# AI Made the Work Cheap. Now We Need Software to Prove Someone Actually Did It. 2026-09-17 · Somerset County, New Jersey · Technology Leapora AI’s Taskeen is built around a strange new workplace problem: when polished output becomes easy to produce, a checkmark saying “done” may be the least interesting thing a manager needs to know. Leapora AI’s Taskeen is built around a strange new workplace problem: when polished output becomes easy to produce, a checkmark saying “done” may be the least interesting thing a manager needs to know. --- For most of the modern software era, workplace technology has been built around a fairly simple promise: make the work easier to produce, easier to move and easier to mark complete. We built dashboards for it. We made tasks draggable. We added status colors, automatic reminders, completion percentages and increasingly cheerful little checkmarks. Then generative AI arrived and made one part of that system dramatically cheaper. A memo can appear in seconds. So can an analysis, a campaign outline, a project update, a recommendation or a polished deck that looks suspiciously ready for the executive meeting. Now comes the next layer of software: tools designed to prove that all of that apparent completion actually means something. Leapora AI on Monday launched Taskeen, which it describes as task-accountability infrastructure. Instead of treating a task’s history as administrative exhaust, the platform is designed to preserve a verifiable record of who received work, who acknowledged it, what evidence was submitted, who approved it and when. Leapora’s pitch is not that another task manager will make the board prettier. It is that a board is no longer enough. That is a much more interesting proposition than the product category alone suggests. The workplace has spent years optimizing for visible completion. AI is exposing how little visible completion can tell us about the work underneath it. A task can be marked done while the reasoning is shallow, the supporting evidence is missing, the human owner barely reviewed the output or the person receiving it is about to spend two hours discovering that the polished thing in front of them is mostly somebody else’s unfinished thinking. There is already a name for one version of that problem: workslop. Research from BetterUp Labs and Stanford Social Media Lab found that 40 percent of U.S. desk workers surveyed had received AI-generated work that looked finished but lacked the substance to move the task forward. The recipients reported spending roughly two hours resolving each incident. The important part of that finding is not merely that AI can produce bad work. Humans have been producing bad work for an admirably long time without machine assistance. The change is that AI can make weak work look complete at extraordinary speed. That breaks one of the quiet assumptions underneath project-management software. Status used to function as a rough proxy for reality. If the task moved from in progress to complete, there was at least some expectation that a person had done whatever the task required. That was never a perfect system, but it was workable because producing the artifact itself usually required enough labor to create friction. When the artifact becomes nearly free, the status loses some of its evidentiary value. Taskeen is essentially built around that gap. Leapora says each step in a task is written as a linked entry containing a cryptographic digest of its own contents and the entry before it, making later alteration detectable. Sign-off records who approved the work, what that approval meant and when it happened. Late work can escalate to the next accountable person, with the escalation itself becoming part of the record. The company’s CEO, Siva Kowsika, summarizes the distinction neatly: conventional task tools keep status history as a byproduct; in Taskeen, the record is the product. That sentence gets close to the larger shift. Once production becomes cheap, accountability becomes expensive. Consider what an organization actually needs when something important goes wrong. It rarely needs a screenshot showing that a box was green on Tuesday afternoon. It needs to know who received the instruction, whether they understood it, what information they relied on, what they submitted, who reviewed it and who ultimately decided that the work was sufficient. Those questions existed before AI. What AI changes is the volume of material that can now reach the word finished without necessarily passing through the amount of human attention that word once implied. So we find ourselves rebuilding the paper trail almost immediately after automating the paperwork. There is something wonderfully bureaucratic about that sequence. We automate drafting so people do not have to spend as much time making documents. Then we need new systems to document what happened while the documents were being made. We automate summaries, recommendations and status updates, then construct another layer to preserve provenance, approvals and responsibility around the automation. The machine makes the artifact cheaper, and the organization begins paying more attention to the chain of custody around it. That does not make the new layer pointless. Quite the opposite. Provenance may become more valuable precisely because output is becoming abundant. When ten competent-looking analyses can be generated before lunch, the scarce thing is no longer the existence of an analysis. It is confidence that somebody understood the question, examined the evidence, made a judgment and is willing to put a name next to the result. That last part matters because responsibility cannot be automated away simply by automating production. An AI system can draft the recommendation. It cannot absorb the organizational consequences in the same way the employee, manager, executive or institution deploying it must. At some point, somebody still has to say: I saw this. I understood what it was being used for. I approved it. This is where the workplace conversation about AI starts becoming less about whether machines can do a task and more about what evidence we require when they do. We may end up with a strange division of labor in which AI produces increasing amounts of the visible work while humans become more valuable as validators, accountable decision-makers and custodians of context. The artifact becomes abundant. Judgment becomes the signature at the bottom. Leapora is, of course, making a commercial argument for its own product, and its early customer claims are company-reported. But the problem it is selling against is broader than Taskeen. Organizations are already discovering that faster creation does not eliminate the need for trust. It changes where trust has to live. A completed task used to be the record. Increasingly, the record may need to prove how that completion came into existence. The great workplace-technology project of the last several decades was to reduce friction. AI may force companies to decide which friction was waste and which friction was quietly doing useful work. Review takes time. Approval takes time. Showing your evidence takes time. Attaching a human name to a decision can slow things down. Those steps are inefficient right up until the moment somebody needs to know what actually happened. We spent years teaching software to help us say done faster. The next wave may be software asking a much less convenient question: done by whom, based on what, and who is willing to stand behind it? SOURCE NOTES • Leapora AI / PR Newswire, Sept. 14, 2026: “As AI Floods Workplaces With Unverified Work, Leapora AI Launches Taskeen to Prove Who Did What” • BetterUp Labs / Stanford Social Media Lab research on AI “workslop”: The Hidden Cost of AI-Generated Busywork --- ProbleMattic is written and maintained by Matthew Kulcsar, a software engineer, project manager, technologist, platform builder, emergency-services-trained helper, grandfather, and lifelong collector of broken systems, odd behaviors, and useful nonsense.