Unless you really don’t want it to get traction, then every business case looks immaculate at the start; the numbers line up neatly, the benefits appear achievable, and the ROI curve takes off the moment the project launches. It’s comforting, tidy, and persuasive, especially when budgets are tight, headcount is being cut all over the place, and leadership wants quick results.
But it’s also a lie. The idea of instant ROI is at best naive, at worst deliberately, desperately fanciful.
The moment a project goes live is not the moment value begins to flow, as people don’t instantly adopt new systems, processes, or behaviors, teams don’t abandon familiar habits overnight, and benefits don’t materialize just because the implementation checklist says “complete.”
The fallacy of instant ROI is one of the most pervasive (and I’d argue, needlessly damaging) assumptions in business case development. It quietly (over)inflates forecasts, hides real risks, and erodes confidence in the process when “expected returns” inevitably fail to appear.
The seduction of instant ROI
Don’t get me wrong, 20 years of working across a range of companies and industries mean it’s not hard for me to see why this myth persists; any Executive worth their salt wants to demonstrate quick wins, boards expect returns within the fiscal year, and project sponsors know that “fast ROI” makes approval easier. The result is that your humble business case author is often caught in the middle; under pressure to make the case compelling enough to get that lovely green light for investment.
So, without necessarily consciously intending to deceive, teams often build models where benefits start the moment the system goes live. It feels rational, as to the untrained eye, the project delivers a new capability, therefore benefits should begin immediately. But sadly, that assumption rarely survives first contact with reality.
The gap between go-live and value realized can stretch to months (sometimes years) depending on how complex the change is, and during that time productivity may actually even dip as teams adjust, as new processes need debugging, new tools need refining, and old habits fight hard to return.
The world of instant ROI assumes that people are machines, but (for now at least…) organizations are made of people, not processors.
Why we keep falling for it
Part of the problem lies in the way ROI tends to be communicated. Graphs that show a clear upward trajectory are more persuasive than gradual, uncertain ones, and due to the overly optimistic corporate personality of many companies, presenting a (realistic) slow ramp to benefits can be seen as defensive, almost like admitting weakness.
Yet, the more sensible organizations recognize that realism is a strength, not a weakness, as it signals that the team understands not just the mechanics of the investment but also the psychology of change.
The persistence of the “instant ROI” mindset is also a cultural phenomenon, as in many organizations, success is measured quarterly, not over the lifespan of adoption. Budgets are reviewed annually and sponsors move on to new roles before full realization occurs, so with that short-term focus, it’s easy to overpromise in the near term and quietly hope adoption catches up later. And when it inevitably doesn’t, the post-project review is usually too late (or is just a superficial, box-ticking exercise) to drive lasting improvement.
The hidden cost of ignoring adoption
Ignoring the realities of adoption doesn’t just shift the timing of benefits though; it can change the outcome entirely. Projects that underestimate the adoption curve often experience:
🐢 Delayed realization as benefits take months or years longer to appear than forecasted
🤏 Reduced impact as only partial adoption occurs, so value is permanently lower than expected
🤔 Lost credibility when stakeholders begin to doubt future business cases, eroding confidence in the process
And as we’re currently still in that blissfully ignorant period before the machines fully take over, there’s also an emotional cost. Teams that have worked hard on delivery feel deflated when their success isn’t recognized because results haven’t yet shown up in the numbers. Meanwhile, leadership grows impatient, assuming the problem lies in execution rather than adoption (or actually, in bad forecasting of the adoption).
I won’t win any popularity contests in the sales world, but lets use a CRM deployment as an example. The technology may be world-class, but if sales teams don’t log their activities, then forecast accuracy won’t improve. The tool works, but the benefit doesn’t materialize.
In the worst cases, organizations then double down by investing in even more technology to “fix” the issue, when the real problem is human adoption. It’s your classic vicious cycle of spending, disillusionment, and poor accountability.
Why traditional business cases miss it
The typical business case tends to treat adoption as a background assumption rather than a modeled variable, which tends to happen for three main reasons:
𝄜 Simplicity
Traditional spreadsheet models are built for static inputs, not behavioral dynamics. Modeling a ramp-up curve adds complexity most teams don’t have time for.
🙍🏻♂️↔🙎🏻♀️ Separation of roles
The finance team builds the case, the change team manages adoption, yet bizarrely, it’s rarely questioned if the two barely connect their data or assumptions.
🤞 Optimism bias
Teams naturally overestimate their ability to change quickly; it’s not really deception, more just human nature.
Most business cases forecast benefits in neat, linear increments (a full year of savings here, a complete efficiency gain delivered in a single day there), with no allowance for ramp-up or learning curves. Adoption, if mentioned at all, merely appears as a risk line:
“If adoption is slow, benefits may be delayed.”
The things is, that’s not risk management, that’s wishful thinking. The reality is that adoption isn’t a binary event, it’s a process, and one that both, can, and should, be modeled.
The economics of adoption
If you map adoption on a curve, you’ll see it generally resembles the classic S-shape of change diffusion: early adopters experiment first, followed by a growing majority, and eventually the laggards catch up. The curve isn’t just theoretical though, as thanks to either the wonders of Economics or just a bit of real world common sense, it can be quantified.
👉 e.g. if an organization introduces a new automation tool that’s projected to save 10,000 hours per year, it’s unrealistic to assume that full savings will be achieved in Year 1. A more credible forecast might allocate
1️⃣ 20% realization in Year 1, for pilot teams and early adopters
2️⃣60% in Year 2, as they achieve a wider rollout and stabilization
3️⃣100% in Year 3, as they reach full adoption and maturity
This phased approach changes everything, as the ROI still exists, but it’s distributed differently (and more truthfully). It also has the added bonus of giving leadership a clearer view of where to intervene if adoption stalls, as instead of waiting for a post-mortem when it’s too late to do anything, they can adjust training, communication, or incentives midstream to accelerate uptake.
Building adoption into the business case
By getting this far, you’ve obviously not given up all hope of a solution, and I’m here to reward you, as achieving promised ROI is doable. The key thing to realise is that organizations that consistently achieve their ROI don’t treat adoption as an afterthought, instead they embed it in the business case from the start, by doing three main things differently:
📶 Model benefit ramping
Instead of flat benefit profiles, they model adoption over time, showing when and how benefits will ramp. This not only improves accuracy but also creates more realistic expectations with leadership.
ℹ️ Read more about how to apply Benefit Ramping
🔢 Quantify enablers
Change management, communication, and user training are often seen as “soft” costs, but they’re not, instead successful orgs tend to think of them as “enablers of ROI”. Cutting these budgets is easy, but is actually often one of the most expensive mistakes an organization can make, because it directly undermines benefit realization.
🔎 Track realized ROI.
The business case doesn’t end at approval, as we’ve seen before, the best thing to do is to treat it as a living document. What that means is revisiting it periodically and comparing forecasted with actuals across all areas, not just costs but also benefits and adoption rates. When adoption lags, diagnose the cause; is it process design, system usability, leadership engagement, or capability gaps?
This continuous feedback loop helps turn the business case from a prediction into a learning system, helping to fight the curse of Corporate Amnesia.
The mindset shift
However, despite me having already said a lot, arguably the biggest change isn’t actually technical, it’s cultural.
Effectively, what it comes down to is the fact that leaders need to stop equating “delivery” with “success.” Implementation is only the beginning, as real success is when the new way of working becomes the normal way of working, and that takes time, reinforcement, and iteration.
Accepting that ROI is delayed doesn’t weaken the case; it actually strengthens it as it builds trust, because the organization knows the forecast is grounded in reality, not just hope.
It also changes how project teams are incentivized, as instead of rewarding early delivery at all costs, mature organizations reward sustained adoption and realized outcomes. You could say they focus less on “go-live” and more on “go-thrive.”
When this mindset shift happens, ROI forecasting becomes more credible, governance becomes more evidence-based, and decision-making improves across the portfolio.
From instant ROI to Real ROI
The good news is that this shift doesn’t require abandoning business cases; it just requires smarter ones.
Modern tools and methodologies make it easier to model adoption curves, test scenarios, and link real performance data back to original forecasts. When you can visualize how adoption affects ROI timing, you can make more informed decisions about sequencing, resourcing, and risk.
That’s why platforms like KangaROI are designed to make this process transparent. By allowing teams to model benefit ramping, capture post-approval check-ins, and track Real ROI through the full lifecycle, organizations can see (and not just assume) how adoption is influencing value.
It’s not about software immediately replacing judgment; it’s about giving decision-makers the visibility they need to separate optimism from evidence.
The takeaway
The fallacy of instant ROI is comfortable but costly as it simplifies business cases at the expense of accuracy and sets expectations that real-world adoption will inevitably fail to meet.
ROI is not realized at go-live; it’s realized when teams change behavior, when processes stabilize, and when new capabilities become part of daily work.
Organizations that understand this don’t just build better business cases; they build better outcomes as they plan for adoption, measure it, and learn from it.
Because in the end, the only ROI that matters is the one that’s actually realized.
