Adobe Acrobat PDFs have been programmable for nearly thirty years. Most people are surprised to hear this: not because it isn’t true, but because almost no one has experienced a PDF that behaves like a decision tool.

PDFs are everywhere. They are the ultimate destination for reports, diligence materials, and board-ready deliverables. Yet for decades, they have functioned largely as digital binders: static, read-only artifacts that archive conclusions rather than support ongoing decision-making.

The reason is not lack of capability. Rather, it has been a question of economics. While PDFs have long supported interactivity and embedded logic, the time and cost required to design and program them far exceeded their practical value. As a result, this functionality remained largely invisible, even to sophisticated teams producing complex analytical work.

Advances in AI have dramatically reduced the effort required to design, structure, and program interactive PDFs. Tasks that once made embedded tools impractical, such as creating and maintaining hundreds of linked fields, can now be executed in the time it previously took to build a handful. This shift enables a fundamental rethinking of what a report is meant to be.

At Bracken, deliverables are not treated as archived binders of analysis. They are designed as living tools. Deep research, structured conclusions, and clear recommendations remain essential, but they are now paired with embedded tools that allow stakeholders to explore assumptions, test scenarios, and understand how decisions evolve as conditions change. 

When Static Reports Fall Short

Traditional due diligence reports are static by design. They explain assumptions, summarize findings, and present conclusions, but they don’t necessarily invite interaction. Meanwhile, the real decision-making often happens elsewhere, as stakeholders adjust inputs, explore sensitivities, and ask “what if?”

When those explorations take place in a separate file, several risks can occur:

• Attachments can be misplaced or ignored
• Readers may rely on screenshots instead of live calculations
• The connection between narrative conclusions and underlying assumptions weakens

The result is analysis that exists but doesn’t always travel with the insight it was meant to support.

Historically, the complex formulas underlying high-stakes decisions were accessible primarily to specialists such as scientists, actuaries, or analysts. Today, subject matter experts empowered by AI can translate that complexity into user-friendly tools, allowing a broader group of stakeholders to engage directly with the logic behind the decision. This shift makes participation more inclusive without sacrificing any of the necessary rigor.

Another limitation of static reports is that they are often only relevant at a single point in time. An interactive report can be revisited, adjusted, and reused—for example, later in the year—extending its value well beyond initial delivery.

Turning the Report into the Tool

Modern PDFs are capable of much more than static text. Using Adobe Acrobat’s JavaScript functionality, it is now possible to embed fully interactive calculators directly inside a report—preserving the analytical rigor of an Excel model while keeping it with the written analysis. Instead of telling readers how conclusions might change under different assumptions, the report itself can show them.

In a recent due diligence engagement, this approach was used to embed a decision calculator directly into the final deliverable. The calculator supported questions such as:

• Whether to build a new manufacturing facility
• The value of improving efficiency in a specific process
• How increasing treatment dosage might affect supply chain constraints

All of this logic already existed in Excel. The challenge was not inventing a new model but rather relocating it to where decision influences actually happen.

From Spreadsheet to Embedded Intelligence

The original calculator was built in Excel, as is typical for this type of project. Excel remains an excellent environment for developing and validating models. But Excel is not always the most user-friendly environment for consumption.

By translating the model into a PDF-based calculator:

• The analysis remained directly attached to the report
• Users could interact with assumptions in a visually appealing, logical interface
• The risk of version confusion or missing information was eliminated

What once took weeks of build time was prototyped in days, largely because AI generated JavaScript that worked perfectly in Acrobat after only a couple of rounds of debugging.

Managing Complexity at Scale

The initial version of the calculator relied on a small number of inputs. As the model matured, transparency became more important. Additional source values were displayed to show how key figures were constructed.

This expansion increased the number of text fields from roughly ten to more than one hundred. Each field needed:

• A clear, human-readable name
• A structure that allowed programmatic updates
• Consistency to support looping and recalculation

At this scale, structure is crucial. Without careful naming conventions and organization, even a working calculator becomes fragile.

Human-in-the-Loop Automation

Generative AI played a role in accelerating the most repetitive parts of the build, particularly tasks that were well-defined but time-consuming.

For example:

• Converting tables into programmable PDF fields
• Generating consistent, readable field names suitable for scripting

These are tasks that traditionally require extensive manual clicking and renaming. With the right prompts and starting materials, work that once took an hour could be completed in minutes.

Crucially, this was lot a hands-off process. Strategic control remained with the human designer. When full automation introduced errors, control was deliberately pulled back to the last working version, and AI was used selectively where it added leverage without introducing risk.

The result was speed without surrendering accountability.

Designing for Real Decisions

Not every input deserves unlimited flexibility. Most variables in the calculator were designed to move within narrow, realistic ranges, reflecting how decisions are actually adjusted in practice.

A small number of inputs were intentionally left more open, allowing users to explore wider uncertainty where it genuinely exists.

Trust Through Validation

An embedded calculator must be more than convenient and fun to use: it must be correct.

Every output in the PDF was tested against the original Excel model to ensure perfect alignment. In some cases, calculations were intentionally duplicated as a form of internal cross-checking. What might look like redundancy was, in fact, a validation strategy.

What This Enables Going Forward

Once the pattern is established, the benefits compound:

• Future calculators can be built faster
• Clients will be empowered with a new level of decision-making capacity
• Reports become living tools rather than static summaries

By embedding decision intelligence directly into reports, analysis stays connected to context, assumptions remain visible, and stakeholders are empowered to explore and forecast.