Evaluating agentic AI solutions for the enterprise - WRITER

Evaluating agentic AI solutions for the enterprise

The CIO’s complete guide

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You’re at an inflection point‌ — ‌and the window is closing

Your board expects AI transformation. Your business units are already building shadow agents. Your security team is raising red flags. And you’re being asked to make a platform decision that will define your company’s competitive position for the next decade.

The pressure is real: 88% of senior executives plan to increase AI-related budgets in the next 12 months due to agentic AI ( PwC AI Agent Survey, May 2025). But here’s what keeps you up at night‌ — ‌the enterprise agentic AI market is still immature. Vendors are rebranding workflow automation as “agentic.” Point solutions promise quick wins but create long-term technical debt. And DIY approaches hide crushing costs that only emerge at scale.

The stakes are higher than your first generative AI pilots. This isn’t about productivity tools anymore. This is about re-architecting core business processes with autonomous agents that make decisions, take actions, and generate measurable business value across three critical dimensions:

But here’s what most enterprises get wrong: They’re forced to compromise. You can have developer tools that are powerful but siloed from the business users who hold the context. Or business tools that are accessible but shallow. You can streamline your stack in one ecosystem but get locked into one vendor’s roadmap. Or manage the complexity of stitching everything together yourself.

WRITER changes that calculus. We’re the only platform purpose-built for enterprises that delivers all three pillars with no tradeoffs:

  1. Business Empowerment: The people closest to the work design and maintain agents—encoding institutional knowledge that makes AI actually work
  2. IT Governance: Complete control over the environment every agent runs in—with full interoperability across your existing tech stack
  3. Industry Expertise: Proven domain-specific solutions and embedded specialists that accelerate time-to-value from months to weeks

The fundamental question has shifted. You’re no longer asking “What can this tool do for my organization?” You’re asking: 
”How do we re-engineer—and actually govern—our business processes with AI automation at enterprise scale?”

This guide gives you a framework to cut through the noise and make a decision you can defend to your board, your CISO, and your CFO.

What you’ll learn:

  1. The three pain points derailing enterprise agentic AI initiatives—and why a patchwork approach fails
  2. How the AI technology stack is evolving—from models to orchestrated multi-agent systems
  3. A strategic evaluation framework—with competitive contrast and questions that separate real platforms from point solutions
  4. What successful enterprise adoption actually looks like‌ — ‌including implementation timelines and ROI metrics
  5. How to move from evaluation to decision‌ — ‌with answers to the toughest objections you’ll face internally

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Executive summary

This guide provides CIOs with a comprehensive framework for evaluating enterprise agentic AI platforms — the platform decision that will define your organization’s competitive position for the next decade.

What you’ll gain:

The bottom line:

The enterprise agentic AI market is still maturing, but the window for strategic advantage is closing. Organizations that successfully navigate the “Crawl, Walk, Run, Fly” maturity curve will re-architect core operations and scale without proportional headcount growth. Those that delay six months for “more data” will find themselves catching up to competitors who moved decisively. This guide gives you the framework to choose wisely, execute strategically, and lead the transformation.

## What are the top three agentic AI pain points 
for CIOs?

KEY TAKEAWAYS

Pain point #1

Spiraling AI costs and hidden fees

It’s tempting to think that stitching together best-of-breed AI models and tools gives you ultimate control and cost efficiency. But the sticker price of an AI model is just the tip of the iceberg. The real expense lies in the massive, ongoing effort to build and support the infrastructure that allows agents to function reliably at an enterprise scale.

The integration tax

Consider what happens when you try to connect agents to your existing apps and data. Each integration becomes a constant, expensive engineering nightmare. Every new connection is a fragile point of failure. Each API update requires maintenance. Each security patch demands cross-system testing.

Most enterprises underestimate these integration costs by 3-5x 
in their initial business case.

The performance challenge

A slow chatbot is an annoyance. A slow agent fumbling a time-sensitive financial transaction is a disaster. Achieving the latency-optimized inference needed for agents to perform complex tasks instantly requires infrastructure that’s incredibly difficult and expensive to build from scratch.

Production-grade agent performance demands specialized infrastructure that point solutions don’t provide — and most IT teams don’t have the expertise to build.

The talent gap

The engineers who can build, deploy, and govern autonomous agents represent a new breed of specialist. They need to understand LLM ops, orchestration frameworks, enterprise integration patterns, and AI security. They are rare, in high demand, and expensive.

The “build it ourselves” path typically requires hiring 5-8 specialized FTEs at $200K+ each, plus ongoing retention costs in a hyper-competitive talent market.

\ \ Beyond ‘build vs. buy’: Why a unified AI platform is the only way to scale agentic AI\ \ Learn more](/content/blog/build-vs-buy-generative-ai/index.html)

Pain point #2

Ungoverned AI and mounting security risks

Your biggest risk isn’t an employee pasting a confidential document into a public AI tool. It’s that same employee, with the best of intentions, using a no-code app to build a makeshift AI agent that starts moving customer data between systems autonomously—completely invisible to your IT and security teams.

This is the “shadow operations” problem: autonomous processes running outside your governance framework, creating compliance exposure you can’t even see, let alone manage.

Why retrofitted security fails

Most agentic AI platforms evolved from consumer products or developer tools. Security and compliance get bolted on after core architecture decisions are locked in. You end up with different security models for different components‌ — ‌the exact fragmentation you’re trying to prevent.

Every additional point solution in your AI stack multiplies your security surface area and creates gaps where data can leak or compliance can fail.

What true enterprise governance requires

True enterprise agentic AI governance isn’t about controlling the AI model itself—it’s about governing every single action an agent can take. Think of a genuine enterprise platform as the central nervous system for all your agents.

It must provide a unified security perimeter through single-tenant or private cloud deployment that completely isolates your agents and your data. It needs action-level guardrails—a strict permissioning layer that defines exactly which systems an agent can touch, what actions it can perform, and what data it can handle.

Complete auditability is non-negotiable. Every agent action must be traceable, compliant, and secure by design, with full audit logs that satisfy SOC 2, HIPAA, and GDPR requirements. And you need real-time observability and alerting—continuous monitoring of agent and user activity, with policy enforcement and alerts that catch issues before they become incidents.

\ \ WRITER connectors: Governed agent access across enterprise systems\ \ Learn More](/content/blog/writer-connectors/index.html)

Pain point #3

Agentic power without central control

The real magic of agentic AI happens when you move beyond a single agent and start conducting an orchestra of them. One agent monitors your supply chain, another analyzes sales data, a third drafts executive summaries—all working in concert to achieve complex business outcomes.

But this power brings a critical question: How do you keep that orchestra from descending into chaos?

The coordination crisis

When you stitch together different agentic solutions, every agent operates in its own silo. Each has its own security model, its own data protocols, its own way of handling errors and exceptions. You’re not building an intelligent enterprise‌ — ‌you’re building a digital house of cards that will collapse under its own complexity.

Agents start making contradictory decisions based on different data sources. You lose visibility into which agent is doing what and when. When something goes wrong, debugging becomes impossible. You can’t enforce consistent guardrails across agent behaviors, and each new agent integration exponentially increases complexity.

This is a fast track to a new, crippling kind of technical debt‌ — ‌one that undermines the very value you’re trying to create with AI automation.

What orchestration demands

The full potential of an automated enterprise only materializes when your fleet of agents is managed from a single command center.

You need a unified orchestration layer‌ — ‌a central system that coordinates agent workflows, manages dependencies, and ensures agents work together toward business goals. Agents must be able to build on each other’s work through shared context and memory, accessing common knowledge bases and maintaining consistency across interactions.

Centralized governance becomes essential: one place to define policies, set guardrails, monitor performance, and audit actions across all agents. And critically, you need the ability to improve agent capabilities without breaking existing workflows through coordinated updates and backward compatibility.

\ \ Supervising the synthetic workforce: Observability for AI agents requires managers, not metrics\ \ learn More](/content/engineering/supervising-synthetic-workforce/index.html)

A modern framework for evaluating enterprise AI solutions

KEY TAKEAWAYS

The evaluation paradox: Most enterprise software evaluations follow a familiar pattern—create requirements, demo solutions, check boxes, pick a winner. But agentic AI platforms don’t fit this model. You’re not buying software that does a specific thing. You’re choosing the architectural foundation for how your business will operate for the next decade.

The shift from tools to platform: In traditional enterprise software, you could choose best-of-breed tools for different functions and integrate them over time. With agentic AI, that approach fails. The integration complexity, security gaps, and orchestration challenges make a patchwork architecture unsustainable at scale.

This means your evaluation framework must go deeper than feature checklists. You need to understand architectural philosophy, security design, governance capabilities, and the hidden costs of different approaches.

1

Model strategy and architecture

When evaluating AI platforms, most buyers start with “How good is the model?” But the more strategic question is: “How much control do I have over the model, and what happens as AI technology evolves?”

What to evaluate:

Model ownership and transparency

Why this matters: If your platform provider is dependent on a third-party LLM provider, you inherit that dependency. Your pricing, capabilities, and roadmap are at the mercy of someone else’s decisions.

Backward compatibility and version control

Why this matters: A major cause of failed enterprise AI initiatives is broken workflows after model updates. If your critical business processes rely on agents that can change behavior unpredictably, you don’t have a platform‌ — ‌you have a time bomb.

Fine-tuning and customization

Why this matters: Generic models provide generic results. Enterprise differentiation comes from AI that understands your business, not just general knowledge.

Questions that separate platforms from point solutions

Platform evaluation best practices: Model ownership & independence

The challenge: Model dependency creates vendor lock-in at the worst possible level—the intelligence layer. If your critical workflows depend on a model you don’t control, you’re betting your business on someone else’s roadmap, pricing changes, 
and deprecation decisions. But you also need the flexibility to use specialized models for specific use cases.

What separates platforms from point solutions: Most “agentic platforms” are wrappers on foundation models from OpenAI, Anthropic, or Google. When those providers change pricing, deprecate models, or update capabilities, you’re stuck adapting with no backward compatibility guarantees. True enterprise platforms provide both owned models with guaranteed backward compatibility AND the flexibility to use third-party models (including custom-trained ones) through universal model controls—managing everything through a single, unified governance layer.

What best-in-class looks like: Leading platforms own their core models, ensuring they’re never distilled or quantized post-training for consistent, predictable performance. They guarantee strict backward compatibility‌ — ‌new model versions never break existing workflows. AND they integrate with model platforms like Amazon Bedrock, giving you the flexibility to choose the best model for each job while maintaining unified governance, routing decisions, and compliance enforcement across all models.

Example: WRITER provides fully owned Palmyra models (including the latest Palmyra X5) with guaranteed backward compatibility, plus integration with Amazon Bedrock for third-party model access. A manufacturing company built 47 specialized agents on WRITER’s platform over 18 months. When WRITER released Palmyra X5, every agent got smarter without changing a line of code‌ — ‌zero breakage. Meanwhile, they can leverage Amazon Bedrock models for specialized tasks, all governed through WRITER’s unified platform.

EVALUATION QUESTIONS:

2. Enterprise-grade security and governance

The non-negotiable requirement: For Global 2000 companies, security and governance aren’t features‌ — ‌they’re foundational requirements. Your AI platform must be a fortress, not a convenience.

What to evaluate:

Data privacy and isolation

Why this matters: In a multi-tenant environment, you’re trusting the vendor’s infrastructure. In regulated industries or with sensitive data, that’s often unacceptable to your legal and compliance teams.

Compliance certifications

Why this matters: Each missing certification means months of internal security reviews, legal negotiations, and risk committee approvals. Vendors with comprehensive certifications accelerate your path to production.

AI guardrails and policy enforcement

Why this matters: Without granular guardrails, agents become uncontrolled automation—exactly the “shadow AI” problem you’re trying to solve.

Supervision suite and governance at scale

The governance challenge for agentic AI isn’t securing the platform—it’s supervising autonomous agents operating at enterprise scale. As business teams build dozens or hundreds of agents, IT needs comprehensive visibility, control, and confidence to govern this new “synthetic workforce” without creating bottlenecks.

Key supervision capabilities:

Why this matters: Without comprehensive supervision, agent adoption creates ungovernable chaos. Business teams build agents faster than IT can review them. Agents access systems without proper permissions. Costs spiral without visibility. Supervision at scale means IT governs the entire fleet through centralized controls that scale automatically—no manual bottleneck as adoption grows.

What good looks like:
Red flags:

Questions that separate platforms from point solutions

### Platform evaluation best practices:
Enterprise security & governance architecture

The challenge: Most agentic AI platforms evolved from consumer products or developer tools. Security and compliance are retrofitted after core architecture decisions are made, creating vulnerabilities at integration points that CISO teams can’t accept. Additionally, you need governance that scales automatically as agent adoption grows—not manual processes that create IT bottlenecks.

What separates platforms from point solutions: Patchwork approaches force you to conduct separate security reviews for each component (model API, orchestration layer, data connectors, etc.). Different vendors have different security models, and integrations create data leakage gaps. True enterprise platforms are architected from day one for Global 2000 security requirements—every component operates within a unified security perimeter with event-level audit trails, global policies that propagate automatically, granular permissions enforced at runtime, and native integration with your existing security tools.

What best-in-class looks like: Leading platforms provide comprehensive supervision suites that give IT full visibility, control, and confidence to govern agents at scale. This includes: centralized dashboards with event-level monitoring and analytics, agent approval workflows that prevent shadow AI, global guardrails that automatically block or mask sensitive data across all agents, role-based permissions with least-privileged access, and integration with your existing security platforms. One security audit covers the entire platform. SOC 2 Type II, HIPAA, GDPR, PCI compliance ready.

Example: WRITER’s platform was architected for enterprise security and governance from day one, with a supervision suite providing centralized control. A financial services firm couldn’t start pilots with other vendors until security completed separate reviews of each component. With WRITER, they conducted one comprehensive security review covering the entire unified platform‌ — ‌saving four months. WRITER integrates natively with observability tools (Datadog, Traceloop) and security platforms (Noma, Lakera, Amazon Bedrock Guardrails), so governance scales through existing workflows rather than creating another siloed system.

EVALUATION QUESTIONS:

3. Agentic capabilities and orchestration

Anyone can build a single agent that does one thing well. 
The platform test is: Can it orchestrate multiple agents into complex, coordinated workflows that deliver business outcomes?

What to evaluate:

RAG (Retrieval-Augmented Generation) Capabilities

Why this matters: Agents without access to your proprietary data are just expensive chatbots. RAG is the bridge between generic AI and business-specific intelligence.

Multi-Agent Orchestration

Why this matters: The value of agentic AI compounds when agents work together. If your platform can only support isolated agents, you’ll never achieve enterprise-scale automation.

Agent lifecycle management

Building agents is only the beginning. True enterprise platforms provide comprehensive lifecycle management‌ — ‌from creation through testing, deployment, monitoring, and continuous improvement.

Key lifecycle capabilities:

Why this matters: Agents aren’t “set it and forget it”—they require ongoing management and optimization. Platforms with strong lifecycle management make it easy to build better agents over time. Those without create technical debt as agents drift from optimal performance.

What good looks like:
Red flags:

### Platform evaluation best practices: 
Build, activate & supervise at scale

The challenge: The real value of agentic AI comes from orchestrating multiple specialized agents into complex workflows. But the very autonomy that makesagents powerful also makes them a liability if left ungoverned. When business teams can build at scale, how does IT maintain visibility, control, and confidence? How do you democratize AI development without creating 
shadow AI chaos?

What separates platforms from point solutions: Point solutions excel at single-agent use cases but fail when you need to build, deploy, and supervise agents at enterprise scale. Each vendor has its own approach, creating silos and governance gaps. True enterprise platforms provide an end-to-end environment for the entire agent lifecycle‌ — ‌from collaborative building to deployment to comprehensive supervision‌ — ‌all managed from unified dashboards with centralized governance that scales automatically.

What best-in-class looks like: Leading platforms enable both business empowerment AND IT governance without tradeoffs. Business users can design agents using 100+ prebuilt templates and no-code tools, while developers extend them with custom logic. IT maintains complete oversight through centralized monitoring, event-level analytics, agent approval workflows, global policies that propagate automatically, and granular role-based permissions. Supervision scales with adoption—no manual IT effort required.

Example: WRITER provides an end-to-end platform for building, activating, and supervising AI agents at enterprise scale. With 100+ prebuilt agents and collaborative Agent Builder, teams start fast. WRITER’s newly launched Supervision Suite gives IT full visibility with event-level monitoring, agent approval workflows, 
and global guardrails that enforce compliance automatically. Customers like Qualcomm achieve 85% weekly agent usage with 2,400 hours saved monthly, while Prudential reaches 70% adoption—both with IT maintaining complete 
governance throughout.

EVALUATION QUESTIONS:

4. Platform experience & enablement

The platform paradox: Enterprise AI platforms promise to democratize AI development—empowering business users to build agents without deep technical skills. But most fail to deliver on this promise. The technical teams get powerful tools, but business users are locked out. Or business tools are accessible but so shallow that developers can’t extend them.

The strategic question isn’t just “Can this platform build agents?” It’s “Who can build agents, and what support do they need to succeed?”

True enterprise platforms eliminate the forced choice between business empowerment and technical sophistication. They provide intuitive tools for business users while giving developers the extensibility they need—all in one unified environment. And they back these capabilities with comprehensive enablement that turns platform adoption into organizational transformation.

What to evaluate:

Business user experience

The litmus test for enterprise agentic AI is simple: Can the people closest to the work‌ — ‌marketers, sales operations, customer success managers, HR specialists — design and maintain agents that solve their problems? Or are they dependent on IT to build everything?

Key capabilities to assess:

Why this matters: The bottleneck in enterprise AI isn’t technology—it’s the backlog of use cases waiting for IT resources. If only developers can build agents, you’ll never scale beyond a handful of high-value projects. Business user empowerment is the difference between 10 agents and 1,000 agents.

What good looks like:
Red flags:

Developer experience and extensibility

While business users need simplicity, developers need power. 
A true enterprise platform provides both—without forcing tradeoffs.

Key capabilities to assess:

Why this matters: Your internal developers are the force multipliers who extend the platform’s capabilities to your unique business needs. A platform without a strong developer experience becomes a bottleneck—developers can only work within preset boundaries, and custom use cases stall.

What good looks like:
Red flags:

Pre-built solutions and accelerators

The fastest path to value isn’t building from scratch — it’s starting with proven solutions and customizing for your context.

Key capabilities to assess:

Why this matters: Generic platforms force you to reinvent the wheel for every use case. Platforms with deep industry expertise and proven playbooks compress time-to-value from months to weeks—and reduce implementation risk through battle-tested solutions.

What good looks like:
Red flags:

Enablement and support

Platform capabilities don’t matter if your team doesn’t know how to use them. The vendor’s enablement approach determines whether adoption succeeds or stalls.

Key capabilities to assess:

Why this matters: Platform adoption is organizational transformation, not software installation. Vendors who treat it as such—providing strategic partnership, not just technical support—are the ones whose customers succeed at scale.

What good looks like:
Red flags:

Questions that separate platforms from point solutions

### Platform evaluation best practices: 
Enabling business-led AI transformation

The challenge: Most enterprises get trapped choosing between powerful-but-inaccessible developer tools or simple-but-shallow business tools. They’re forced to compromise: either business teams wait in IT backlogs, or they build agents in isolated tools that IT can’t govern. Meanwhile, lack of strategic enablement means platform capabilities sit unused because teams don’t know how to harness them effectively.

What separates platforms from point solutions: Point solutions optimize for one audience—either developers get sophisticated APIs but business users are locked out, or business users get templates but developers can’t extend them. Patchwork approaches try to combine separate tools but create friction through constant context-switching. True enterprise platforms provide unified environments where business users build with no-code tools, developers extend with full SDK access, and both collaborate seamlessly—backed by comprehensive enablement that drives organizational transformation.

What best-in-class looks like: Leading platforms eliminate the forced choice between accessibility and power. Business users leverage 100+ pre-built agents and visual workflow designers to solve problems autonomously. Developers extend these solutions using robust SDKs, build custom integrations, and establish reusable patterns. Both work in the same platform with role-appropriate interfaces. The vendor acts as strategic partner—providing structured training, proactive customer success, implementation services that co-create your AI operating model, and community learning that accelerates adoption across the organization.

Example: WRITER provides an end-to-end platform where business empowerment and developer sophistication coexist. Business users access 100+ pre-built agents and Agent Builder (collaborative no-code environment) to design workflows matching how work actually gets done. Developers extend these agents using WRITER’s SDK, build custom integrations, and orchestrate complex multi-agent systems—all within the unified platform. Salesforce trained 50 non-technical business users to build and maintain their own agents through WRITER’s enablement programs. Meanwhile, WRITER’s professional services team partners with enterprises to design comprehensive AI operating models—not just implement technology, but transform how organizations work—accelerating adoption from pilot to enterprise-wide impact.

EVALUATION QUESTIONS:

5. Infrastructure, integration & total cost of ownership

The hidden costs and lock-in risks: Most enterprise AI evaluations focus on capabilities‌ — ‌what can the platform do? But the strategic questions are architectural: How does it integrate with your existing infrastructure? What does it actually cost to deploy and operate at scale? And can you evolve your stack as AI technology changes, or are you locked in?

These questions matter because agentic AI isn’t a standalone application‌ — ‌it’s the nervous system for your entire business. It must connect to every critical system, scale with adoption, and deliver economic value that justifies the investment. And it must do this while preserving your strategic flexibility as the AI landscape evolves.

The difference between platforms that succeed at enterprise scale and those that stall comes down to three factors: architectural philosophy (open vs. closed), operational excellence (performance, scalability, reliability), and total economic impact (visible costs + hidden costs + business value created).

What to evaluate:

Platform architecture philosophy

Before evaluating technical specifications, understand the vendor’s architectural philosophy. This determines whether you’re building on a foundation that can evolve with your needs—or one that constrains your future.

Key capabilities to assess:

Why this matters: The AI landscape is evolving rapidly. Platforms with open architectures allow you to integrate best-of-breed tools as they emerge. Closed ecosystems lock you into one vendor’s roadmap and innovation pace—creating strategic risk when technology shifts unpredictably.

What good looks like:
Red flags:

Integration ecosystem

Agentic AI platforms that can’t connect to your existing systems aren’t platforms‌ — ‌they’re expensive science experiments. 
True enterprise platforms provide comprehensive integration capabilities that make agents operational across your entire 
tech stack.

Key integration capabilities:

Why this matters: Your agentic AI platform will touch every critical system in your enterprise. The integration burden determines whether deployment takes weeks or quarters—and whether governance remains consistent across your tech stack or fragments into silos.

What good looks like:
Red flags:

Performance and scalability

Platform capabilities mean nothing if performance degrades as adoption scales. Production-ready enterprise platforms deliver predictable performance under load and scale gracefully as your agentic AI footprint grows.

Key integration capabilities:

Why this matters: A slow chatbot is an annoyance. A slow agent fumbling a time-sensitive business process is a disaster. Enterprise adoption demands predictable, production-grade performance that scales as usage grows.

What good looks like:
Red flags:

Total cost of ownership (TCO)

The sticker price of an AI platform is just the beginning. 
The true cost includes licensing, implementation, ongoing operations, hidden fees, and the opportunity cost of alternatives. Smart CIOs evaluate total economic impact‌ — ‌costs AND 
value created.

Comprehensive TCO analysis:

1. Licensing and subscription costs

Typical range: For enterprise deployments (250-500 users, 20-50 agents), annual licensing typically ranges from $500K-$2M depending on platform sophistication and included capabilities.

2. Implementation costs

Typical range: Implementation costs for mid-sized enterprise deployments typically range from $200K-$1M (professional services + internal resources), with timeline of 3-6 months from contract to first agents in production.

Key insight from Forrester TEI study: Organizations using platforms with pre-built infrastructure and professional services deploy in 8 weeks vs. 18+ months for DIY approaches‌ — ‌avoiding hiring 6+ specialized FTEs for ongoing 
platform management.

3. Ongoing operational costs

Typical range: Ongoing operational costs (beyond licensing) typically add 20-30% to annual TCO, including support fees, incremental infrastructure, and 1-2 FTEs for platform management.

4. Hidden costs and fees

Key insight: Hidden integration costs are typically 3-5x higher than initial estimates when using point solutions or DIY approaches. Platforms with comprehensive connector libraries dramatically reduce this burden.

5. Total Economic Impact (Costs + Value)

The most sophisticated TCO analysis includes the value created‌ — ‌not just costs incurred.

Why this matters: A platform that costs $2M annually but delivers $5M in value has a negative “cost” — it’s a profit center. Cheap platforms that deliver minimal value are actually more expensive. Always calculate total economic impact, not just purchase price.

6. Build vs Buy economics

The alternative to buying a platform is building your own. 
Here’s the realistic comparison:

Key advantage: Platform approach delivers production-ready capabilities in weeks vs. quarters, with enterprise-grade security, governance, and performance built in.

Questions that separate platforms from point solutions

Platform evaluation best practices: Infrastructure & interoperability

The challenge: Most enterprises get trapped choosing between powerful-but-inaccessible developer tools or simple-but-shallow business tools. They’re forced to compromise: either business teams wait in IT backlogs, or they build agents in isolated tools that IT can’t govern. Meanwhile, lack of strategic enablement means platform capabilities sit unused because teams don’t know how to harness them effectively.

What separates platforms from point solutions: Point solutions optimize for one audience—either developers get sophisticated APIs but business users are locked out, or business users get templates but developers can’t extend them. Patchwork approaches try 
to combine separate tools but create friction through constant context-switching. True enterprise platforms provide unified environments where business users build with no-code tools, developers extend with full SDK access, and both collaborate seamlessly—backed by comprehensive enablement that drives organizational transformation.

What best-in-class looks like: Leading platforms eliminate the forced choice between accessibility and power. Business users leverage 100+ pre-built agents and visual workflow designers to solve problems autonomously. Developers extend these solutions using robust SDKs, build custom integrations, and establish reusable patterns. Both work in the same platform with role-appropriate interfaces. The vendor acts as strategic partner—providing structured training, proactive customer success, implementation services that co-create your AI operating model, and community learning that accelerates adoption across the organization.

Example: WRITER provides an end-to-end platform where business empowerment and developer sophistication coexist. Business users access 100+ pre-built agents and Agent Builder (collaborative no-code environment) to design workflows matching how work actually gets done. Developers extend these agents using WRITER’s SDK, build custom integrations, and orchestrate complex multi-agent systems—all within the unified platform. Salesforce trained 50 non-technical business users to build and maintain their own agents through WRITER’s enablement programs. Meanwhile, WRITER’s professional services team partners with enterprises to design comprehensive AI operating models—not just implement technology, but transform how organizations work—accelerating adoption from pilot to enterprise-wide impact.

EVALUATION QUESTIONS:

how quickly can we customize these solutions?

What successful enterprise adoption actually looks like

KEY TAKEAWAYS

The evaluation paradox part two: You’ve assessed platforms across five critical dimensions. You’ve asked the tough questions. You’ve seen the demos. Now comes the harder question every CIO faces: What does successful deployment actually look like at enterprise scale?

The data reveals a critical gap. According to PwC’s May 2025 AI Agent Survey of 300 senior executives, 79% of companies are already adopting AI agents and 66% report measurable productivity gains. Investment is surging — 88% plan to increase AI budgets in the next 12 months.

But here’s what separates early adopters from true transformation: Most organizations are using agents for routine task automation, not operational re-architecture. Only 45% are fundamentally rethinking operating models, and just 42% are redesigning core processes around AI agents. The result? Broad adoption 
but shallow impact.

The strategic question isn’t “Are we using AI agents?” 
It’s “Are we using them to re-architect how work gets done?”

The organizations achieving transformational results share common patterns in how they approach maturity, measure success, and scale from pilots to production.

This section provides a realistic roadmap based on actual enterprise deployments, including timelines, resource requirements, and the metrics that separate transformation from theater.

### The agentic maturity framework: 
Your journey from pilots to production

The first 90 days determine whether you join the 79% of organizations with AI agents or the 45% achieving true operational transformation. Based on analysis of successful enterprise deployments, this timeline provides realistic expectations for moving from platform selection to measurable business value.

The pattern is consistent: Organizations that secure quick wins in weeks 1-8, validate ROI in months 2-3, and demonstrate scaling capability by day 90 build the momentum, internal champions, and executive confidence needed for enterprise-wide adoption. Those that over-plan, delay deployment, or skip the “crawl” stage typically stall in pilot purgatory.

Your first 90 days: What to expect

CUSTOMER SPOTLIGHT

Prudential’s journey to agentic AI market teams with generative AI

From a clunky homegrown tool to an enterprise platform

Prudential, a 150-year-old global financial leader, faced a challenge familiar to many established enterprises: balancing a legacy of trust and regulation with urgent digital transformation needs. Their global marketing organization needed to scale personalized customer experiences in a highly complex, regulated B2B environment.

Their AI journey began with a homegrown solution that highlighted exactly what a siloed approach creates. They had one tool for content generation and another, separate tool for pre-compliance checks. The user interface was unfriendly, the workflow was disjointed, and the output was poor. This inefficient process created significant delays—content was frequently kicked back from compliance, extending cycle times and frustrating teams.

Their vendor evaluation criteria were clear:

After testing multiple solutions, Prudential chose WRITER.

The solution: An agentic platform for business intelligence

Prudential started with content generation but quickly moved beyond basics to embrace agentic AI. Their most powerful use case is a voice-of-customer (VOC) analysis agent that executes an entire, complex business intelligence workflow autonomously:

Step 1: It ingests the data

The agent takes in a massive Excel file with thousands of unstructured customer comments from their Medallia feedback platform.

Step 2: It performs the analysis

The agent analyzes all 4,000+ rows of customer verbatims, automatically identifying and categorizing key themes, sentiment, 
and trends hidden in raw text.

Step 3: It delivers the strategy

The agent generates strategic recommendations for the business, suggesting concrete actions to improve customer satisfaction 
or fix website problems.

IMPACT

This entire workflow, which previously took analysts days of manual work, now completes in minutes. The team’s speed to deliver actionable insights increased by 50%.

CIO lessons learned

Lily Raymond (Global Marketing Strategy) and Ashley Chiretis (Director of AI) learned critical lessons every CIO should hear:

1

Don’t automate a broken process

“The biggest mistake you can make is just layering AI on top 
of a bad workflow. The real win comes when you truly deeply understand the process and really reimagine it. Use agentic AI 
as your chance to redesign how work gets done from scratch.”

2

Talk about productivity, not “efficiency”

To get teams on board, especially creative and knowledge workers, words matter. Prudential avoids corporate-speak 
of “efficiency” and instead talks about “productivity”—giving people time back for strategic, fulfilling work. As Ashley says, 
”AI does not replace jobs right now. It replaces tasks.”

3

Let your people build

You don’t need a central IT team to build every AI solution. Prudential is training its marketing team to build their own agents using WRITER’s AI Studio. Ashley, who is not a technologist, built an AI matchmaking app for an internal mentoring program in less than an hour. When you give business experts user-friendly tools, you scale innovation across the company.

Additional results

Lily Raymond (Global Marketing Strategy) and Ashley Chiretis (Director of AI) learned critical lessons every CIO should hear:

70%

faster

campaign

time-to-market

70%

adoption rate

across marketing

organization

40%

boost

in creative

capacity

\ \ Prudential’s journey with WRITER: Past lessons, present practices, future possibilities\ \ learn More](/content/blog/prudential-customer-story/index.html)

Why traditional ROI models fail for agentic AI

Traditional ROI models focus narrowly on cost savings and task efficiency: 
”How much time did we save?” This factory-floor thinking completely misses the exponential value of agentic AI.

The critical distinction

Agentic AI isn’t just a tool—it’s an extension of your team. It understands complex business objectives, creates execution plans, and autonomously delivers results across multiple systems. This fundamental difference demands a new 
measurement framework.

When you measure only “hours saved,” you miss the strategic value: revenue from faster time-to-market, cost avoidance from preventing errors before they occur, competitive advantage from experimentation velocity, and employee retention from eliminating soul-crushing repetitive work.

The four-pillar measurement framework

PILLAR 1

Efficiency & employee productivity

What to measure:

Formula: (Process volume × time savings × hourly rate) + (new processes × revenue)

EXAMPLES

Marketing team creates 50 campaigns/year, each requiring 22 hours. With agentic AI: 6 hours per campaign.

Forrester TEI validation: 200% improvement, $10M over 3 years

PILLAR 2

Revenue generation & business growth

What to measure:

Formula: (Revenue from accelerated launches × market timing) + (new opportunities × conversion × deal size)

EXAMPLES

Software company launches quarterly features. 
With agentic development/testing agents: monthly releases.

Real-world examples: CirrusMD 234%, Prudential 70% faster, Adore Me 40% traffic growth

PILLAR 3

Risk mitigation & regulatory compliance

What to measure:

Formula: (Prevented incidents × cost) + (review time saved × cost) + (error reduction value)

EXAMPLES

Legal team reviews 500 contracts/year averaging $50K value each. Agentic contract analysis:

Forrester TEI validation: 85% faster reviews, $100K over 3 years

PILLAR 4

Business agility & innovation

What to measure:

Formula: (Faster experiments × conversion × revenue) + (retention × replacement cost) + (competitive advantage)

EXAMPLES

E-commerce company runs A/B tests. 
With agentic test design/analysis:

Forrester TEI validation: 65% faster onboarding, $492K savings

How to apply this framework

Step 1

Select 2-3 high-impact processes to measure across all four pillars

Step 2

Establish baseline metrics (current state before agentic AI)

Step 3

Deploy agents and measure for 90 days

Step 4

Calculate total strategic value:

Step 5

Project annualized value and present to CFO/Board

Example Total ROI: $1.56M + $6.5M + $4.275M + $4.05M = $16.385M/year across all four pillars

This is why enterprises achieving 333% ROI focus on outcome automation, not task efficiency.

Top failure patterns to avoid

1

Starting too complex:

Build momentum with quick wins before

complex automation.

2

Treating as IT project:

Involve business process owners from day one.

3

Insufficient governance:

Establish policies and approval workflows from the start.

4

Neglecting change management:

Invest in training, celebrate wins, create champions.

5

Measuring wrong metrics:

Track business outcomes, not “AI usage” metrics.

For comprehensive guidance on successful deployment, download WRITER’s Business Leaders Guide to Agentic AI.

How to move from evaluation to decision

KEY TAKEAWAYS

You’ve evaluated platforms. You’ve built your comparison framework. You understand the technology. But you’re stuck.

Not because you don’t know which platform is best. 
You’re stuck because:

This is the reality of enterprise AI decisions today. The technical evaluation is the easy part. The hard part is building consensus across stakeholders with fundamentally different concerns and timelines.

This section provides a decision framework and answers to the toughest objections you’ll face internally‌ — ‌so you can move from analysis paralysis to confident action.

The three-constituency business case

Enterprise AI platform decisions require simultaneous buy-in from three groups with non-overlapping priorities:

FOR THE BOARD

Strategic transformation and competitive advantage

FOR THE CFO

Financial returns and risk-adjusted value

FOR THE CISO

Security, compliance, and governance at scale

## The tough questions (And how to 
answer them)

Every enterprise agentic AI evaluation surfaces these objections from different stakeholders. Here’s how to address each with credible, evidence-based responses.

OBJECTION #1

### “Why not build this ourselves? 
We have strong engineering teams.”

The surface logic: We control the roadmap, avoid vendor lock-in, and leverage our existing AI expertise.

The hidden reality: 
Build costs 3-5x more than expected. The model API is just 
the beginning. You need:

Total 3-year TCO for DIY: 
$5M-$8M plus opportunity cost of engineering team focused on infrastructure vs. business innovation.

Evidence: 
During platform evaluations, enterprise customers consistently estimate 18-24 months to reach feature parity if building internally. For example, a Fortune 500 financial services firm calculated that WRITER delivered in 8 weeks what would have taken their internal team 18+ months‌ — ‌and they would have needed to hire 6 specialized engineers at $200K+ each.

OBJECTION #2

### “Why not build this ourselves? 
We have strong engineering teams.”

“Why not just use Microsoft Copilot or Google Gemini? We’re already in their ecosystem.”

The surface logic: We already pay for these platforms, they integrate with our existing tools, and they’re from trusted vendors.

The platform vs. point solution reality: 
Microsoft Copilot is a productivity assistant optimized for Microsoft 365 workflows. It excels at helping individuals work faster within familiar applications. But it’s not an enterprise agentic AI platform:

Google Gemini provides powerful models but similar 
platform limitations:

The strategic trade-off:

Point solutions from horizontal platform vendors give you ease 
of initial deployment but constraint on what you can build. 
Purpose-built enterprise agentic platforms give you:

When Microsoft/Google make sense: If your needs are limited to productivity enhancement within their ecosystems and you don’t need complex multi-agent workflows or business-user-led development.

When enterprise platforms make sense: When you’re re-architecting core business processes, need governance for business-led development, want model flexibility, or require industry-specific capabilities.

Evidence: 
Organizations that selected Copilot/Gemini for comprehensive agentic AI later added enterprise platforms when they hit orchestration, governance, or extensibility limits. 
Starting with the right platform avoids this costly detour.

OBJECTION #3

### “What about vendor lock-in? 
We don’t want to be dependent on a single AI vendor.”

The legitimate concern: If we commit to this platform and the vendor gets acquired, changes strategy, or fails, we’re exposed with critical business processes dependent on their technology.

How modern platforms address this:

  1. Open by design architecture

Leading platforms integrate natively with your existing infrastructure rather than replacing it:

  1. Data portability

Enterprise platforms provide export capabilities:

  1. Model independence

Platforms with owned models (like WRITER’s Palmyra) 
provide optionality:

  1. Deployment flexibility

Hybrid deployment options preserve control:

  1. Contractual protections

Enterprise platforms provide export capabilities:

The real lock-in risk isn’t the platform‌ — ‌it’s the underlying 
LLM providers.

If your platform is a wrapper on OpenAI/Anthropic models, you’re locked into their pricing, deprecation schedules, and capability roadmaps. Platforms with owned models eliminate this dependency.

OBJECTION #4

“Won’t giving business users agent building tools just create more shadow AI and governance nightmares?”

The legitimate concern: If we empower business users to build agents, we’ll lose control‌ — ‌creating the exact shadow AI problem we’re trying to solve.

The governance-by-design reality

Shadow AI emerges because:

Platform-enabled governance inverts this:

  1. Supervised autonomy through built-in guardrails
    • Business users build within platform’s governance framework (not outside it)
    • Global policies automatically apply to every agent 
(no per-agent configuration)
    • Agent approval workflows prevent deployment of risky agents
    • IT maintains visibility into all AI activity through 
centralized dashboard
  2. Graduated permissions prevent risky configurations
    • Business users: Access to templates, no-code builders, 
pre-approved data sources
    • Developers: Extended permissions for custom integrations and advanced features
    • Admins: Full platform control with role-based access 
and policy management
  3. Real-time supervision scales automatically
    • Event-level monitoring tracks every agent action
    • Anomaly detection flags unusual behavior for review
    • Cost management prevents budget overruns
    • SIEM integration ensures security team visibility

The key insight:

Giving business users agent-building tools within a governed platform is more secure than forcing them to use ungoverned consumer tools outside your visibility.

Evidence:

WRITER’s supervision suite enables the best of both worlds: business-led development velocity with enterprise governance. 
The platform’s agent approval workflows and centralized visibility prevent shadow AI proliferation while enabling teams to build 
and deploy use cases faster—removing the IT bottleneck without sacrificing control.”

OBJECTION #5

“What if AI regulations change? We don’t want to be locked into a platform that becomes non-compliant.”

The legitimate concern: The AI regulatory landscape is evolving rapidly (EU AI Act, proposed U.S. regulations, industry-specific rules). If we commit to a platform and regulations change, we could face expensive re-architecture or compliance violations.

How adaptable platforms prepare for regulatory change:

  1. Architecturally flexible for policy changes
    • Configure guardrails and policies without code changes
    • Update content filtering, data handling, or access controls through admin interface
    • Modify agent behaviors to meet new requirements 
without rebuilding
  2. Compliance by design, not compliance by retrofit
    • Platforms built for Global 2000 already meet strictest requirements (GDPR, HIPAA, SOC 2 Type II)
    • Adding new compliance frameworks is incremental 
(not architectural)
    • Audit trails and documentation requirements built-in from day one
  3. Open architecture allows regulatory tool integration
    • Integrate new compliance monitoring tools as regulations evolve
    • Work with specialized legal tech providers for jurisdiction-
specific requirements
    • Don’t depend solely on platform vendor for compliance adaptation
  4. Vendor commitment to regulatory compliance
    • Enterprise platform vendors have incentive to maintain compliance (entire business depends on it)
    • Dedicated legal and compliance teams track 
evolving regulations
    • Customer base collectively funds regulatory adaptation

The alternative risk: DIY approaches and point solutions force you to track regulatory changes and implement compliance updates yourself. Strong platform vendors absorb this burden across their customer base.

What to verify:

Evidence:

When the EU AI Code of Practice was published in July 2025, WRITER was one of the first companies to sign — demonstrating the architectural advantage of platforms built for regulatory compliance from day one. Organizations with DIY or patchwork approaches face the challenge of updating multiple components independently to meet evolving regulatory requirements.

The anti-paralysis decision framework

When you’re stuck between “not enough data to decide” and “waiting too long means falling behind,” use this framework to move forward confidently.

STEP 1

Score your current state (1-5 scale)

Strategic imperative

1

AI is distant future concern

3

Competitors deploying AI, we should too

5

AI is existential—we must transform or become irrelevant

Platform readiness

1

No AI expertise, immature data infrastructure

3

Some pilots running, basic data readiness

5

Strong AI capabilities, production-ready infrastructure

Executive alignment

1

Stakeholders skeptical or unengaged

3

Interest exists but no clear champion

5

Board/C-suite actively pushing for AI transformation

Risk tolerance

1

Risk-averse culture, slow decision-making

3

Balanced approach, proven success required

5

High risk tolerance, move fast

STEP 2

Define your decision criteria (not evaluation criteria)

What would make you say “yes” with confidence?

Common criteria

Two successful pilot use cases with measurable ROI

Security review passed with no major findings

CFO approval of 3-year business case

CISO confidence in governance model

Reference calls with 3 similar organizations

Board approval of strategic direction

Time-box this phase: If you hit these criteria within 90 days, commit to moving forward. If you don’t, either adjust criteria or acknowledge this isn’t the right time.

STEP 3

Identify and address the true blockers

Often “we need more data” masks other concerns.

Common hidden blockers

Someone with veto power opposes agentic AI (fear of change, turf protection, budget competition) → Address directly: One-on-one conversations, address specific concerns, build coalition

Funds aren’t secured, or competing for same dollars as other major initiatives → Address directly: Present ROI case to CFO showing self-funding through cost avoidance

Worried about adding complexity to already fragile infrastructure → Address directly: Pilot approach proves platform integration doesn’t break existing systems

Someone with veto power opposes agentic AI (fear of change, turf protection, budget competition) → Address directly: One-on-one conversations, address specific concerns, build coalition

STEP 4

Set a decision deadline

Without a deadline, evaluation continues indefinitely. Set a firm date for platform selection—typically 90-120 days from evaluation start.

What breaks this timeline: Legitimate discovery of disqualifying information (security flaw, financial instability, technology limitation). Not “we’d like to see one more vendor” or “let’s wait for next 
budget cycle.”

The CIO decision checklist

Before final commitment, validate these 12 critical elements:

## From decision to deployment: 
Your first week

You’ve selected a platform. Now what? 
The first week post-decision sets the foundation for successful deployment.

DAY 1

Announcement and stakeholder communication

DAYS 2-3

Kickoff planning

DAY 4

Governance establishment

DAY 5

First use case finalization

Next steps:

How to engage with WRITER

These aren’t generic demos‌ — ‌they’re customized implementations of your priority use cases with your data, your systems, 
and your workflows. WRITER’s delivery team (Engagement Manager, AI Architect, Customer Trainer, and Customer Success Manager) works alongside your functional SMEs and technical builders to ensure successful adoption.

The Professional Services partnership includes:

WRITER’s goal is to enable your AI HQ within 60 days and prove measurable value with priority use cases within the first 90 days.

## Learn more about WRITER, 
a full-stack generative AI platform built for enterprise

Request a demo

Try for free