Information Transformation: Is AI Actually Changing Everything?
What AI will impact most, what it won’t — and what to do with the time you’re getting back.
What is the spreadsheet for?
There’s somebody on your team who manages a spreadsheet that’s core to your operation. Maybe it’s your bookkeeper closing out the month, your dispatcher reconciling the call ledger, your project coordinator patching the job-costing sheet. Maybe it’s you, running the analysis on your next big contract or your next big market move.
What is the spreadsheet for?
The pipeline tracker isn’t the point — the call to unstick the critical deal is. The job-costing sheet isn’t the point — the site walk to fix the estimate is. The cash-flow forecast isn’t the point — knowing when to push receivables is. The spreadsheet is the means. The action you take as a result — the call, the walk, the decision — is the end.
But it takes work — informational work — to get to the end.
AI is the next frontier of informational work. You or your team might not need to wrangle that spreadsheet quite so hard before long. You could have an AI agent for that. Which means you’d get that time back — the scarcest resource — to focus on the thing the spreadsheet is for. How you choose to reinvest that resource is the key question to wrestle with.
History rhymes, it doesn’t repeat
Spreadsheets themselves represent a historical example of this displacement and reinvestment dynamic.
In October 1979, Dan Bricklin and Bob Frankston shipped VisiCalc on the Apple II. Bricklin had been watching a Harvard Business School professor erase and rewrite a blackboard ledger every time a number changed, and he figured there had to be a better way. Turns out there was. Stores started selling the Apple II computer as a “VisiCalc accessory”. More than a quarter of every Apple II sold in 1979 went out the door to run it.
The thing the spreadsheet replaced wasn’t the thinking part of the work. It was the rote arithmetic underneath it — hand calculation, ledger posting, manual reconciliation. Believe it or not, “Computer” was an actual job title that humans held in 1979, spending all day literally doing arithmetic by hand, aggregating the results, and passing them back to a central synthesizer and organizer. I assume this was also a great time to be in orthopedics, specializing in carpal tunnel.
You might expect that this would reduce the number of bookkeepers needed across the economy — after all, much of the handiwork could now be done by digital computers, not living, breathing ones. That’s true, but it’s only half the story.
Since 1980, US bookkeeping and accounting clerk roles have shrunk by about 400,000 — but accounting jobs have grown by about 600,000. That’s a net positive. Economic historians call this creative destruction. When a new innovation arrives, it has displacing effects on the current state of work in that domain — and at the same time it opens up entirely new opportunities that couldn’t be contemplated before.
What happened? The work moved up the stack. People who’d been ledger clerks became analysts; analysts became Financial Planning & Analysis leads, business-intelligence partners, finance directors. Steven Levy named it in 1984, writing for Harper’s:
“Making spreadsheets, however necessary, was a dull chore best left to accountants, junior analysts, or secretaries… [now] senior executives who take the time to learn how to use spreadsheets are no longer forced to rely on their subordinates for information.”
— Steven Levy, A Spreadsheet Way of Knowledge, November 1984
But the spreadsheet revolution did something else, too. It didn’t just automate calculation. It made it cheap to capture, manage, and analyze entirely new information about the work — information that used to be too expensive to collect, store, and update. Shoshana Zuboff named this in her 1988 book In the Age of the Smart Machine, when she watched computers entering factories and offices. Computers didn’t only automate the work; they informated it - that is, created the need and means to capture and store previously unrecorded information. The work order. The dispatch log. The patient chart. The barcoded inventory. The customer-history note. All these now had a reason to become hard-coded into a hard drive somewhere - because they could be captured, analyzed, and stored so cheaply - digitally and not by hand.
Forty years of falling cost-to-capture-information is why even the most hands-on jobs have grown an administrative layer around them — because each piece of captured information was worth its declining cost.
AI is the next major chapter in that arc. Not the first one. Just the most aggressive yet. It’s compressing the cost of the information-shuffling layer your business sits inside — the administrative, analytical, coordinating wrapper around whatever your core business actually is.
So what’s the point from that quick history lesson? That revolution impacted your business whether you see it that way or not. The next one is coming for the same slice, faster and harder. How do we interpret that change, and turn that reality into a thoughtful decision rather than just something to cope with?
A framework: Three types of work
Every business is engaged in three broad types of work. Not jobs — types of work, mixed in different ratios across every role.
Informational work. Reading, writing, calculating, looking up, building the spreadsheet, building the deck, finding the answer. Shorthand: if you’re on a computer and there’s no human directly on the other side, it’s informational work.
Physical work. Installing the system, repairing the equipment, walking the jobsite, driving the truck, treating the patient. Shorthand: if you’re moving physical objects, it’s physical work.
Relational work. Talking to the customer, mentoring the new hire, negotiating with the supplier, developing a partnership. Shorthand: if you’re in a live one-to-one with another human, it’s relational work.
Every role on your team sits somewhere inside that triangle, and every role is a mix of all three layers, never just one vertex. Think of it as the role’s work fingerprint. The Technician sits low and left, mostly physical — but logs parts on a tablet (informational) and explains the findings to the homeowner or equipment manager (relational). The Operations Manager sits near the center because every day pulls them across all three — reviewing dashboards, walking the floor, coaching a tough conversation. The minimum slice of any one layer is almost never zero. AI gets in on the informational slice no matter how large or small.
So, do informational workers go away?
That’s the dominant narrative right now, particularly among the gloomier voices. Look at who’s writing it: academics, journalists, software engineers, consultants (hello!), podcasters. People whose work is almost entirely informational. Their sense of disruption is real — they’re watching their own jobs transform. But what comes in is a worldview error. They end up extrapolating that disruption to the whole economy, assuming most valuable work is fundamentally informational.
The numbers say otherwise. Anthropic, the company behind Claude, has their own Economic Index report from January 2026. Of 756 occupations they tracked, more than half show essentially zero Claude exposure — almost all of them physical trades, transportation, agriculture, food, and personal-care work. The BLS projects construction, healthcare delivery, and personal services to keep growing through 2034, not shrink. The categories that are mostly physical and relational keep growing despite all the AI doomerism.
This hits home for me personally at Conversint. I’ve been a technology consultant for most of my career. At Deloitte I spent much of my time researching, synthesizing, and building decks to carry an idea into a room — the kind of work AI hits hardest. Now, building Conversint, I feel like I have a team of highly paid consulting analysts at my fingertips. The result isn’t that I produce more decks. It’s that I can go deeper informationally faster, and show up (sometimes physically) better-prepared for the relational moments that matter — the conversations with people whose businesses we can actually help.
If your business is mostly informational — you run a SaaS company, a marketing agency, a professional-services firm — AI hits a larger share of your pie, and warnings of disruption apply to you more directly. Even then, the physical and relational layers of your business, may be the key to where your competitive edge may live. Or you double down on the informational — figure out how to use these tools to sling around more information faster and better than your competition.
For everybody else — the operator running a real-world business — AI hits the smaller part of the pie. Hard. The other two are largely untouched.
This dynamic brings us back to where we started: how do you reinvest the time you’re gaining back on the informational side?
Where does the time go? Augment to Expand vs. Automate to Extract
AI is effectively handing you time back to reinvest. You’re used to making reinvestment calls — does free cash flow go back into the business, or to the owners, or onto the balance sheet? AI is trying to hand you a new form of capital to allocate in terms of time saved on informational work. There are two main reinvestment paths to consider
The first path is Augment to Expand. Reinvest the time AI gives you back into the business. There are two ways to do this, and they can run at the same time:
Go deeper on the informational side. Capture and analyze information you couldn’t afford to before. The customer-history note becomes a real history. The job-costing sheet captures dimensions you used to drop. Predictive maintenance and customer churn flags emerge from data you were already collecting but never had hours to analyze. Better decisions, faster, on more questions.
Reinvest in the physical and relational layers. Your bookkeeper closes the books in half the time, and those hours go into checking with customers to review the numbers and see how their operation is doing. Your dispatcher routes calls faster and spends the extra time coaching the field techs based on patterns they see across calls. The foreman walks three more sites a week. The technician spends more time with the homeowner or equipment manager explaining what they found to help them feel reassured.
Same philosophy underneath both flavors: the time goes back into the business — to expand what you and your team can do, what you know, how intently you can serve your customers.
The second path stems from a different philosophy: automate to extract. Same productivity gain, different decision. Cut the role. Hold the output. Pocket the savings. Margin grows, market share even may as well. Headcount doesn’t. This is where the AI doomers can be shown to be onto something — the more business leaders make decisions in this direction, the more viscerally the job displacement effect will be felt.
Same technology, two philosophies. The difference isn’t in the tool. It’s in how you choose to use it.
But there is a third path. A recent Gartner survey found that AI saves sellers nearly five hours per week — but 72% of sales organizations fail to reinvest that time in higher-value activities. The default, in other words, is to lose the question entirely. Deferred capital allocation decisions result in deferred returns.
AI doesn’t decide where the time goes — you do. The hours it’s handing back are bigger than any previous informational tool, which makes the decision bigger too. Past tools handed you minutes a day; AI is handing you hours a week per person.
How you choose to reallocate that capital will shape what your business looks like a year from now.
Say it with me: what is this spreadsheet for?
The question we opened with works as a Monday-morning exercise. Thirty minutes, a notebook, two or three spreadsheets.
Pick a spreadsheet you and your team rely on every week — the one you dread maintaining, the one you always have open. Write one sentence answering the question. Not what it tracks — what it’s for. “Knowing which customers haven’t heard from us in ninety days so somebody can pick up the phone.” That’s your for talking. “Tracking customer activity” is a means, not an end.
Now write a second sentence: what would you and your team be doing more of if this spreadsheet maintained itself? “Two more customer visits a week.” “Calling the five oldest overdue invoices every Monday.” “The foreman walking three more jobsites.” Notice that almost every answer lands on the physical or relational layer of your business. That’s not a coincidence — that’s the triangle showing up in your own handwriting.
Do that for two or three of your spreadsheets. The pattern will tell you where the gap is widest, and where the reinvestment question lands first.
What it’s always been about
The spreadsheet was always a means. AI is too. The end has always been the work your business actually does in the real world — the physical work and the relational work.
Where the time goes is your call. Reinvest it — into going deeper on the informational side, or into the physical and relational sides of your business that customers actually pay you for — and your team grows into bigger, more interesting work. Extract it as margin, and you’re trading longer term compounding and expansion for shorter term gains. Doing nothing means ceding ground to your competitors who start down one path or another.
Which path will you take?
This post was collaboratively brainstormed, researched, and drafted alongside AI (Claude Opus is our go-to lately for this sort of thing), then revised and edited heavily by real humans.
Conversint is in the business of augmenting you and your workforce. If you want to get serious about reinvesting the time AI gives you back, on whichever layer of your business it lands, reach out.
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Research Sources
Anthropic. (January 2026). Anthropic Economic Index — January 2026 Report. https://www.anthropic.com/research/anthropic-economic-index-january-2026-report
US Bureau of Labor Statistics. Occupational Outlook Handbook. https://www.bls.gov/ooh/
Zuboff, S. (1988). In the Age of the Smart Machine: The Future of Work and Power. Basic Books. https://www.basicbooks.com/titles/shoshana-zuboff/in-the-age-of-the-smart-machine/9780465032181/
Goldstein, J. & Kestenbaum, D. (2015). Planet Money episode 606: Spreadsheets! NPR. https://www.npr.org/sections/money/2017/05/17/528807590/episode-606-spreadsheets
Levy, S. (1984). A Spreadsheet Way of Knowledge. Harper’s Magazine, November 1984. https://harpers.org/archive/1984/11/a-spreadsheet-way-of-knowledge/
VisiCalc. Wikipedia. https://en.wikipedia.org/wiki/VisiCalc
Computer (occupation). Wikipedia. https://en.wikipedia.org/wiki/Computer_(occupation)
Gartner. (May 2026). Survey Finds AI Saves Sellers Nearly Five Hours per Week, Yet 72% of Sales Organizations Fail to Reinvest Time in High-Value Activities. https://www.gartner.com/en/newsroom/press-releases/2026-05-19-gartner-survey-finds-ai-saves-sellers-nearly-five-hours-per-week-yet-seventy-two-percent-of-sales-organizations-fail-to-reinvest-time-in-high-value-activities