The perfect solution myth: Why ‘good enough’ can drive digital transformation

The opportunities to acquire one innovative tool after another have led to messy expectations and untimely goals. While some companies have already introduced driverless cars and agentic artificial intelligence (AI), others may just be starting the journey.

New technology will always be on the horizon. A strategy that’s considered ‘good enough’ may be more viable than chasing perfection.

Chasing perfection often derails digital transformation

A study by McKinsey reported that only 16 percent of large digital transformations managed to meet or exceed expectations. A lack of clear goals was one of the troubling reasons behind this low success rate. However, pushing for a clear goal may not be sufficient – especially if ‘clear’ implies chasing an optimal goal.

One misconception in goal setting is the belief that organizations can always find the optimal solution. In reality, decisions are made with limited information and under uncertainty. Rather than evaluating every alternative and choosing the one with the best possible outcome, leaders often settle for a satisfactory option. In the context of organizational decisions, Herbert Simon called this process “satisficing.” Finding a satisfactory solution doesn’t sound all that inspiring when compared to the optimizers, but knowing when something is ‘good enough’ can also contribute to progress.

The ‘good enough’ message is yet to resonate

Despite the many well-being workshops that discourage us from being perfectionists, the ‘good enough’ message hasn’t resonated well with ambitious professionals.

The ambitious types will often take these goal-setting activities as a chance to create a laundry list of their ideals. The goals seem sound on paper: write down your vision, establish a hierarchy of objectives and distinguish between short-, mid- and long-term priorities. A five- or 10-year plan then emerges, not from a position of self-acceptance as we would expect of human beings, but from a desire to maximize results: “They’ve done it before, we’ve done it before, it must be true,” or “If you adopt X, then performance will increase by XX percent.” 

These statements send a strong message telling us that performance can and should be optimized. While these sets of ideals are ambitious and inspiring, others within the company may value them differently. Stakeholders will hold conflicting criteria for what counts as success – not to mention there are stark differences between industries. A startup might define success through triple-digit customer growth, while a larger firm might focus on year-on-year profitability.

Even within a single company, the criteria for success can vary significantly. In a bank, for instance, the front, middle and back offices experience technology differently. Front-office workers interact directly with customer-facing apps and are more sensitive to usability. Meanwhile, mid- and back-office teams focus on infrastructure and stability. Their expectations are less visible but critical to operations. 

No single strategy can align perfectly across a company. What benefits one group may create challenges for another. For example, the IT department may prioritize data governance and maturity, while the marketing team pushes for increased data collection and personalization. The focus of each department is shaped by its unique history and culture. In practice, it’s unlikely that any group will share the same criteria.

A recent study analyzing over 40,000 mergers and acquisitions found that 70-75 percent failed. Other studies, and anecdotal evidence, suggest even higher failure rates. When multiple and conflicting criteria exist – as is the case in nearly all companies – it becomes nearly impossible to set one goal that captures everyone’s ideal outcome or whatever they envisioned.

When a ‘good enough’ solution delivers more

Transformation leaders must continuously define what counts as progress. As a strategy, choosing what’s ‘good enough’ may be more effective than the ‘never-ending chase’ of optimality. Implementing AI may appear to be a desirable objective, but it’s acceptable (and even preferable) if the transformation doesn’t meet the ideal standard that’s been envisioned. Cutting-edge innovations typically come with high infrastructure costs, regulatory challenges and workforce readiness concerns – all of which can create more bottlenecks than adoption success.

Consider Microsoft 365’s Copilot, which might be touted as a faster, cheaper way to draft a compliance checklist. Does the speed of Copilot necessarily align with the users’ pain points? It could be that, from an employee’s perspective, the inefficiencies that Copilot aimed to solve should not have existed in the first place. The excessive forms and approvals were initially place-holder solutions that stuck around without a purpose. This automation project would be addressing an inefficiency that was not meant to exist. 

If the sole purpose of ChatGPT is efficiency, then the solution might soon become a distraction. Being solution-first ignores the business problem and risks overlooking the many ‘good enough’ solutions that can also do the job.

By now, transformation professionals will have noticed the unique practices and cultures that shape a company’s inefficiencies. Traditional companies that still rely on paper forms often do so for a reason – be it legacy decisions, resource allocations, behavioral resistance or a lack of priority for digitization. If it turns out that users prefer paper forms for their assurance, then blasting the inefficiencies away with AI might not do anyone justice. What counts as good progress might involve a policy change rather than a radical strategy. Unless we begin with the problem, we will never know for sure if a solution is good enough. 

Aligning our expectations is inherently challenging, especially when companies are set on optimal outcomes rather than acceptable ones. With the surge of AI-related products on the way, it may be time to reconsider what counts as progress. By the time leaders wait for the perfect moment to execute their next move, new technology and expectations will have emerged.

Transformation leaders should be constantly defining their criteria for ‘good enough.’ A strategy that strives towards gradual progress may be more viable than waiting for perfection.

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