Infinite opportunity cost: Why AI acceleration means your next RFP could be your last

“We are climbing the long arc of exponential technological progress; it always looks vertical looking forward and flat going backwards, but it’s one smooth curve. (Think back to 2020, and what it would have sounded like to have something close to AGI by 2025…)” - Sam Altman, The Gentle Singularity, June 2025

Money isn’t your bottleneck; hesitation is. Lost weeks now mean lost markets later.

The pace of artificial intelligence is shattering the traditional cadence of enterprise tech. AI capabilities are advancing so quickly that opportunity cost — the value lost by waiting — is ballooning into a potentially existential cost. In a world where AI models, agents, and systems are surpassing human capabilities in benchmarks every week, a months-long procurement or implementation cycle can leave your company hopelessly behind. 

AI is advancing faster than business cycles

AI progress is no longer linear or predictable — it is exponential and accelerating. The latest Stanford AI Index found that performance on cutting-edge AI benchmarks jumped by as much as 67 percentage points in just one year. In one striking example, an AI’s ability to solve coding problems leapt from 4.4% to nearly 72% between the end of 2023 and early 2025. These aren’t gradual improvements — they are 10x leaps happening in the span of quarters.

This breakneck progress isn’t confined to research labs; it’s hitting the enterprise now. In 2024, 78% of organizations reported using AI, up from just 55% in 2023. Your competitors are likely among them, rapidly deploying AI to automate, optimize, and outpace slower movers. Weekly model upgrades and monthly product releases are the new normal. The half-life of technology advantage has never been shorter. If you are stuck in last year’s deployment schedule, you’re missing today’s capabilities and your competitors will gladly seize that opening.

Leaders need to internalize this: AI is moving faster than traditional business cycles. As Microsoft CEO Satya Nadella observed during the pandemic, “we’ve seen two years’ worth of digital transformation in two months”. Now imagine that kind of acceleration in AI today — it’s happening. Multi-year roadmaps are luxury items of a bygone era. The window for action has collapsed. The old pace of change — quarterly planning, annual upgrades — now guarantees falling behind.

The high cost of slow (and the myth of safety in delay)

Long procurement and rollout cycles once felt like due diligence; today they look like dangerous indecision.

In an AI-driven market, slow is the new broken. Every extra week spent on protracted evaluations or long implementations isn’t just time lost, it’s opportunity lost, compounded continuously. Traditional enterprise roll-outs often take multiple quarters or more, especially for heavy platforms like CRM or marketing clouds.

Consider CRM implementations: a basic Salesforce deployment might take 4–8 weeks, but enterprise-wide rollouts with many integrations often drag on 6–12+ months.5 And that’s after the buying process. The procurement cycle itself can be a quagmire; the typical enterprise software RFP and vendor selection can range from 6 to 18 months. By the time you’ve signed a contract and begun onboarding, a year (or more) has passed — an eternity in the AI era.

The opportunity cost of these delays is staggering. If you wait 12 months to implement an AI-powered solution, that’s 12 months your competitors could be leveraging AI to win customers, optimize operations, and learn from real data. Delayed implementation can mean you are obsolete before you go live. A platform that was state-of-the-art at project kickoff might feel antiquated upon deployment because AI capabilities advanced in the interim. The risk isn’t just a few missed features — it’s the risk of irrelevance.

Clinging to protracted timelines gives a false sense of safety. In reality, it’s exposing the business to continual disruption. The cost of waiting is no longer just lost efficiency or delayed ROI; it’s the potential permanent loss of market position. Every month of delay is a month of competitors courting your customers with smarter, AI-driven offerings. The opportunity cost isn’t just financial — it is strategic and potentially infinite if you miss the window to adapt. In short, the slow will be left behind.

To visualize this, below find a typical Enterprise six-month RFP cycle along with a 12-month Salesforce or Adobe implementation cycle against AI progress over the 18 months prior to June 2025. Over the 18 month period before June 2025, OpenAI has gone from gpt-4o being their state of the art model to o3. To represent what that progress means in the domain of code generation, it’s the difference between gpt-4o telling you that it cannot create a website to o3 actually generating it for you immediately, with an interactive preview. 

Note how one 18-month RFP and implementation cycle spans two model generations: 

Remember, the pace of AI progress is accelerating, and the jump over the 18 months analyzed here will be the absolute slowest 18 months of progress compared to 18 months of progress looking forward. The opportunity cost of waiting on vendors continues to accelerate as well.

From multi-quarter to multi-week: a new mandate for leaders

The era of multi-quarter roll-outs is dead; leaders must embrace a multi-month (or even multi-week) mentality. To thrive under these conditions, executives should fundamentally rethink how they select and implement technology:

• Demand instant impact, not eventually impact -Set the expectation that new tech will deliver value in weeks, not quarters. If a vendor proposes a nine-month implementation, ask how they can prove value in one month instead. Insist on tangible results fast — whether it’s a working prototype showing operational efficiencies, a pilot integration, or a quick win on a key metric. The truth is that modern cloud software can often be stood up swiftly. Businesses that once mapped strategy in 1–3 year phases must now scale initiatives in weeks — your tech partners need to operate on that same compressed timeline. If they can’t, that’s a red flag. Speed to value is your competitive edge.

• Insist on rapid proof-of-concepts - The easiest way to separate hype from reality is to run a lightning-fast proof-of-concept (POC). Require vendors to let you use their software in less than four weeks or move on. This isn’t an unfair ask. Rather, it’s increasingly standard. In practice, an AI proof-of-concept can often be executed in just 3–4 weeks. By setting a four-week POC deadline, you force focus on what truly matters and avoid wasting months. The idea is simple: if it takes longer than a month to show any value, how will it keep up with an AI landscape that’s evolving monthly? Agile, test-and-learn engagements are the new due diligence. You’re not looking for perfection in four weeks, just proof that a solution can hit the ground running and keep pace.

• Adopt a “launch fast, iterate faster” mindset -Treat every tech deployment like a living project, not a one-off implementation. Instead of waiting for a perfect, enterprise-wide rollout, launch a minimal viable solution in one department or for one use case immediately. Then iterate. Weekly or bi-weekly improvements should be the norm. This agile approach not only delivers value sooner; it also creates organizational muscle for rapid adaptation. No more big-bang launches that arrive late and over budget. Aim for continuous delivery of new AI-driven capabilities. In the AI era, iterating beats elaborate planning every time.

Executives should set the tone that velocity is a feature, not an afterthought. This may require cultural change like rewarding teams who deliver quick wins and learn fast, rather than those who plan forever and launch too late. Remember, indecision and inertia are now the most dangerous forms of decision-making. A bold imperfect step today beats a perfect plan next year.

The new playbook: platforms built for AI-speed

Adapting to this breakneck environment isn’t just about mindset — you also need the right technology stack. Not all platforms are created equal in an AI-driven world. Many legacy enterprise systems, even from big-name vendors, simply aren’t built for agility. To avoid getting stuck in another multi-quarter morass, leaders should prioritize platforms with the following attributes:

• Delivers value in weeks, not quarters - Choose solutions that can be deployed quickly and show value within a single quarter or less. Modern cloud-native platforms often allow initial deployments in days or weeks. Look for technologies that come with out-of-the-box integrations, pre-built AI models or automations, and intuitive interfaces that minimize training time. If a platform requires six months just to implement basic features, it’s a non-starter. Your benchmark should be solutions that can meaningfully improve a KPI or process in under eight weeks, not in Q4 of next year. Speed isn’t just about moving fast for its own sake; it directly reduces opportunity cost by capturing value early.

• Sits on your cloud data (no heavy data mapping) - Favor platforms that work directly on top of your existing cloud data instead of forcing yet another data copy or elaborate “semantic layer” mapping exercise. The last generation of enterprise tools often made you spend months mapping and moving data into their proprietary format. That’s wasted time. Today’s composable architectures can sit directly on a data warehouse or data lake (like BigQuery, Snowflake, Databricks, or Redshift) and start analyzing/activating data immediately. By skipping a massive data migration or modeling project, you save quarters. In short, bring the computation to the data, not the data to the application. This not only accelerates time-to-value, it also means less risk and fewer moving parts. Working off data in your own warehouse lets organizations get up and running much faster than if they have to create an entirely new data pipeline or model. The result: you plug in and go, instead of spending a quarter mapping fields and reconciling schemas.

• Writes back to your data cloud) - Prioritize open platforms that play nicely in your ecosystem. In an AI-driven tech stack, no single vendor will do everything best — nor should they. You want tools that write their results back into your data cloud, so your data and insights remain yours. For example, if an AI platform generates an audience segment or a predictive score, it should save that data in your cloud warehouse under your control. That way, your other tools (BI dashboards, marketing activation systems, etc.) and teams can immediately leverage it, and you’re not locked into that one vendor for downstream action. Open architectures also tend to be more future-proof: they can incorporate new models or data sources as they emerge. The key is avoiding black boxes. If a vendor insists on being the sole keeper of your intelligence or stores data in a closed system, think twice. Your tech stack should be a set of composable parts connected by your own data layer, not a monolithic suite where all pieces are trapped under one brand’s control. The bottom line: don’t let anyone else own your relationships with your customers or the insights about them. Keep that data in-house.

• Avoids vendor lock-in across the stack - Finally, avoid vendor lock-in like your company’s life depends on it — because it might. Lock-in leads to complacency and stagnation, which is lethal when technology is evolving so fast. When evaluating any platform in your stack (CDP, CRM, ESP, advertising, analytics, AI, etc.), ask: “If a better option emerges next year, how hard would it be to switch?” If the honest answer is “extremely hard,” reconsider that choice. The goal is a composable stack where components can be replaced with minimal disruption. In practice, that could mean favoring vendors who embrace standards, provide query-in-place data access, and don’t penalize you for using other tools in conjunction. Remember, vendors should compete for your business continuously through performance, not through proprietary data locks. Keep your options open, and you’ll keep your agility.

By enforcing these principles, you build a technology environment that can keep up with AI’s blistering pace. You position your organization to seize new opportunities as they arise, rather than being stuck on a sunk-cost outdated platform because “we’ve already invested so much in it.” The old saying in startups was “move fast and break things.” For enterprises in 2025, it is “move fast or you’ll be broken.” And the right platforms — cloud-native, open, composable, rapidly deployable — are essential for moving fast with control.

Thriving at AI velocity

The future belongs to companies that can operate at AI velocity. This means adopting a mindset and a stack that optimize for speed, learning, and adaptability above all else. Executives need to lead the charge by instilling urgency, rewarding rapid execution, and shedding the comfortable old rhythms that no longer serve us. The cost of inaction is no longer just lost opportunity — it’s the potential demise of once-dominant businesses. In contrast, those who embrace agile implementation and continuous innovation will turn AI’s speed into their strategic advantage.

The imperative is clear: ditch the multi-quarter roll-out mentality. In a time of infinite opportunity cost, speed of execution is the ultimate arbitrage. The companies that thrive will be those that act boldly and swiftly — evaluating tech quickly, implementing in weeks, and iterating relentlessly. AI isn’t waiting around, and neither should you. The death of the slow roll-out is not a tragedy; it’s a chance for rebirth. Those who seize the moment will redefine their markets, while those who don’t will watch from the sidelines as the world races ahead. The choice, and the urgency, is yours.

At GrowthLoop we’ve built the Compound Marketing Engine because our customers encountered these problems firsthand. It’s built natively on the modern data cloud, leverages agentic AI, and isdesigned explicitly for this AI-speed world. It enables rapid iterations to drive compounding growth and writes back to your own data cloud, aligning with all the principles discussed above. By choosing partners and platforms that are architected for agility, leaders can compound growth instead of fighting fires.