Getting AI Ready: What It Really Takes to Succeed at Scale

There’s no denying it—AI is transforming how we work. From predictive insights to automated responses, organizations across industries are eager to harness its potential. But as the appetite for AI grows, a critical challenge has come into focus: many organizations aren’t operationally ready to succeed with AI.

It’s not the algorithms or the tools that fall short—it’s the foundation beneath them. Without high-quality data and well-structured processes, even the most advanced AI initiatives struggle to deliver meaningful results.

This raises the stakes for getting the groundwork right—and it also reframes how we evaluate AI platforms. It’s not just about having powerful capabilities, but about how well those capabilities are connected to clean data, relevant workflows, and responsible governance.

This is where ServiceNow’s vision for AI stands out. As the company puts it: “AI is only as powerful as the platform it’s built on.”

Let’s explore what that really means—and what it takes to build an organization that’s truly AI-ready.

The Problem: AI Can’t Fix Bad Data

Let’s get real—bad data = bad AI outcomes. Large Language Models (LLMs), no matter how sophisticated, are only as effective as the data and context they learn from. If your incident tickets are riddled with vague descriptions, inconsistent updates, or poorly categorized issues, your AI won’t magically clean it up. It learns from the mess—and replicates it at scale.

This isn’t just a theory. According to Gartner, 30% of generative AI projects will be abandoned after proof of concept by 2025 due to “poor data quality, inadequate risk controls, escalating costs or unclear business value” [1].

The takeaway? AI-readiness starts with operational discipline.

Good Data Starts with Good Process

Improving data quality doesn’t require a massive overhaul. It starts with raising the bar for how your teams document, update, and resolve work:

• Clear, consistent ticket descriptions
• Timely updates and status changes
• Accurate categorization and closure codes

These seemingly small details shape the data that powers your AI—and ultimately, the outcomes it delivers.

Gartner frames AI-readiness as a living practice: “Data readiness for AI is not something you can build once and for all... It is a process and a practice.” [2].

That process includes three key pillars:

• Alignment: Ensuring your data fits your AI use case
• Qualification: Continuously measuring quality, trust, and semantic clarity
• Governance: Defining standards, roles, and validation procedures for data use

These principles map directly to how we manage service data today. Without structure and stewardship, AI becomes a mirror of our existing inefficiencies.

Why ServiceNow Is Built for This Moment

What makes ServiceNow stand out in the AI space isn’t just its technology, it’s the ecosystem. The Now Platform combines AI Agents, enterprise data, and automated workflows, enabling organizations to extend AI across every corner of the business.

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Let’s break that down.

🔹 AI Agents:

Intelligent assistants embedded in workflows that summarize tickets, suggest next steps, and reduce time-to-resolution.

🔹 Data:

The Workflow Data Fabric unifies structured and unstructured data from across your tech stack, providing the context and quality AI needs to operate effectively.

🔹 Workflows:

Where automation, policies, and guardrails live. With flow governance and orchestration built in, the platform ensures AI doesn’t act without purpose.

Together, this trio reflects ServiceNow’s AI architecture principle:

“AI + Data + Workflows = Transformation at Scale”

This is where many generic LLM-based approaches fall short. Without aligned data and workflows, AI remains disconnected from execution.

Flexibility Without Compromise

ServiceNow offers LLMs trained specifically on enterprise workflows, with content and models fine-tuned for ITSM, HRSD, CSM, and beyond. These models are built using relevant, high-quality, anonymized data that reflects real-world usage patterns across industries.

And for organizations that have already invested in proprietary models or external providers? ServiceNow’s flexible architecture supports bring-your-own-LLM strategies, ensuring you’re never locked in and can adopt AI with confidence and control.

Getting AI Ready: It Starts Now

If your organization is looking to adopt AI, the path forward isn’t just technical—it’s operational. Before scaling automation or deploying generative AI assistants, ask:

✅ Are our processes consistent and well-governed?
✅ Is our data complete, structured, and accurate?
✅ Are our teams empowered to maintain quality at every step?

However, our advice is: don’t postpone the journey, waiting for a “future perfect time” to begin. You don’t need perfect data or flawless processes to start using AI—but you do need to be intentional about how you’ll continue improving the way you work and which technologies will support you along the way.

Successful AI adoption isn’t about waiting until everything is in order. It’s about continuously building a foundation that allows you to grow, adapt, and scale as your capabilities mature.

That’s why it’s so important to start where you are—with a platform that supports flexibility, visibility, and quality at every stage of your AI journey. Whether you're just beginning your AI journey or facing mounting pressure from the board to accelerate adoption, it's critical to start with a foundation that can scale without locking you in.

With its open architecture, purpose-trained LLMs, and ability to integrate your own models, ServiceNow offers a flexible and future-proof path to enterprise-wide AI transformation. Even if your data and processes aren’t perfect today, the platform enables you to scale AI across the enterprise with purpose and control.

As we said earlier: AI is only as powerful as the platform it’s built on.

Let’s build your AI readiness roadmap.

Getting AI ready isn’t about checking a box—it’s about building the right foundation for long-term success.

From process design and data quality to platform optimization and AI adoption strategy, we help organizations prepare, scale, and evolve with the Now Platform.

Let’s talk about how we can support your next step—whether that’s exploring AI capabilities within the Now Platform or accelerating automation across the enterprise.

👉 Contact us to start the conversation

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