of AI agents?

CIOs define ¡°platform¡± primarily as an integration backbone connecting applications and data systems (31%), followed by the ability to run ERP applications (28%) and a data and analytics foundation (27%). Platforms are fundamentally about connectivity, data access, and flexibility¡ªnot about where code runs or which applications are hosted.

That definition has direct implications for evaluation. The platform you choose for HR and finance must function as a governed data foundation that enables AI, connects to the broader ecosystem, and remains stable through upgrades and expansions. And the category is shifting fast: 30% of CIOs now want their ERP partner to function as a ¡°system of action,¡± while only 11% still want a ¡°system of record¡± alone. You¡¯re looking for a platform that can execute, not just store.

How can CIOs tell whether a platform¡¯s AI can execute end?to?end or only suggest?

Before evaluating specific capabilities, CIOs need to apply a foundational architectural test that separates platforms built for the agentic enterprise from those still operating as enhanced record-keeping systems.

This distinction¡ªbetween AI that guesses and AI that executes¡ªis the single most important architectural filter in the evaluation. A platform where probabilistic AI is governed by deterministic controls means agents can reason, recommend, and act, while critical processes remain correct, compliant, and repeatable. A platform where AI sits outside the business process framework means every agent action becomes a manual handoff, a data consistency risk, and a governance gap.

Apply this test to every vendor demo: Does the agent complete the transaction end-to-end within existing business processes and security controls? Or does it generate a recommendation that requires someone to log into a different system and finish the job?

How does platform fragmentation shape CIOs¡¯ architecture decisions?

Most organizations operate in highly fragmented IT environments. Nearly 40% of CIOs report managing 21¨C50 distinct platforms, and another 10% manage more than 50. Among the largest enterprises (100,000+ employees), 61% operate 21¨C50 platforms and 35% manage more than 50.

Fragmentation is the primary driver behind platform strategy adoption.

Top 3 reasons for pursuing platform strategy.

The evaluation question isn¡¯t whether you can replace every platform with one. It¡¯s whether the HR and finance platform you select can orchestrate across the ecosystem¡ªconnecting data, governing agents, and enabling automation¡ªwithout requiring you to rip and replace everything else.

What technical architecture criteria should CIOs evaluate for AI?ready HR and finance platforms?

• Data architecture and consistency. Does the platform provide a centralized system for registering, permissioning, monitoring, and retiring AI agents¡ªregardless of which vendor built them? Can agents be managed alongside employees in the organizational structure, with the same rigor applied to access controls and accountability?
• Upgrade-safe integrations. Every agent action that touches people or money must be logged, traceable, and auditable. Can the platform enable compliance with evolving AI regulations across regions¡ªfrom the EU AI Act to state-level and federal legislation in the U.S.¡ªwithout requiring customers to build an extensive compliance and reporting layer?
• Data openness and AI readiness. Are AI agents governed by the same security model as the core product? Or do they require separate security reviews, access controls, and compliance validation for each new deployment?
• Agent deployment flexibility. No enterprise runs a single-vendor AI stack. Can the platform govern agents regardless of where they were built, across cloud infrastructure providers, collaboration platforms, and third-party partner ecosystems? The 43% of CIOs who believe agents should be managed in a central layer regardless of origin are signaling a clear requirement.
• Ecosystem openness. Partner integrations, agent-ready APIs, and multi-vendor agent support are no longer differentiators¡ªthey¡¯re requirements. What¡¯s the partner ecosystem like? What¡¯s the API strategy? What¡¯s the vendor¡¯s stance on data portability and exit rights?
• Extensibility and custom development. No vendor roadmap anticipates every business need. When 40% of CIOs manage 21¨C50 platforms, the gaps between them become the problem¡ªand the platform that enables your team to build custom agents, apps, and integrations to close those gaps wins. The decisive filter: Does every custom build inherit the platform¡¯s security and governance by default, or does your team reassemble authentication, monitoring, and compliance from scratch each time?

To apply these criteria to your own vendor shortlist, download the full CIO buyer¡¯s guide (PDF) to get the complete evaluation scorecard.

What is ²ÝÝ®ÊÓƵ's architecture advantage?

For two decades, ²ÝÝ®ÊÓƵ has served as the system of record for people and money, processing more than a trillion transactions annually across 70 million workers and over 11,000 customers. In an AI-first era, that foundation is more than important; it is what distinguishes ²ÝÝ®ÊÓƵ, supplying the context and guardrails that allow AI to operate safely within an enterprise¡¯s most consequentiWhat is ²ÝÝ®ÊÓƵ's architecture advantage?al processes. Realizing that potential requires the platform itself to evolve¡ªfrom a system of record into a platform for agents.

Data and context. The evolution begins at the foundation. ²ÝÝ®ÊÓƵ¡¯s core is built on unified data, governance, ownership of business processes, a unified security model, and the kind of compliance that enterprises can trust. These are the elements that have made ²ÝÝ®ÊÓƵ the leader in HCM and ERP for two decades, and together they form a context moat: years of approval chains, separations of duty, and regional rules encoded directly into the platform. In an agentic world, this is not legacy infrastructure. It is the set of guardrails that every agent inherits by default, ensuring that the most critical people and money tasks are automated safely and correctly.

Core applications. Layered upon that foundation are the core applications: ²ÝÝ®ÊÓƵ Human Capital Management (HCM), ²ÝÝ®ÊÓƵ Payroll, ²ÝÝ®ÊÓƵ Financial Management, ²ÝÝ®ÊÓƵ Adaptive Planning, ²ÝÝ®ÊÓƵ Student, and industry-specific products. These are the mission-critical domains in which ²ÝÝ®ÊÓƵ applies AI to reimagine how functional work is performed across HR, finance, and IT.

Teams of agents. Yet in an agentic world, the applications must themselves be reconsidered: How they are used and how people and agents collaborate in purposeful ways to accomplish meaningful work. This is the role of ²ÝÝ®ÊÓƵ agents. Teams of agents, across HR, finance, IT, and legal have a pre-configured runtime; they do not learn the rules, they inherit them from day one. The effect is a fundamental change in how people engage with ²ÝÝ®ÊÓƵ. Friction decreases, mundane tasks fall away, and agents work alongside people, capable of acting, advising, and adapting autonomously.

Platform extensibility and ecosystem. Beyond what ²ÝÝ®ÊÓƵ delivers on its own, customers can extend the platform further. ²ÝÝ®ÊÓƵ Data Cloud is the knowledge layer for AI, where people and money data becomes trusted, governed, and agent-ready. Zero-copy access through Apache Iceberg for high-volume analytics and Live Data Query for real-time SQL allow customers to connect ²ÝÝ®ÊÓƵ bidirectionally with platforms such as Databricks, Google Cloud, Salesforce, Snowflake, and a range of BI tools. A forthcoming semantic layer preserves both the what and the how¡ªmore than 20 years of ²ÝÝ®ÊÓƵ business objects, logic, metrics, relationships, and governance.

²ÝÝ®ÊÓƵ Build serves as the agent-ready developer platform: customers may build on ²ÝÝ®ÊÓƵ to deliver agents, applications, and orchestrations that run inside ²ÝÝ®ÊÓƵ or build with ²ÝÝ®ÊÓƵ to power agents and actions across the wider stack. Along either path, agents inherit the guardrails from the outset.

Agent management and governance. Governing it all is ²ÝÝ®ÊÓƵ Agent System of Record, which provides visibility, governance, and control over every agent an organization deploys, whether first-party, partner-built, or third-party. Each agent is issued a unique identity, scoped permissions, a complete audit trail, and continuous attestation. This is where the lawful becomes provable. Without it, governance gives way to liability.

Unified and connected experience. Sana, in turn, offers the opportunity to redefine how and where work happens and how people interact with software altogether. It is the last software anyone will need to learn. Sana is the surface upon which work is done, connected to every application so that everything from ask to action resides within a single experience, with ²ÝÝ®ÊÓƵ¡¯s guardrails built in. Agents meet users where they already are, and every action still routes back down through the governance stack.

The foundation of trust. Governance, in this architecture, is neither feature nor add-on. It is the foundation that ensures any agent engaging with people and money data runs on the rails that already run the business. That foundation is what renders agents worthy of trust, both within ²ÝÝ®ÊÓƵ and across the broader ecosystem. It is how the system of record becomes the platform for agents¡ªand the trusted backend for agentic work across the entire AI stack.

How can CIOs align the buying committee on an AI?ready platform decision?

Who are the key stakeholders in buying an AI?ready HR and finance platform?

CIOs frequently lead shortlisting (48%) and own final vendor approval (67%), but budget authority often sits elsewhere. HR and IT most often share technical ownership of HCM systems¡ª52% report joint management¡ªreinforcing the need to balance business and technology requirements throughout the evaluation.

Building consensus across the buying committee requires understanding what each stakeholder needs to hear.

A consensus-building framework.

• Step 1: Establish shared evaluation criteria. Before vendor demos begin, align the buying committee on the seven evaluation dimensions in this guide. Weight them based on your organization¡¯s priorities¡ªand ensure AI governance, data architecture, and ecosystem openness are represented alongside the traditional criteria.
• Step 2: Map stakeholder requirements to vendor capabilities. Each stakeholder defines their top three non-negotiable requirements. The platform that satisfies the most non-negotiables across the committee¡ªnot just the most features on a checklist¡ªis the strongest candidate.
• Step 3: Run a structured proof of concept. Don¡¯t evaluate based on demos alone. Request a proof of concept that tests the platform against your specific data, your specific workflows, and your specific integration environment. Evaluate governance, not just functionality.
• Step 4: Assess for risk, not just capability. Ask every vendor: what happens when an integration breaks during an upgrade? What happens when an AI agent accesses data it shouldn¡¯t? What happens when a regulation changes? The platform that answers these questions with structural safeguards¡ªnot promises¡ªis the one that mitigates regret.
• Step 5: Build the consensus narrative. Frame the recommendation around the outcome each stakeholder cares about most. For the CIO, governed AI at scale. For the CHRO, exceptional employee experience. For the CFO, measurable productivity gains. For the CISO, enterprise-grade security applied to every agent. For IT operations, reduced complexity, not more of it.

What scorecard should CIOs use to compare AI?ready HR and finance platforms?

Download the guide to get access to the scorecard, which includes seven dimensions you should assess. You can rate each dimension to find the right AI platform for your organization.

Conclusion: The platform you choose is the AI strategy you get.

CIO decisions compound over time. The systems selected five years ago shaped what is possible today; the platform chosen now will determine what¡¯s possible in the age of AI agents.

The research is unambiguous: CIOs are not debating whether to invest in AI. That question is settled. They¡¯re navigating how to deploy AI responsibly, at scale, and with measurable value¡ªinside the systems that run their most critical processes.

The platform you choose is simultaneously an architecture decision, a governance decision, an AI strategy decision, and an execution decision¡ªand it¡¯s yours to make. Evaluate for how a platform governs AI, activates data, supports extensibility, and scales without breaking. Build consensus across the buying committee around shared evaluation criteria, not feature comparisons. And choose partners, not just vendors¡ªthe ones who treat governance as a prerequisite, data as an asset, and openness as a design principle.

Download the full CIO buyer¡¯s guide (PDF) to get:

• Printable seven?dimension evaluation scorecard with 1¨C5 scoring
• Detailed CIO-CFO business?case examples
• Architecture and governance checklists you can use in RFPs