Time to Innovation: Why Your Competitors Innovate Faster

Imagine two identical products. One hits the market in March, the other in September. According to McKinsey, the late one will generate 33% lower profit over five years. Now here is the interesting part: if the first one exceeded its budget by half, it would lose only 4% of profit. In other words, in the 2025 market, it is not expensive development that kills you — it is slow development.

This article explores the concept of Time to Innovation (TTI), the time from identifying an opportunity to delivering it to market. This is not a metric of coding speed or how quickly a company deploys new technology. TTI measures an organizational capability — how fast a company can turn a recognized opportunity or threat into a concrete product, service, or decision.

Data from BCG shows that fast innovators are 18x more likely to be disruptive in their industry and capture over 30% of revenue from new products, compared to just 11% for slow innovators. Yet in 2024, only 3% of companies were classified as fully ready to innovate, a dramatic drop from 20% just two years earlier.

For companies in regulated industries across Central and Eastern Europe (CEE) — banking, telecommunications, energy, and healthcare — this dynamic is even more urgent. The combination of strict European regulation, legacy IT systems, and decision-making structures of multinational parent companies creates an environment where innovation cycles are measured in years rather than months.

Four Speeds of Innovation

The speed of innovation is not random. It is a direct function of organizational structure: the number of decision-makers, layers of approval, and degree of team autonomy. When we look at the four typical organizational forms operating in the CEE region, the differences are dramatic.

Startup (1 to 50 people): decisions in hours, MVP in weeks

Startups operate in a mode where 3 to 5 decision-makers can make key decisions in hours. The typical time from idea to MVP is 1 to 4 months, and 75% of startups can execute a major pivot within a month. Accelerator Y Combinator routinely expects a working MVP in 2 to 3 months.

CEE examples illustrate this well. Payhawk (Bulgaria) went from founding in 2018 to a one-billion-dollar valuation in just four years. Czech Lemonero became the first startup integrated into a major Czech bank's main app. And Kardi AI, a team of fewer than ten people, obtained MDR Class IIa certification for AI-powered diagnostics, a first for a Czech startup.

This speed comes at a cost, however: a high failure rate. In regulated industries, startups hit the so-called valley of death — where compliance costs drain capital before the product generates revenue. Surveys show that 42% of Czech SMEs dedicate more than 10% of their workforce purely to regulatory compliance.

Scaleup (50 to 500 people): balancing speed and scale

The CEE region had approximately 275 active scaleups as of Q1 2025. Their product cycles range from 3 to 9 months for major features, with typical releases every 1 to 3 months.

A critical inflection point occurs around 100 to 200 employees. At that stage, informal communication networks break down and the company must introduce formal processes. What emerges is what analysts call the clay layer of middle management — people motivated by operational stability rather than risk-taking. Innovation directives from leadership get stuck in this layer, filtered through KPIs focused on efficiency. Decision cycles can stretch by 30 to 50%.

Successful scaleup examples from the region include Rohlik (valued at $1.2 billion), which built proprietary logistics software as a key differentiator. Productboard grew to more than 6,000 enterprise clients including Microsoft. And Polish ElevenLabs closed the largest CEE round in Q1 2025 at a $6.6 billion valuation. The structural weakness of the region remains brain drain: nearly 50% of CEE scaleups have relocated their headquarters abroad due to a lack of late-stage capital.

Corporate (500 to 5,000 people): decision fog and months of waiting

In the corporate environment, projects typically pass through 10 to 15 decision-makers, each with different interests and potential veto power. The average company needs 6 to 12 months from idea to production. Compliance review alone adds 3 to 6 months. And maintenance of legacy systems consumes 64% of the average bank's IT budget, leaving minimal room for anything new.

McKinsey documents a telling case: an innovation team at a global logistics company worked 12 months on a concept before reaching the CEO, who immediately rejected it because it did not align with the strategy. With clearly defined boundaries, the project could have been stopped in weeks. Each additional approval layer reduces the probability of innovation success by roughly 12%. Agile organizations average 3 to 4 approval gates; traditional corporates have 7 to 8.

A positive example in the region is Poland's mBank, named by Deloitte as a Global Digital Champion. But that is precisely because it was born as a digitally native bank, not as a transformed legacy player.

Multinational (5,000+ people): when Paris, Vienna, or Bonn decides for Prague

Multinational structures add a headquarters decision-making layer on top of corporate slowdown. Local market insight must travel up the organizational hierarchy, across cultural and language barriers, competing with other subsidiaries for attention and budget. The result is 6 to 12 months of dead time that purely local competitors simply do not have.

Three examples from the Czech market illustrate this dynamic. Komerční banka / Société Générale: the core banking system replacement, the longest transformation initiative in KB's history, is a three-year project where the technology choice (Temenos) reflected the group's strategic relationship. Erste Group / Česká spořitelna: the George platform, developed centrally in Vienna, was rolled out sequentially. Austria in 2015, Czech Republic not until 2017, a two-year lag. Today George has approximately 10 million users, but the model sacrificed first-deployment speed for standardization. Deutsche Telekom / T-Mobile CZ: innovation is managed from Bonn with standardized approaches across CEE, while US-based T-Mobile visibly moves faster than European subsidiaries.

There is, however, an interesting counter-trend. Some progressive groups like UniCredit or Erste are starting to use their CEE subsidiaries as innovation sandboxes, piloting new digital products in the Czech Republic or Hungary where digital adoption is faster, then exporting them to more conservative markets. In these cases, TTI in the CEE subsidiary can paradoxically be shorter than in the parent country.

Three Time Eaters

Why are innovation cycles so long? It is not just about company size. Three systemic patterns reliably consume months — sometimes years — of innovation effort.

Regulatory overhead: when compliance costs more than development

European regulation, including AI Act, GDPR, NIS2, and DORA, protects the market but systematically favors large players who can absorb compliance costs through scale. The AI Act adds approximately 17% overhead to all AI spending for high-risk systems. GDPR effectively functions as a 25% tax on the smallest companies. For SMEs, compliance costs can consume up to 40% of profit margins.

A survey of over a thousand European tech firms shows that nearly 60% of developers report regulation-caused delays in bringing products to market. For comparison: 54% of American developers report no delays at all. Hope comes in the form of the Digital Omnibus initiative, which aims to consolidate overlapping rules, but its approval and implementation will take years.

PoC Purgatory: the prototype graveyard

Data from multiple sources converge on an alarming conclusion: 78% of proof-of-concepts never reach production. For AI projects, the situation is even worse. Out of every 33 AI prototypes, only 4 made it to production. In 2025, the average enterprise discarded 46% of AI pilots before production, up from just 17% the year before.

The main reasons? Isolation from legacy systems, poor data quality (80% of ML work is data preparation), and lack of executive sponsorship. The key finding: PoCs shorter than 3 months have a 3x higher chance of successful commercial implementation. A slow PoC is not thorough — it is dead.

Innovation Theatre: millions in PR with zero market impact

80 to 90% of corporate innovation labs fail. Most focus on hackathons and idea generation instead of measurable outcomes. Of new businesses brought to market, only 19% generate more than $50 million in annual revenue after four years. And yet only 6% of executives are satisfied with their company's innovation performance, even though 84% consider innovation critical to future success. The problem is not a lack of innovation efforts — it is an inability to carry them through to production.

AI as Accelerator, Not Equalizer

Generative AI in 2025 is genuinely changing the equation of development delay. Controlled experiments show that AI assistants (such as GitHub Copilot) reduce task completion time by more than 55%. McKinsey documents 40 to 50% productivity improvements for specific tasks. And most organizations need just 1 to 4 months from project start to deploying GenAI into production.

For the CEE region, GenAI represents an opportunity worth 90 to 100 billion euros in annual GDP. But timing matters: a five-year delay in adoption would reduce this potential from 5% of GDP to just 1%, from roughly 100 billion to just 15 billion euros.

The CEE paradox: more than 75% of companies claim to use AI, but only 25% at scale. At the company-wide level, only 4% of firms deployed AI solutions in 2023. This is a problem because AI is not a universal equalizer — it is an accelerator for agile organizations. BCG data shows that so-called future-built companies (just 5% of the market) achieve 1.7x higher revenue growth and 3.6x higher total shareholder return. Not because they spend more, but because they spend smarter and faster.

The next wave, agentic AI, promises further cycle compression. Already today, 62% of organizations are experimenting with AI agents. By 2028, 15% of daily work decisions are expected to be made autonomously by AI, and one-third of enterprise software is projected to contain agentic components. In regulated industries, compliance automation is especially promising: AI tools already achieve 98% accuracy in regulatory reporting and have cut audit preparation time by 35%.

What Now? Three Takeaways for Leaders

First: Time to Innovation is an organizational problem, not a technological one. The difference between a 30-day and an 18-month cycle in banking is not determined by engineer quality but by the number of decision-making layers, legacy architecture, and regulatory overhead. Organizations that want to innovate faster must start with their decision-making structure, and at the same time build the capability to continuously track signals that determine when and where to innovate.

Second: innovation speed is not just about development. It is about readiness. Most delays do not originate in the coding phase but in the decision phase. The company does not know what is changing in its industry and therefore cannot respond in time. Systematic foresight — the tracking of signals, risks, and future scenarios — shortens precisely this decision latency. When an R&D team knows which regulations are coming and which technologies are gaining traction, it can start working months before competitors even register the change.

Third: what matters is not perfection but the distance between identifying an opportunity and deploying it. PwC summed it up well in 2025: Speed matters more, scale matters less. A company that can spot a weak signal in the market and respond within months with a product or a strategic decision will outperform one that spends a year perfecting its plan.

This is exactly why the need is growing for living systems that continuously monitor signals, update scenarios, and deliver concrete recommendations — not once a year in the form of a static report, but in real time, as the world around us begins to shift. Companies that build this capability will gain something that cannot be copied — a time advantage over their competition in every decision they make.