By Peter Vin, founder of Vindaris
We have spent seven years running strategy execution transformations for 300 organisations across 15 countries, including 10 Fortune/Global 500 and 9 organizations that IPO'd, got acquired or reached unicorn status since working with us. The strategy execution workshops themselves worked. People left those rooms with sharp goals and genuine momentum behind them - and the impact was real. Increased clarity and alignment on the executive level created an execution strength on the first few levels of management that pulled organizations forward. What it didn't do though, was successfully cascade that clarity and energy down into the organization and to the people that actually need to be aligned. The 95% that spend 50 hours a week working to fulfil the strategic promise of executive leadership.
It took me too long to understand why. The OKR process preaches OKR setting meetings to solve for the missing alignment from goal to goal, and then relies on check-in meetings to tie work to those goals. OKR tools are rolled out, which help track OKR status and Project status in the hopes to provide leading indicators for success, in the best case something other than just pure revenue increases. However, the missing pieces are much deeper in the organizations. They are the micro decisions, Slack threads, comments on a task and quick call outs in a meeting about a blocker. In short: They're the context about the actual effort that's being spend. And no one know for certain - and definitely can't see and measure - if that effort actually aligns to the strategic goals we're trying to hit. What everyone reads as a discipline problem in strategy execution and OKRs is a data problem.
Consider the asymmetry in what an organisation knows about each half of its own execution. One the one side you have the strategy, goals, and narratives. Maybe the organization even pulled in McKinsey and paid $2MM for a deck to the strategy to the board. Yet, the strategy remains a few hundred words with a handful of goals in it. Each goal a 20 word sentence with a KPI to it. On the other side, you have the execution layer. That's the effort spent pursuing the strategy and it generates millions of data points a week: tasks opened and closed, comments, blockers, reassignments, the Slack thread where someone got pulled into another project, the project whose last real activity was three weeks ago, the quick comments said just before a meeting ended. Thin data on one side, rich data on the other. The decisions that matter most, where to add people and what to stop funding, get made on the thin data side, because the thin side is the only one leadership can see.
What the effort-to-goal gap actually is
Most companies invest in one of two kinds of visibility, and each covers one half of the same relationship.
The first is goal visibility. You run the offsite, choose the KPIs, cascade objectives from company level down to each team, and publish the result where everyone can find it. A lot of effort goes into this. What it buys is a clear picture of strategic intent and direction, which is critically important - but still says nothing about whether the work in flight helps achieve that intent. You can read a KPI dashboard for an hour and still have no idea which of the forty-odd projects underway is the one actually moving the number. A goal published without a link to the effort beneath it documents an ambition and stops there. But that link to effort is difficult. Because effort doesn't cleanly sit in one project in Jira that you can just link to and query a "67% progress" update from. Measuring effort is messy. That's why organizations have come up with many ways to wrangle with managing effort.
The second is project visibility, and an entire industry exists to supply it. PMO functions track it, project tools display it, scrums and weekly syncs repeat it aloud. A serious fraction of every manager's week disappears into it. Some say up to 70% of work time is spent tracking and measuring the remaining 30%. What comes out the other end lets you see task progress but never impact nor where time actually was spent. On time and on budget reads as healthy in any report ever written, whether or not the goal that justified the project is still one the company is pursuing or rather the project itself is what the team is actually spending time on - or rather should should.
The distance between those two halves is what we call the effort-to-goal gap: the distance between where a goal says an organisation should be spending its time, and where the organisation is actually spending it. Neither kind of visibility measures it effectively. And what you can't measure, you can't manage.
Ask a leadership team where their people's hours went last quarter and the truthful answer is that nobody knows with any precision. What gets reported is whatever fit on the board slide, tidied up for a meeting that had forty-five minutes to spend on it. That version is not dishonest. It's just not where the hours went, not reflective of what the focus actually was, and not a meaningful indicator for what the organization can achieve if it closed the gap. The true allocation is spread across places no one adds up: assignments, comment threads, calendars, Slack channels. Who touched what, and when. How long something sat before anyone came back to it. How many people are still quietly parked on work that stopped being urgent two quarters ago?
The gap takes a few recognisable shapes, and only the first is obvious. A team keeps running a project that should have been cancelled the day the strategy moved. A project that genuinely deserves to continue gets worked the wrong way, in the wrong order or scoped for a segment the company no longer sells to. A project gets ranked at the wrong level, treated as background noise while it quietly carries a KPI the board is watching. Or the headcount on it is wrong in either direction: three engineers on something that needs one of them half-time, or a single overloaded owner holding up work that warrants a team. Every one of those is a mismatch between resources and the goal those resources exist to serve, and not one of them shows up in a percent-complete field.
Execution risk, in plain terms
The name for that mismatch, once you decide to manage it rather than discover it, is execution risk: the gap between a goal and the effort, resources and focus actually pointed at it.
I like the term because it puts the risk where it belongs. A normal risk register tracks delivery risk, meaning the chance a project ships late or over budget. Execution risk asks something else entirely. If the project ships exactly as planned, does it still move anything the company currently cares about?
The pattern repeats itself almost identically everywhere I have watched it happen. In Q1, leadership picks a direction: move up into the mid-market. It becomes an objective with a revenue number and a product-readiness milestone attached. Teams open projects, cut them into epics, and start shipping. Then Q2 arrives, a competitor lands a strong enterprise product, and leadership swings round to defending the enterprise base. The mid-market objective gets archived quietly, or its targets get softened.
The projects underneath it are already moving. Engineering has three sprints of mid-market features queued. Marketing has a campaign in production. But nobody issues a stop order, because at task level the thread back to the strategy was cut long before the pivot happened, so there is nothing to send the signal along. Or even worse, teams are simply asked to 'tag initiatives to strategic priorities' without questioning if the initiative is the best way to achieve it - or rather it should be worked at all. Effort carries on at full speed, aimed at an objective the company walked away from six weeks ago. That is execution risk in its purest form, and it compounds, because a team still serving the old priority is a team not yet serving the new one.
The expensive part is how little of this resembles failure while it happens. Tasks complete. Sprints close. Status stays green. And the project might even sit on the Executive Dashboard. The Project Management Institute's research puts waste from poor project performance near 12 percent of total organisational investment, and much of that is not sloppy delivery at all. It is careful delivery against a target that had already expired. Across the category, companies deliver on average only 63 percent of the performance their strategies promised.
What a decision actually needs
Picture a leader at the moment of a resource decision. In hand: a status field reading on track, 70 percent, and a confidence rating someone picked under time pressure.
The things the decision turns on are somewhere else. Whether the remaining 30 percent contains the hard part. Whether the two engineers on it were reassigned last Tuesday. Whether the blocker raised in a comment thread ever got resolved, or whether the customer signal that justified the whole initiative still holds. That context exists, in the work tools and the Slack threads, at a level of detail no status report survives, and spread across different systems throughout your teams. A comment thread becomes a status update, the status update becomes a percentage, the percentage becomes a colour on a slide, and each step strips out the specifics that made the original signal worth having. What reaches the leader is an artefact of that compression rather than the thing itself.
Which is also why more reporting never fixes it. Even when everyone reads the same dashboard, they read a 20x compressed bit of information that used to be a full window of deep context and rich insights . Instead of reading that, everyone read different meanings into the 70 percent KPI in the color green. One team sees "KPI on track" and thinks: permission to hold course. Another sees an invitation to reallocate resources, a third sees a goal close to completion that needs our focus to push it through. Ten people, one number, ten different plans.
Joining the two sides
Vindaris is intelligent strategy execution software built to automatically track and hold both halves in one graph. It maps your strategy, goals and KPIs down to every project and task, which gives the thin side a structure. Deep two-way integrations with Jira, Asana, HubSpot, MS Planner, Excel and the like, Conversation transcript ingest, Slack and Teams Threads, Emails and the rest bring in the rich side.
How deep that second part goes decides whether any of this works. Across most of the SaaS category for this, an 'integration' means one number copied on a schedule: a project's overall percent-complete, dropped into a progress field. It is an average over everything in the project, so it conceals which piece moved, which piece froze, and what caused either. And it totally disregards that any single project is never the true driver of anything. It's the intersection of work that achieves progress. Vindaris syncs each task on its own instead, with it's full comment and change history, across different systems, aligned to make sense and represent reality. Comments ride along with the task, so the reason something is stuck arrives at the same moment as the status. So does its history, which is why a KPI drifting for three weeks looks nothing like one that tipped over this morning. Linked Slack and Teams threads and Meeting transcripts come too, and that matters more than it sounds, because a decision to deprioritise something is made in a thread far more often than in a tool. Newly created work aligns itself to the right goal instead of waiting to be mapped by hand, and the sync runs in both directions, so a risk flagged in Vindaris lands back on the task in Jira where the person doing the work will see it.
With both halves in one place, the effort-to-goal gap becomes a measurement rather than a suspicion. The Work Graph reads the edges between goals and the work beneath them and flags where effort and resources are not aligned with the goals, without anyone writing a status update. Twelve deterministic and AI-driven alerts watch for slipping work, broken routines and misaligned focus, and they name the specific thing: a task connected to a slipping KPI that could save it, a goal running 12 percent behind pace, effort drifting from a goal, at-risk work sitting on the canvas, a check-in overdue. Ask why a KPI is at risk and the answer arrives with the tasks underneath it and the thread where the problem first came up.
One of our customers, rufmacher, found this in their pipeline. Their reporting said the pipeline was healthy. The alignment scoring underneath it showed that about a fifth of one region's active pipeline work was still pointed at SMB accounts, a segment leadership had deprioritised the previous quarter in favour of a DACH mid-market push. Nobody had told the reps to stop, and the board showed those deals as open and moving. Real hours, aimed at a goal the company had already stopped chasing. Their CEO put it better than I can: the reports told them the pipeline was healthy, and the Work Graph told them a fifth of it was healthy in the wrong direction.
Depth also changes which questions can be asked at all. With every goal, KPI, project and task queryable like a database, you can ask where two teams are building the same thing, which initiatives lost their strategic rationale, which goals have owners and intentions but no work behind them, which people are carrying effort for four different objectives at once. Vindaris is the intelligent strategy execution layer that connects effort to goal and provides deep insights and recommendations to achieve your strategy.
Why depth decides what AI is worth here
Most AI in goal software writes goal suggestions or polishes check-in text. The genuinely useful role is reading the thousands of task-level signals no human has time to read, slipping delivery patterns and effort flowing to goals that were deprioritised last month, then turning them into a warning early enough to act on.
The output can only be as good as the data underneath it. AI pointed at status reports is summarising summaries, and it produces confident nonsense at speed. In Vindaris, the AI Scorecard Summary is a written read on the quarter, generated from the work context itself, and its accuracy comes from the richness of the Work Graph beneath it. Point a model at the thin side and you get decoration. Point it at the Vindaris Work Graph and you get something a leader can act on.
Where the impact comes from
The payoff shows up as recovered capacity. Those one-in-five disconnected tasks, once visible, turn into decisions: adopt them into a goal or stop them. Duplicated work across teams surfaces and consolidates. Initiatives running on expired assumptions get killed in week two rather than month four. Reallocation happens while it can still change the quarter. None of that required anyone to get better at execution. The organisation simply started deciding on rich data.
For teams weighing tools against that standard, we keep a detailed comparison of the best strategy execution software in 2026, covering eleven platforms including our own. One question separates them: does status come from the actual work, or from a disconnected percentage number that somebody typed in a PowerPoint deck?
After seven years inside goal programmes, our conclusion is that most teams execute hard against a map nobody keeps current, and then get told they have a culture problem. The Vindaris strategy execution platform is built on the other explanation. Give an organisation the full depth of its own effort data, joined to its goals, and the waste resolves because it no longer has anywhere to hide.