In the current market landscape, Chief Revenue Officers and growth leaders are facing a fundamental breakdown of the traditional go-to-market (GTM) engine. As organizations scramble to gain traction by implementing AI pilots, they often hit a wall: the daunting reality of their own data. Many leaders feel overwhelmed, thinking they have to have perfect data before seeing any real progress. However, this is a myth that stalls momentum. It doesn’t take perfect data to achieve real results with AI. It takes the right data for the right outcomes. The solution to moving beyond this roadblock is a data roadmap for AI that prioritizes based on business impact.
By shifting the focus from total data readiness to targeted, phased integration, organizations can finally start building an integrated data layer, the connective tissue needed for a modern growth engine. This was the resounding theme from Trilliad’s 2026 Growth Imperatives that industry leaders shared as being a top priority for GTM leaders to focus on this year to unlock sustainable success. It is the data that serves as the foundation to power impactful AI, analytics, and workflows across the customer lifecycle to deliver the connected customer experiences modern buyers expect. Which is why it is so critical for teams to move past a notion of ultimate data readiness and start prioritizing the data that needs to be connected now to start gaining real momentum.
The AI Mandate Reality Check for a Data Foundation
Data continues to be the foundation that powers experience, but with the AI era, it has a newfound importance. Sercante’s State of AI in Enterprise report reveals that organizations are dealing with more tech than ever before, yet they have yet to see meaningful results from their AI initiatives.
- The average large enterprise now manages a technology stack of over 600 applications, leading to unparalleled volumes of fragmented data (WalkMe Inc.).
- Currently, 56% of executives have yet to see a true impact on the bottom line from their AI deployments (Oxygen Staff).
- 87% of AI failures are rooted in poor data quality (RAND).
It is true that organizations need a solid data foundation to gain impactful outputs from AI. However, the belief that every one of those 600+ apps must be connected simultaneously is a recipe for gridlock. To gain momentum, leaders must identify the specific data needed for the intended outcomes and build from there.
“Uncertainty is chaos. It drains energy. It drains time. It feels out of control. Building a plan gives a sense of control. It alleviates the chaotic, stressful, anxiety-feeling that leaders feel and makes their path clear.”
- Lauren Noonan, VP of Growth & Alliances, Sercante
This sense of control with data begins when the pursuit of perfection is replaced with a disciplined, strategic roadmap.
A Prioritized Data Roadmap for AI
Creating a data foundation is not an “all or nothing” technical project. It is a strategic initiative that should be grounded in measurable, targeted business outcomes. It’s not about gaining more data. It’s about unlocking the right data. It can be overwhelming for leaders to think about connecting all their systems. That’s where the roadmap comes in. It gives leaders a clear plan to move forward with to start gaining real traction with their data and AI.
“More data does not make you better at anything. You need the right data, the right activation layer, and a team and process that knows what to do with what they are seeing.”
- Andrea Tarrell, Founder & CEO, Sercante
Establishing the vision for your data
Answering the question of “what is the end goal?” is the first step in setting your vision. Without a vision to anchor your data and AI initiatives, it can feel like departments are running in several different directions, which can cause more silos and make it feel like busy activity without a lot of real results.
“Without a vision to ground your strategy, technology can feel like motion without progress.”
- Jenna Packard, Strategy Director, Sercante
By defining the end goal first and the metrics that will be used to measure success, you create a filter for prioritization. This ensures the data initiatives are aimed at enhancements that drive measurable growth.
A sequenced path for your data and AI
As Jenna Packard shared in her article, The AI Roadmap for Enterprise, “a roadmap is highly sequenced, helping organizations understand which capabilities to build first and how each piece creates a foundation for the next.” A roadmap, or vision map, captures your end goal and measurable outcomes, then organizes the technology and data required to make it happen.
When creating the roadmap and thinking about what should come first, consider the following:
- Specific Data Sources: Which data sources must be unlocked to fuel the AI to get the results we’re after?
- Integration Effort: What is the technical and operational burden to enable this specific capability?
- Support Systems: What is the infrastructure needed to ensure data connections run smoothly?
- Data Integrity: Is there anything that needs to happen first to ensure that data will be clean, accurate, and actionable?
Answering these questions will allow the team to start placing the actions that need to happen on the roadmap to unlock the right data for the intended outcome. The end result gives teams a clear path forward to gain momentum, replacing reactive data fixes with impactful strategic discipline.
Getting Started with Your Prioritized Data Roadmap for AI
Data perfection is not a realistic end goal, and waiting for it only stalls progress. It takes a disciplined approach to step back, identify the end vision with measurable business outcomes, and then let that guide your data roadmap for AI. By prioritizing the right data for the right outcomes, you unlock the ability to gain real, measurable results with AI, fuel impactful analytics, and deliver connected customer experiences that modern buyers expect.
If you’d like support with creating your vision map for data and AI, reach out to the Sercante team. We partner with go-to-market teams daily to establish strategic roadmaps and design data foundations that result in scalable, impactful AI solutions.
