If you’re a CIO or CTO shaping your 2026 AI strategy, here’s the uncomfortable question I want you to sit with:
Are the productivity tools your organisation actually runs on built for human-AI collaboration… or only for human-to-human work?
Because almost everything still in use today was designed before 2023. And that creates a massive, hidden problem most leaders haven’t fully faced yet.
The Enterprise Context Gap
Here is something true since 2024 that still surprises people: AI models can read your Excel files and Word documents just fine. The problem is not that AI cannot see what’s in your spreadsheets. The problem is that it cannot see what your spreadsheets actually mean — and the difference between those two things is the enterprise context gap.
Think about how humans actually use Excel in an enterprise setting. The Q3 budget workbook doesn’t look like a clean CSV. It has:
- Colour coding — red on over-budget cells, green on tracking-well, yellow for review. None of this is in the data. It’s in the formatting. Your finance team relies on it to read the sheet at a glance.
- Merged cells and spatial layouts — headers spanning three columns, summary blocks positioned to the right, whitespace signalling logical groupings. Flat data extracts obliterate these spatial relationships.
- Conditional formatting rules — dynamic colour scales, data bars, icon sets. These aren’t decorative. They’re decision-support signals, tuned over months by the person who owns that workbook.
- Formulas, cross-sheet references, and named ranges — the logic layer. An AI that sees only cell values without the formulas that produced them is missing half the story.
- Comments, notes, and annotations — threaded discussions attached to specific cells. Strip them out, and the AI works with incomplete intelligence.
Word documents carry the same problem in a different shape: heading hierarchies encoding document structure, tracked changes carrying negotiation history, embedded charts and SmartArt, template styles signalling “board paper” versus “internal memo.”
When you convert an enterprise Excel workbook to CSV for AI consumption, or a Word document to plain text, you are not “making the data accessible.” You are amputating the semantics. The AI gets the words and the numbers. It loses everything your team encoded in colour, layout, formula logic, and document structure.
The Writing Problem Is Worse Than the Reading Problem
Let’s say your AI agent reads the Q3 budget, understands the colour-coded semantics, and formulates a perfectly reasoned update to row 47, column D. Now it needs to write that update back into the workbook.
It cannot. Not into the real Excel file. It can generate a new CSV. It can suggest a value. But it cannot open the actual .xlsx, navigate to the correct sheet, locate the exact cell, preserve the conditional formatting rules, respect merged cell boundaries, update dependent formulas, and save — all while keeping the workbook in a state your finance team can open and trust.
Word is worse. An AI can draft a section of a report. It cannot insert that section into the live .docx with the correct heading style applied, the table of contents updated, the cross-references intact, and tracked changes recording who made the edit. Those operations require structural understanding of the document model that goes far beyond text generation.
So the enterprise finds itself in an absurd position: AI agents can reason about the business. They just cannot touch the tools the business actually uses. The last mile — the distance between AI output and the formatted document your stakeholder needs to see — is still walked by a human, manually.
The Mental Model Shift
The industry’s response so far has been middleware: connectors, parsers, RAG pipelines, screenshot scrapers. Each is a patch on a structural problem. They add latency. They degrade fidelity. They require you to strip the semantics before the AI can work, then manually reconstruct them afterward. That is a fundamentally lossy pipeline, and it will never close the gap.
The criteria you use to evaluate enterprise software need to change:
| 🔴 Old Mental Model | ✅ New Mental Model |
|---|---|
| “Does this tool have an API?” | “Is every data object — and its formatting, its layout, its semantic context — natively addressable by AI?” |
| “Can we connect our AI platform to this tool?” | “Was this tool architected from the start for human-AI collaboration, or is AI access an afterthought?” |
| “What features does the tool offer?” | “What is the semantic fidelity gap between what a human sees and what an AI agent can access?” |
| “How do we train users on the new AI features?” | “Does the tool require any retraining at all, or does it preserve existing workflows while making the underlying data AI-native?” |
| “Can the AI generate the right answer?” | “Can the AI insert that answer into the live document with the correct formatting, template, and audit trail — or does a human still need to walk the last mile?” |
This is not a technology problem. It is a procurement philosophy problem. The question “can my AI agents work with this tool at the same level of fidelity as my human employees?” needs to be on every RFP, every vendor evaluation, and every architecture review from this point forward.
One Practical Experiment: KASUMI
So what does a tool built from the ground up for human-AI collaboration actually look like?
KASUMI is an open-source AI-native workspace I’ve been building — one practical experiment in this new design philosophy. It keeps the exact same Excel and Word experience your teams already know… but rebuilds the data model underneath so AI agents have first-class access to formatting, structure, and semantics.
KASUMI has two shells:
- NEXCEL — a spreadsheet interface that looks and feels like what your team already uses. But underneath, every cell, every style, every formula, and every spatial relationship is a first-class object in a structured data model. An AI agent doesn’t need to “parse” the spreadsheet — it addresses cells, rows, columns, and styles directly, with the same semantic fidelity a human has when looking at the screen.
- WORDO — a document editor built on semantic blocks rather than a flat character stream. Paragraphs, headings, tables, and images are discrete addressable objects. An AI agent can insert a section, update a table, or restructure a document without losing the template context or the formatting inheritance chain.
The critical design constraint: the user interface does not change. If your team knows Excel, they know NEXCEL. If they know Word, they know WORDO. Zero retraining. Flat learning curve. The goal is not to teach humans a new way to work — it is to make the tools they already know AI-addressable from the ground up.
The front end stays familiar. The back end is rebuilt for human-AI collaboration. The AI gets the full semantic picture — data, formatting, structure, layout, formulas, comments — not a stripped-down extract. The human never notices the difference, except that their AI agents suddenly become dramatically more useful.
What KASUMI Does — and Doesn’t — Do Today
KASUMI is not a finished product. It is an active open-source research and development project — a concrete experiment, not a polished enterprise platform.
What it handles today: semantic spreadsheet operations with formatting awareness, structured document editing with block-level AI access, programmatic cross-module transfer between NEXCEL and WORDO, and AI-driven data cleanup that preserves layout context.
What it doesn’t yet handle: pixel-perfect reproduction of complex Excel charting, regulatory-grade Word document formatting with full style inheritance, and the long tail of Office features that enterprises have built two decades of workflow around.
But the honesty of that boundary is part of the point. The conversation enterprise technology leaders need to be having is not “what AI tool should we buy?” It is “what would it mean for our core productivity tools to be rebuilt around the principle that both humans and AI agents are first-class users — with equal access to the full semantic richness of the data?”
KASUMI is one attempt to answer that question. It is not the only possible answer. But the mode of thinking it represents — design for human-AI collaboration from the data model up, preserve familiar interfaces, treat formatting and layout as first-class semantic carriers — is, I believe, the direction the entire industry needs to move.
The Bottom Line
In 2026, your AI models are not the bottleneck. Your AI budget is not the bottleneck.
The bottleneck is the assumption that the tools your organisation runs on — tools designed in an era when humans were the only intelligence — can be retrofitted for human-AI collaboration with middleware and parsers. They cannot. Not without losing the semantics that make those tools valuable in the first place.
The enterprises that win the AI transition won’t be the ones with the biggest model budgets.
They’ll be the ones who fix this structural bottleneck first.
So here’s my real question for you:
When you look at the productivity tools your organisation relies on today — how big is the semantic gap for your AI agents?
Drop your thoughts below. Have you seen this exact problem in your stack? What are you doing about it?
Barry Li is a PhD candidate at the University of Newcastle. He builds KASUMI, HASHI, and other AI-native tools, and writes about what he learns from the process. KASUMI is open-source and under active development — the repo is available at github.com/Bazza1982/KASUMI.
