The AI sprawl problem
IT leaders now manage an average of 12 AI tools, which is a 2.4x increase in just the last three years. And yet 89% of AI usage inside large organizations is effectively invisible, suggesting many of these tools aren’t procured, monitored, or measured properly.
A new report from Forrester confirms the pattern: “Go-to-market teams often onboard these new AI tools in silos, leaving out critical conversations about duplicate capabilities, prioritization, and opportunities for broader use.” It’s no surprise to hear that up to 25% of AI spend is now at risk of being delayed because its real value is so hard to quantify.
The issue isn’t lack of investment — it’s deploying AI without a clear plan, which slows adoption and blocks scale.
Forrester’s GTM AI deployment model
The report, “Introducing Forrester’s AI Deployment Model For Go-To-Market Functions,” gives IT and revenue operations leaders a three-step framework for moving from reactive tool accumulation to strategic deployment.
The guiding principle? “Avoid reactive deployments and instead focus on AI initiatives or use cases that are aligned to strategic goals, technically feasible, and operationally sound.” Translation: Deploy fewer tools that work together.
Vision & Strategy
Align initiatives with enterprise strategy, business unit objectives, and customer needs. Define measurable outcomes and success criteria before procurement.
Define & Deliver
Assess capabilities and gaps, prioritize by business impact (not vendor pitches), and build implementation plans with integration, training, and workflow alignment.
Govern & Optimize
Track performance, adjust based on results, and identify new opportunities for innovation.
The infrastructure gap
The question for IT leaders is how they can put this framework into action. This is where consolidation matters because deploying AI onto a fragmented collaboration stack won’t work.
Imagine this common scenario. Your teams want to use AI for roadmap planning but data lives in Jira; strategy docs are in Drive; stakeholder input is in Slack; and goals are gathering dust in SharePoint or Coda. Your teams are dealing with integration and workflow friction before they’ve even begun.
How Miro addresses the gap
When organizations consolidate their AI deployment with Miro, it helps them increase speed to value significantly. Here’s how.
Align strategy and delivery in one workspace. Use Goals and Portfolios to connect company strategy to initiatives, surface dependencies, and plan and execute in one place.
Integrate AI where work happens. Leverage 250+ integrations and bring your own AI key. Plug in enterprise knowledge via Gemini Enterprise, Glean, Amazon Q, or Microsoft Copilot so outputs stay accurate.
Execute and govern. Central boards keep requirements, designs, runbooks, and status in one view — improving auditability and compliance.
Want to know what it looks like in practice? When Miro Partner Smart System Guild collaborated with FREITAG using AI workflows, they were able to build a new enterprise resource planning system twice as fast and at half the projected cost, with AI hitting 80% accuracy on requirements.
Concrete next steps you can take today
Use Miro’s AI Strategy Blueprint to help guide your organization’s transformation journey:
- List every AI tool in use and its owner
- Map each tool to a business objective and workflow
- Identify duplicate capabilities and unused licenses
- Confirm data sources, access controls, and integrations
- Document data governance and access policies
- Prioritize three initiatives with clear ROI, owners, timelines
- Move planning and status into a single Miro board (Goals + Portfolios
AI should make teams faster and smarter. When strategy, work, and AI share one workspace, you scale with confidence instead of chaos.
