Many digital strategies launch with clear intent but stall out before delivering measurable results. The causes are common:
Example:
A retail brand sets a goal to “improve customer experience” but doesn’t define how it will be measured. Is it Net Promoter Score? Repeat purchase rate? Reduced support call volume? Without a clear target, teams build features that don’t move the needle.
Time-to-value is the time between starting a digital initiative and achieving a measurable business result.
Examples of value:
Reducing customer churn by 5% over a quarter - Increasing average order value by 10% in six months - Cutting onboarding time for new employees from 14 days to 5 - Automating 40% of support tickets to reduce manual workload
Teams that focus on time-to-value work backwards from these kinds of goals, rather than forward from abstract planning documents.
AI can help teams move faster by reducing guesswork and automating manual analysis.
Here’s how:
Example:
Instead of spending 3 months interviewing stakeholders, a bank uses AI to identify that 35% of customer service calls are about password resets. That insight becomes a high-priority automation use case that saves thousands of hours annually.
Even with AI tools available, many teams struggle to make progress. The blockers usually fall into one of four categories:
If your team can’t clearly state the business outcome you’re targeting (like “increase account sign-ups by 15% in Q4”), it’s impossible to measure value. Work must be tied to outcomes, not activity.
If customer data lives in three different systems that don’t talk to each other, AI can’t help much. Connecting your data sources is a critical first step.
You might have smart people on staff, but not the right experience to design, build, and scale AI-enabled solutions. If your team isn’t sure how to evaluate a model or prototype quickly, that’s a red flag.
Checklist:
Does your team know how to define and scope AI use cases? Can you validate a prototype in under 4 weeks? Do you have a shared understanding of how AI integrates into existing systems? If not, you likely need outside support or tools to fill the gap.
It’s common to see a 50-page strategy doc that never gets implemented. Why? Because it wasn’t built with delivery teams. A good strategy shows how the work will get done in the systems teams already use.
Example:
Saying “we’ll improve operations” means little. Saying “we’ll reduce average warehouse picking time from 90 seconds to 60 seconds using AI-driven routing” gives teams a clear target to build against.
To reduce time-to-value, organizations are adopting two proven models:
Instead of treating strategy as a one-time planning exercise, high-performing teams operate in short cycles:
Each step builds momentum:
Start with a specific business goal, then: - Identify candidate use cases - Score them for value, feasibility, and speed - Prototype quickly - Scale what works
Example:
An insurance provider wants to reduce claim processing time. They identify a use case for automating document review with AI. A prototype reduces review time by 60%. It’s then scaled across teams.
Diagram helps organizations move from strategy to measurable outcomes by:
Example:
A healthcare company used Diagram to find that 30% of patient intake time was spent on manual data entry. Within 3 weeks, they had an AI-powered intake assistant in pilot that cut intake time in half.