Each time you complete a sprint (meaning a unit of work that takes place over a preplanned period of time), send a report to the customer about what took place, as well as what the next sprint will entail.
How do you do that? What does being transparent with the customer actually look like in practice?
What they don’t know is how to get to that deliverable. After all, if they’ve hired consultants, it’s usually because they need help turning their vision into a reality.
With these insights, the customer can begin to understand what the project will look like on a day-by-day, week-by-week and month-by-month basis. In turn, the customer can establish confidence that the team has a detailed plan for completing the project, and that it will be possible to track progress continuously once work is underway.
In short, customers face a great deal of uncertainty. This often translates to anxiety as well.
Why data projects require transparency
And I’m not talking here about periodic check ins or updates. I mean establishing a systematic approach to keeping customers informed at all stages of the project – and being open with them when challenges arise, as they inevitably will.
Leading with the results is valuable for two main reasons. One is that it gives customers confidence that you remain focused on the goals they hired you to achieve, even when you’re deep in the muck of day-to-day work where it can become easy to lose sight of the final deliverable.
You can’t predict every potential issue that may arise once a project starts, but you can often predict many of them. For instance, you may know that a certain data resource may be difficult to access, or that data quality challenges could slow down part of an epic.
- How long the project is likely to take to complete. Will it take weeks, months or years? Without the requisite in-house experience, businesses usually have no way of establishing an anticipated timeline.
- Which resources the project requires. Customers often don’t know which types of data assets, tools, processes and personnel are necessary to complete the project.
- Which challenges may arise. If you’ve never completed a complex data modernization project before, you probably aren’t sure which types of issues may arise in the course of the project that, if not properly managed, could send it sideways.
- What the risks of failure are. Customers usually have little sense of how much risk they’re undertaking to pursue a project, or what the chances are that the project will not work out.
For consultants who spend their days “in the trenches” of complex data modernization projects, it can be easy to want to focus on technical processes and avoid thinking about the customer until it’s time to hand off the deliverable. But the reality is that the more visibility the customer has into what you’re doing at every step of the process, and the greater the customer’s confidence in the value of what you’re doing, the more likely the project is to succeed. Transparency vis-à-vis customers is key, even if it’s not always easy.
To mitigate these challenges and instill confidence, it’s critical for consultants to operate in a transparent, well-organized fashion. Much more than simply giving lip service to customer communication, consultants need to provide detailed, step-by-step plans to customers, and remain in constant communication with them as a project unfolds.
Editor’s note: Updated July 2026 to reflect current industry context
A systematic approach to project transparency
By Gabriel Klock
The stakes have only grown in recent years. Enterprise organizations now face compounding pressure from technical debt, cybersecurity exposure in aging systems, and the expectation that modernization programs will accommodate AI-assisted delivery from the outset. Customers arrive with sharper questions and tighter timelines, which makes the consultant’s obligation to communicate clearly even more consequential than it once was.
Establish a clear roadmap
When my team begins an AI or data modernization project, the customer typically knows what they want the deliverable to be, such as a data analytics system that lets their business make data-driven decisions, or an AI tool trained on their custom data.
At my firm, it hinges on systematically implementing each of the following key features as part of every project.
Identify risks and roadblocks
Finally, consultants should adopt a practice that I like to call “leading with results.” What I mean is emphasizing at every step of the project what the ultimate outcome is. The project kickoff should include a presentation of what the customer will receive when work is complete, and the summary reports should also explain how progress within each sprint maps onto the ultimate deliverable.
By providing detailed reports like these on a regular basis, you maintain an open communication channel with the customer. Systematic reporting is often more effective at keeping the customer in the loop than periodic check-ins or mere promises to be available upon request to discuss project progress.
Communicate about unexpected problems
But that doesn’t mean a good consultant simply completes the project and delivers the results. On the contrary, successful AI and data system modernization projects involve extensive transparency and reporting between consultants and customers.
First, after meeting with the customer to understand their desired deliverables, the consultant should lay out a clear and detailed roadmap. The roadmap should break the project into stages – including large ones, like epics (to use agile project management terminology), and smaller ones, like sprints – and provide an estimated timeline for each one.
Generate sprint summary reports
When businesses hire consultants to help implement an AI or data modernization project, their main goal is to achieve whatever outcomes they hope to realize via the project.
And second, giving customers a concrete sense of what you’ll deliver helps them communicate the project’s value clearly to other stakeholders inside the business. This is especially important in the event that projects arise or that some business users begin questioning why a data modernization project is taking so long. When the customer can articulate clearly what the outcome will be and how it benefits the business, they’re in a strong position to “sell” the project to internal stakeholders.
Allow me to explain, drawing on my experience helping manage complex AI and data science projects for Indicium, a data modernization consultancy.
Lead with project results
As a consultant, you should be upfront about these anticipated challenges with your customer and what your plans are for handling them. Proactive communication is another way to establish confidence on the customer’s part.
While it may be tempting to keep the issue to yourself and work through it, it’s better to tell customers about it, and how you are addressing it, proactively. Being secretive sets you up for failure in the event the customer finds out about the issue and assumes you were trying to hide it.
This is especially true as more organizations shift away from large-scale replacement initiatives toward incremental transformation, where the scope of any single sprint may be narrow but the cumulative direction must remain legible to stakeholders at every level.
Transparency as the key to data modernization success
In addition to getting ahead of potential problems you anticipate, be transparent with customers when unanticipated problems arise – as they often do in complex data modernization projects.
To be more specific, customers typically don’t know:
