Ready or Not: 7 Ways to Format Construction Project Management Spreadsheet Data for AI in 2026
Brain, strain, train, gain: What’s the common denominator? AI.
We’re already using it, but most organizations in 2025 are still reacting to AI, using it in pre-defined ways via ChatGPT (let’s be honest) or other public models and one-off use cases.
In 2026, this is likely to change: Organizational use of AI is going to evolve so that forward-thinking companies will be incorporating AI into unique, homegrown workflows and systems, for their unique and specific goals.
Traditional methods of project data collection, such as spreadsheets, email attachments and disconnected processes and data formats, can limit AI from being accurate. Having a lot of unstructured data can impact the effectiveness of the results.
If you haven’t already started implementing data governance best practices, here’s one more reason to start.
If your project data is still living in spreadsheets and email attachments, it will be difficult to take full advantage of AI capabilities in a systematic way.
We have identified 7 areas you’re probably using spreadsheets today, that can be optimized for AI tomorrow.
Read on to help your organization transition away from dysfunctional data and prepare to leverage AI in the coming year.
Get spreadsheet data ready for AI in 2026
Most large organizations have some kind of software in place to track capital planning at the corporate level; but for many, the day-to-day tracking of budgets and project tracking still live in spreadsheets, where info must be entered manually: Formatting is inconsistent, updates are irregular and version control is a problem.
As a result, AI can’t reliably learn from or interpret information from these spreadsheets. Inconsistent headers and naming values make it difficult to see patterns, forecast or automate tasks because the data lacks consistency and structure.
One common theme has emerged when it comes to requirements for AI-enabled data analysis: Consistency. Consistency of title and number format, definitions, naming conventions: The biggest way to get your data ready for AI is consistent data governance.
1. Standardize spreadsheet column names.
Work with other stakeholders to get consensus on naming conventions for spreadsheet fields.
For example, in all instances across departments, use “Actual Cost,” rather than “Actuals,” “Actuals-Q4,” “Spend,” “Cost,” or any other one-off name other teams may be using. AI will treat column headers as totally different datasets unless they’re standardized.
What you can do now:
- Stakeholders agree on a standardized naming convention
- Create a simple “approved fields” list for every project spreadsheets
- Enforce best-practice usage among individual team members, going forward
2. Define data types and lock them in.
AI struggles when the format varies for data types. For example, data may not be usable when dates are formatted a like “12/5/25,” “Dec 5,” and “2025-12-05.” Make data types explicit.
For example:
- Dates should be formatted YYYY-MM-DD
- Establish consistency for formatting decimal points, currency symbols and other number formats
- Costs should be formatted as numbers, without symbols or commas
- For example, use consistent Status fields and approved values, such as “Active,” “On Hold,” and “Closed”
What you can do now:
Convert a highly used, existing spreadsheet to the standard naming convention, and use that file as the template going forward
3. Create a single main sheet per domain (not per PM).
It's likely that your project managers (PMs) currently each have their own method and version for spreadsheet documentation. For AI use, this effectively creates dozens of mini-databases. To prepare for AI in 2026:
- Roll up budgets into a single budget sheet
- Roll up schedules into a single schedule sheet
- Roll up risks into a single risk register
What you can do now:
- Identify the most duplicated or referenced sheet across your PM teams
- Merge them into a single consistent master file
- Use a tab for each project, not a separate file for each project
4. Avoid free-text fields where possible.
If ten different PMs were to write “status” in their own words, you’d get: “On hold,” “PM reviewing,” “Awaiting GC,” “Need pricing” ... you get the idea! But AI can’t group these together in a meaningful way as of this writing, so this type of non-standard information can’t really be used as an AI input.
One of the biggest ways to get your data ready for AI is to standardize inputs by eliminating free-form text fields wherever possible. (Consider this job security.)
What you can do now:
- Create standardized drop-down headers and values
- Collaborate with all project managers to ensure they become controlled lists in all spreadsheets
- Provide a definition for each in a separate tab called “Definitions.”
An example of standardized values could be:
- Header: Status Values: Oh hold, In Queue, In progress, In review, Pending approval, Complete
- Header: Project phase
- Values: Design, Procurement, Permitting, Construction, Punchlist, Close-out, Handover, Maintenance & Operations
- Header: Asset types
- Values: HVAC, Plumbing, Electrical, etc.
- Header: Critical path
- Values: Yes, No
- Header: Work type
- Header: Funding source/s
- Header: Priority
- Header: Program roll-up, etc.
5. Version Control
Does this look familiar? Documents titled Budget-FY26, Budget_FY26, Budget_2026_final (2), Budget_v4_final_am-final (3). Even the most organized worker has downloads like these. Almost all of us are guilty!
Similar to the text fields issue above, inconsistent naming conventions reduce the accuracy of AI output. Now is the time to get documents in shape so you’re ready to incorporate AI in the very near future.
What you can do now:
- Get consensus that spreadsheets live in one shared location
- Agree on a naming convention, and rename files using a clear, agreed-upon standard.
- HINT: Starting with the date in a YYYY-MM-DD_Name format is an instinctive, simple format that is easily sorted automatically.
- Lock and rename older versions to avoid one-off editing, and the need to reconcile versions
6. Add data dictionary definitions.
A “data dictionary” doesn’t have to be complicated: AI needs to know what your field names actually mean. Your AI model is unlikely to guess correctly every time unless you define terms clearly. And small mistakes can lead to big expensive errors.
For example, does “Budget Variance” mean “Original Budget vs. Current Budget”? Or does it mean “Current Budget vs. Actuals”?
What you can do now:
- Create an additional tab called “Definitions”
- For every column, include: A short universal definition; how it’s calculated, dates or other parameters; and whether it is required or optional
- Use the same definitions in all documentation across the program
This is a simple but powerful way to get ready for organizational AI.
7. Capture changes as events, not overwrites.
We have good intentions to deprecate old versions when we have a new method to capture data. But AI needs historical information to identify patterns. Unfortunately, spreadsheets often overwrite old values, and people tend to hide or overwrite old info to avoid confusion.
This is a good instinct, but instead of overwriting, consider: Add a new row when a cost changes, add a timestamp, or keep the old values but clearly mark them as old.
What you can do now:
Take one spreadsheet where data is usually overwritten and add a “Change Log” tab. Note values like:
- Field
- Old value
- New value
- Date of change
- Person who made the change
This is the kind of data AI can use for forecasting and exception detection. It creates a foundation that AI can query, summarize or use to create predictions.
Historical data is especially important in construction to identify risks and bottlenecks.
All of these data best practices are beneficial for more than just AI. They save time and money for many other business functions, as well:
- Help teams get out of their silos to collaborate and create accountability
- Improve data accuracy
- Reduce errors related to data entry and overlooked information
- Standardize data for other systems, APIs and integrations
Of course, at the end of the day, spreadsheets can only get you so far. The next step for many capital project owners in 2026 is to move to a platform that incorporates AI from the jump, along with third-party integrations to loop in ERP, BIM, CMMS, GIS and other project data.
Kahua is at the front lines of AI construction project management integration, and we would love to talk to you about your unique needs and goals for 2026.
