Everyone’s been talking about how to make their teams more capable with AI in 2026. Everywhere you look, companies are searching for a clear path to enable their organizations with AI.

This year has brought powerful new models and even more advanced AI tools, but not nearly enough progress in helping teams actually use them successfully.

That’s why we put together this playbook to give you actionable insight into implementing AI to transform your organization to become AI native.

Understanding the challenges

Everyone wants an AI transformation. But the reality is that integrating an AI tool or platform into your company isn’t transformation. What separates companies that see real returns from AI investments from those that don’t is the ability to implement AI methodically and strategically for the needs of the org.

Right now, most aren’t even close. We are seeing a pattern of AI hype not being met by successful adoption:

1. Lack of Strategic Partnership Alignment - 95% of enterprise AI pilots fail to deliver measurable ROI [1] .

Most initiatives never make it past the pilot stage because they lack workflow integration, feedback loops, and clear ownership. AI is treated like software, not as a system that learns and evolves with the organization.

2. Poor Tooling Fit - 49% of product teams say they don’t have time for strategic planning [2] .

Under pressure to “ship AI,” teams skip foundational alignment — clear goals, success metrics, and change management — leading to tools that don’t fit real business needs.

3. Weak Governance and Risk Controls - 72% of tested AI systems were vulnerable to prompt injection attacks [3] .

Security and governance are often treated as afterthoughts. Without version control, audit trails, and review processes, organizations risk deploying models they can’t monitor or trust.

4. Lack of Defined AI Strategy – Only 22% of organizations have a visible, defined AI strategy [4] .

Most companies are implementing AI without a clear direction or measurable outcomes. Without a strategy that ties AI initiatives to business KPIs, even successful pilots fail to translate into enterprise value. Organizations with defined strategies are twice as likely to report revenue growth from AI adoption.

5. Unregulated Shadow AI Adoption – 93% of ChatGPT use in enterprises occurs through non-corporate accounts [5] .

Employees are turning to unsanctioned AI tools to fill capability gaps left by slow internal rollouts. This “shadow AI” trend exposes companies to data leakage and compliance risks, as most of these accounts lack proper security and monitoring controls.

How AI transformation works

Bringing AI into your organization means making sure your strategy, data, technology, and governance all point in the same direction.

At the core, AI transformation is a coordination problem. You’re connecting business intent with data pipelines, models, and the humans who’ll use them every day. That requires clarity across four pillars: strategy, data, tooling, and change.

Strategy

Every AI initiative should ladder up to a measurable business outcome. Ultimately AI will generally always lead to performance and cost efficiency, but setting clear goals and expectations on where that efficiency should be driven is key. Examples include reducing the cost-to-serve, shortening cycle times, improving customer experience, or creating a new revenue stream.

Different teams will have varying needs on their AI usage. Some are best AI enabled through workflow automations for small tasks. Others might require building agentic systems that handle complex and intricate tasks. Discovering these needs through building your strategy will make searching and implementing AI tools into your organization focused and measurable. Without this anchor, you jeopardize your company’s AI transformation before its even started.

Data readiness

Data readiness is where most AI transformations quietly fail. You can’t build reliable systems on fragmented or outdated information. Before building anything with AI, map how data moves through your business: where it lives, who owns it, and how trustworthy it is.

Doing this will ensure the data you already have is accurate and compliant for your use cases you’ve identified in the strategy portion.

When your data pipelines are validated and reliable, your AI transformation gains another clear step to successful implementation.

Tooling

Tooling is the connective tissue of your AI transformation. The tooling you choose will make or break enabling your org’s teams with AI. The right AI tooling gives teams a controlled way to build, test, and evolve workflows without losing visibility or introducing risk.

The AI tools market is overflowing with new platforms and features promising to “do it all.” But not all tools are created equal. Picking the right tool boils down to whether the solution you are evaluating can partner with you to find the best way to implement their tool into your org.

Thoughtful AI solution evaluation means looking beyond surface-level capabilities to discover how each tool handles collaboration, versioning, collaboration, observability, and compliance over time. The strategy you create as the first step in your AI transformation journey will make it clear what you will require out of your tools and what will be most compatible for your org.

Governance

Governance is the key to how your organization controls, monitors, and evolves its AI systems. Strong governance keeps experimental AI use cases from turning into operational risk when used.

Every workflow should have a clear chain of accountability:

who can build who can approve who can deploy

Role-based access, version control, and audit trails are governance requirements that make your AI systems reviewable and compliant as they scale. The goal is to ensure every output, change, and decision can be traced back to its source.

When governance is in place, scaling AI becomes safe and predictable.

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2026 AI Transformation Guide

Now that you understand the core of what enables a successful AI transformation, let’s dive into what this actually looks like in practice.

This guide outlines the sequence of steps that help organizations become AI native.

Each phase focuses on building momentum through small, measurable wins while setting up the structure your teams need to scale with confidence.

Step Objective What to Produce Key Actions Owners Success Signals 1. Strategy Define where AI will move the needle and how it’s measured 1-page value thesis; ranked use-case list; data access note; governance hooks Pick 2–3 KPIs for 90 days; name owners; write one-sentence hypotheses; verify data readiness; set rollback & error budgets Exec sponsor, Domain owner, AI lead Baselines + targets set; owners named; ready use cases with data access confirmed 2. Team Turn strategy into execution with a central AI team + distributed domain leads Central standards + shared services; domain ownership & RACI Centralize platform, evals, governance; decentralize delivery; weekly reviews; approvals & auditability Central AI team, Domain leads 1 shared service live; 2 use cases in ops; approvals/versioning/audits working 3. Tools Select tools that fit workflows, learn from feedback, and give control Tool scorecard (workflow fit, learning, control); eval suite tied to KPIs Day-one vs. scale checks; NIST RMF + GenAI Profile; ISO/IEC 42001; encode gates in tool Central AI team, Security/Compliance, IT Clear tool pick; evaluation gates in tool; governance & rollback verified 4. Pilots Learn in real workflows and prove the pattern safely 1–2 scoped pilots aligned to KPIs with promotion plan Build in tool; shadow runs; small cohort; go/no-go gates; gradual expansion Domain owner, AI lead, Ops lead KPI lift in 60–90 days; incidents within budget; documented rollout pattern 5. Enablement Scale capability across teams with training, champions, and reusable assets Role tracks; champions network; ops dashboard; internal library Role-based training; feedback→tests; track adoption/value/reliability; ship templates/runbooks/eval suites Enablement lead, Domain leads, Champions 50–70% adoption in pilot groups; reusable templates; faster time-to-value across teams

Step 1: Develop a clear and defensible AI strategy

The goal in forming your AI strategy is to define where AI will move the needle and how success will be tracked, not just what tools will be used.

Start with outcomes based on KPIs that match your businesses goals. Name the few KPIs you plan to move through AI implementation in the next quarter, and spell out who owns each number. Treat this as an operating plan that fits into one page and connects goals to data, workflows, and guardrails.

Here’s what to produce in one week to kick off your AI transformation strategy:

1-page value thesis: KPI baseline and target, owner, time frame, acceptable error budget, rollback plan. Ranked use-case list: two to three quick wins, each with a measurable hypothesis. Data access note: the exact sources each use case needs, who owns them, and how you will access them legally and safely. Governance hooks: data that must be properly secured, who can build, who approves, who deploys, and what must happen before a change goes live.

AI Use Cases

Use cases should fall out of your KPIs, not the other way around. Start by asking: Which number are we trying to move in the next quarter , and where does work already happen that AI can safely improve?

From there identify the teams vital to these KPIs, and pick two to three quick wins these teams can achieve with AI. Here are some teams that we see benefiting the most quickly from AI automations:

Marketing

Marketing teams see some of the fastest returns from AI because so much of their work is repetitive, manual, and data-rich. From content generation to campaign analysis, AI helps them move faster while staying consistent with brand voice and messaging. The key is grounding outputs in verified sources and automating the routine creative work that slows teams down.

Common quick wins include:

Accelerating content creation from brief to draft. Personalizing campaigns based on audience or channel data. Summarizing campaign performance and surfacing next best actions.

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Sales

Sales is one of the most natural fits for AI augmentation. Reps spend hours on research, outreach, and documentation—all areas AI can compress dramatically. The goal is not to replace relationship building, but to eliminate the friction that keeps reps from it.

Quick wins often focus on:

Automating research and demo preparation. Drafting and sequencing personalized outreach. Summarizing calls or updating CRM notes automatically.

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Customer Support / Service

Support teams are typically the first to prove measurable ROI from AI because of the clear link between automation and service metrics. AI excels at handling repetitive requests, triaging tickets, and surfacing context for agents in real time. The result is faster resolutions, happier customers, and freed-up agent capacity for complex cases.

High-impact use cases include:

Building knowledge-grounded chat assistants for common inquiries. Automating ticket classification and routing. Summarizing customer interactions for escalations and QA.

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Step 2: Build a in-house AI team

Once your strategy is clear, the next step is assembling the team that will bring it to life. This is where strategy becomes execution.

As AI tech evolves, building a long-term competitive advantage requires in-house expertise. It is now becoming a common practice of forming a centralized AI team that can support the entire company.

The most effective structure blends centralized expertise with distributed execution. The central AI team defines the frameworks and shared infrastructure; domain leaders inside each business unit adapt and apply them to real workflows tied to their KPIs.

The AI team should focus on:

Understanding every team’s AI needs and how to enable them Building core AI capabilities to serve the whole company Execute cross-functional projects with different business units Develop consistent standards for recruiting, retention, and best practices Create company-wide platforms, such as unified data warehouses, that enable AI at scale

The rise in demand for AI specialized talent reflects this need for an in-house AI team. Roles requiring AI fluency have grown over 320% year-over-year, with specialized positions like Prompt Engineer and AI Content Creator among the fastest-growing across industries [6] . Just because these AI specific roles are growing, doesn’t mean leadership across the board will be bought in.

In forming this team, getting C-suite buy-in is critical. Leadership alignment gives this team the authority and visibility it needs to operate across business units and shape standards company-wide. Without it, the AI team risks becoming another side initiative rather than a strategic function.

Hiring a CIO marked the moment organizations got serious about the “.com” era. Now, appointing a formal AI function signals that your company is ready to operationalize AI as a core capability aka AI native.

Step 3: Choose the right AI tools

With the team in place, execution comes down to picking tools that fit your org the best. The best tool will mean something different to every org, but the common denominator is a solution you can partner with for long term success.

With the technology improving seemingly everyday, AI isn’t going anywhere. With this in mind, the right AI tooling will only become more powerful overtime, proving exponential value to your org if its the right fit.

Picking a tool like this comes through meticulous evaluation, so we laid out some frameworks below to help.

Use case evaluation

Your use case goals from your AI transformation strategy plan from step 1 will guide what features and capabilities will be initially valuable to your org. Ask what must work on day one and what must keep working at scale.

Use the use case evaluation criteria below to help guide to understand the necessities you need out of your AI tools:

Day-one viability (will this ship and help now?)

No-code building: Can non-engineers compose flows with forms, queues, schedulers, and approvals to reach first value fast? Collaboration: Shared workspace with comments and approvals so ops, product, and engineering can co-edit. AI-native features: Agent orchestration, prompt management, and basic evaluations so you can iterate safely. Workflow fit: Does it plug into where work already happens (ERP, HRIS, ITSM, CRM, data warehouse) and respect current processes?

Scale viability (will this hold up in production?)

Enterprise governance: Role-based access, versioning, approvals, and audit logs enabled by default. Integration breadth: Native connectors and API support for your data sources and SaaS apps so you’re not building glue code. Observability: Traces, logs, versioning, and real-time monitoring so you can see cost, latency, and errors per run. Scalability: Handles multi-branch, high-volume, and long-running jobs reliably. Deployment options: VPC/on-prem, data residency, and offline/air-gapped modes if needed. Security & compliance: SSO, encryption, and support for frameworks like SOC 2, GDPR, HIPAA when applicable.

This keeps evaluation anchored to outcomes and guardrails, not feature lists, and it mirrors the way your org will actually use AI internally and externally.

Governance evaluation

The market is crowded, so anchor evaluation to recognized frameworks and then pressure-test tooling fit in your workflows.

For governance evaluation, start with NIST’s AI Risk Management Framework and its Generative AI Profile to structure risk and governance across the lifecycle and account for GenAI-specific issues you might face in production [7] [8] .

For organizational due diligence, use ISO/IEC 42001 as your bar for an AI Management System. It helps you assess whether a vendor, and eventually your own team, operates AI with auditable policies, controls, and continuous improvement [9] .

Measuring success

Adapt and use the table below as a reference point to measure progress, validate ROI, and align your team’s efforts around tangible business outcomes.

KPI How to Measure Target Range (first 90 days) Business Dimension Efficiency gains Cycle-time reduction vs. baseline (e.g., AHT, TAT) 15–30% Efficiency Accuracy improvement Grounded accuracy, hallucination rate, policy pass % 30–70% error reduction Quality/Risk Adoption rate Weekly active users / eligible users 50–70% for target cohort Change Management ROI (Benefit – Cost) / Cost, with cost-to-serve tracked Positive by day 90–120 Financial Deflection/automation rate % tasks fully automated or self-serve 20–40% in target workflows Cost and CX Incident rate Policy violations or Sev-1 incidents per 1k runs <1 per 1k after hardening Governance/Resilience

Start evaluation process by discovering the best AI tooling in our guide to the best AI workflow builders for automating business processes →

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Step 4: Start with small wins to build momentum

Early projects are about learning in real workflows and proving the model for how your org will use AI. The goal is to create visible wins that build confidence among leadership, and taking those learnings to set a repeatable pattern other teams can follow.

With the AI tool you’ve chose from step 3, start by picking pilots that line up with step one KPIs and the ownership model from step two. Scope them so they can run safely in a small slice of your operations, then expand as results hold.

Choosing the first pilots

Start where the pain is obvious and the path is controllable. Pick a workflow you already measure, wire it up inside your tool’s builder, and scope it small enough to ship safely with the tool’s built-in controls.

Meaningful: Tie the pilot to one KPI people already care about (cycle time, cost to serve, backlog). Achievable: Use data sources and connectors your tool already supports. Measurable: Baseline the KPI, set a target, name an owner, and encode pass/fail thresholds as evaluation gates inside the tool.

Running AI pilots

Treat pilots like operational changes. Build in the tool, test with its evals, ship behind its approvals, and observe with its logs and traces.

Test outputs first: Run the workflow in a sandbox/shadow run with actions disabled, send proposed actions to a review queue, and compare results to SOPs and your golden set using the tool’s evals before enabling any actions. Pilot with a small group: Enable for one team or shift using the tool’s RBAC. Start in assist mode, then allow limited auto-actions behind an approval step in the tool’s release flow. Use clear go/no-go gates: Promote only when your in-tool pre-release checks pass, shadow runs meet the thresholds you set, and incidents stay within the configured error budget. Require the workflow owner’s approval in the tool before rollout. Expand gradually: Increase coverage by team, queue, or time window using the tool’s rollout controls (percent or cohort). Keep the same evaluation gates and approvals for every expansion.

Once you’ve ran a few successful pilots using this strategy, the next step is to turn those isolated wins into a rollout that will transform your org. AI nativity coming right up!

Step 5: Enable your teams

By this step you’ve built out most of your AI transformation strategy. Now the job is to get AI into the hands of your teams at scale. The aim is simple: equip every function to run, improve, and trust AI-powered workflows in daily operations, with clear ownership and measurable outcomes.

What “enablement” means here

You’re not rolling out a course. You’re establishing an operating system for how people build, review, run, and improve agents and automations in their day-to-day work.

Do three things well:

Give people the skills to participate Make it easy to contribute examples and feedback Measure adoption and value so wins compound

This is where your in-house AI team will be spending most their time, ensuring enablement is successful. Use the following best practices to understand and help guide this rollout.

Role-based enablement

Create short, focused tracks that map to how each role touches AI. Keep it practical and tied to the workflows you’re rolling out.

Executives

Learn to navigate the tool’s dashboards, read KPI and incident views, and approve releases. Practice using built-in approvals, reviewing version diffs, and applying your go/no-go rules before a rollout.

Domain owners & team leads

Learn to submit examples directly in the tool, review traces of runs, and request changes with comments. Set pass/fail thresholds in the tool’s evaluation gates and report KPI movement from the tool’s adoption/value panels.

Automation Builders

Learn the visual builder/SDK to compose steps, connect data sources, and set guardrails. Create golden datasets, configure evaluation suites, set RBAC/permissions, and use logs/traces to debug. Practice rollout controls (cohorts/percent), versioning, and one-click rollback.

Team members

Leaders within their org should lead their teams enablement your AI tool to create simple automations that eliminate repetitive tasks in your daily work. Start with approved templates or build a basic workflow to speed up processes like requests, updates, or summaries.

Establish AI champions

Pick a few high-trust people in each function to unblock peers, coach good practices, and surface patterns the central team can act on.

Host weekly office hours and a monthly show-and-tell to demo wins and lessons. Share “before/after” examples, short clips, and tip sheets in a dedicated channel. Funnel common issues into the backlog and test suite, with owners and due dates. Maintain a lightweight champions roster (name, domain, workflows owned, SLA for help).

Track adoption and value like a product

Use adoption of your AI rollout as the leading indicator, with proven value confirming you should scale. Instrument your AI tool so these metrics are visible per team.

Adoption: weekly active users over eligible users, tasks/run per user, % of suggested actions accepted, cohort retention after 30/60/90 days. Value: cycle time, manual touches per task, deflection or auto-complete rate, cost-to-serve; include time saved per workflow to socialize impact. Reliability: incident rate per 1,000 runs, latency, rollback frequency, MTTR for failed runs. Create a single ops dashboard reviewed weekly with domain owners; set promotion gates tied to these metrics before expanding to more teams.

Ship reusable automations

Package AI automations that worked, so other teams can repeat it faster and safer.

Workflow templates: pre-wired steps, approvals, and eval gates, plus inputs/outputs documented and sample data. Data connectors: documented access patterns with redaction, secrets handling, and least-privilege permissions. Eval suites: golden sets, pass thresholds per use case, and a changelog of what each version validates. Runbooks: start/stop, rollback, on-call instructions, common failure modes, and escalation paths. Add ownership and versioning to every asset and set a simple deprecation policy so outdated automations don’t linger.

Vellum’s Point of View on AI Transformation

AI transformation is an organizational shift, not a tool install. To run a winning AI transformation playbook at scale, you need a platform that lets non-engineers build, lets IT keep control, and doesn’t lock you into a single model or vendor.

That’s the gap Vellum fills.

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What Makes Vellum Different

Prompt-to-build workflows

Describe workflows in plain language and Vellum instantly generates the AI agent orchestrations that include retrieval, tools, and guardrails. For AI transformation, this enables non-technical teams to build complex AI workflows without creating engineering overhead

Shareable AI Apps

Vellum automatically turns AI workflows into shareable AI apps that any team can deploy and reuse. Once a workflow is built, it can be published as an interactive app, so other teams can run it safely without touching the underlying logic. This lets organizations build a unified library of AI workflows that scale across the entire organization.

No-code visual builder + SDK

Vellum balances no-code speed with full developer extensibility in TypeScript/Python. Enterprise teams can let ops and product iterate visually while engineering codifies complex logic and integrations—accelerating AI implementation while maintaining engineering standards.

Shared canvas for collaboration

Product, ops, and engineering co-design workflows on a shared canvas with comments, version diffs, and approvals. This directly supports cross-functional AI transformation, reducing building friction and misalignment.

Built-in evaluations and versioning

Define offline/online evals, golden datasets, and error budgets; promote only versions that pass. This operationalizes best practices so your AI transformation remains reliable and audit-ready as you scale.

Full observability and audit trails

Every input, output, prompt, and model decision is traceable. For AI governance, this provides the evidence compliance and risk teams need to enable faster approvals and safer iteration.

Enterprise-grade governance

Role-based access, approval workflows, and compliance support (SOC 2, GDPR, HIPAA). This aligns your AI transformation with regulatory requirements and internal control frameworks like ISO/IEC 42001.

Flexible deployment options

Deploy in Vellum cloud, your private VPC, or on-prem. This meets security and sovereignty needs that are critical for regulated industries and global data residency constraints.

When Vellum is the Right Fit

Vellum is built for organizations serious about operationalizing AI. If your goal is to transform how work gets done across teams while maintaining KPI’s critical to your business, this is where Vellum shines.

You’re ready to enable everyone to build AI workflows: Vellum lets teams describe what they need in plain language and turn it into working automations, no engineering backlog required. You want one standard across teams: A single platform for building, testing, and governing AI workflows ensures consistency, visibility, and compliance company-wide. You operate in a regulated or complex environment: With SOC 2, GDPR, and HIPAA support, plus VPC and on-prem deployment options, Vellum fits industries where control and data protection matter. You need visibility and accountability: Full version history, audit trails, and cost-performance insights let leaders scale safely without losing oversight. You want fast, measurable transformation: Go from idea to production-ready automation in hours, not months—then reuse, govern, and expand what works across the org.

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FAQs

1) What’s the biggest difference between adopting AI and undergoing an AI transformation?

AI adoption is using tools in isolation; AI transformation means re-engineering how work happens by aligning strategy, data, governance, and tooling so AI becomes part of everyday operations.

2) How do I know if my organization is ready for AI transformation?

You’re ready when you can clearly link business KPIs to specific workflows, have accessible and compliant data, and leadership alignment around measurable AI outcomes — not just exploration.

3) What’s a realistic timeline for showing measurable ROI from AI initiatives?

Most organizations see early wins within 90 days when starting small (2–3 pilot workflows). Full-scale transformation typically unfolds over 12–18 months, depending on team enablement and governance maturity.

4) Why do most AI pilots fail to scale?

Lack of clean data, unclear ownership, and weak governance. Without reliable pipelines and defined guardrails, pilots stay in experimentation mode — never reaching operational deployment.

5) How does governance fit into an AI transformation?

Governance isn’t red tape; it’s the foundation for scale. Role-based access, audit trails, and version control make sure teams can innovate safely without creating risk.

6) What’s the best way to manage compliance while building automations?

Use platforms that embed compliance frameworks (like ISO/IEC 42001 or SOC 2 ) into workflow design. Vellum’s governance model enforces audit-ability, approvals, and policy controls by default.

7) What should I look for when evaluating AI tools for transformation?

Prioritize tools that support cross-functional collaboration, no-code building, evaluation gates, and enterprise governance — all while fitting seamlessly into your current data and workflow systems.

8) How does Vellum fit into an existing tech stack?

Vellum connects with your existing SaaS tools, APIs, and data warehouses. It acts as an orchestration layer on top — managing workflows, models, and evaluations without disrupting your infrastructure.

9) Can non-technical teams really build automations in Vellum?

Yes. Vellum’s prompt-to-build and no-code workflow builder let business users describe what they need, while IT retains control through approvals, versioning, and audit logs.

10) How do we make sure teams actually adopt AI once it’s implemented?

Treat enablement as part of the rollout. Create role-specific training paths, appoint AI champions in each department, and measure adoption weekly (e.g., active users, automations launched, and error rates).

11) How does Vellum help scale AI across the organization?

Once workflows are proven, they can be published as shareable AI apps . Teams can deploy and reuse them safely, each inheriting your org’s access rules, governance settings, and audit controls that turn small wins into a compounding transformation.

Citations

[1] MIT NANDA (2025). State of AI in Business 2025: The GenAI Divide .

[2] Atlassian (2025). State of Product 2026 .

[3] Pangea (2025). Abusing Legal Disclaimers to Trigger Prompt Injections (LegalPwn): Research Report .

[4] Thomson Reuters (2025, June). The AI Adoption Reality Check: Firms with AI strategies are twice as likely to see AI-driven revenue growth .

[5] Cyberhaven (2025). Shadow AI: How employees are leading the charge in AI adoption and putting company data at risk .

[6] Autodesk (2025). AI Jobs Report 2025: AI Fluency and the Future of Work .

[7] NIST (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0) .

[8] NIST (2024). Artificial Intelligence Risk Management Framework: Generative AI Profile (AI 600-1) .

[9] ISO/IEC (2023). ISO/IEC 42001:2023 — Artificial intelligence — Management system .