Artificial intelligence promises innovation and faster growth, but only a few organizations see good return on their investment. Studies, including RAND’s review of failed AI projects, reveal a common thread that most teams stumble on in the first 90 days.
This early stage is where excitement meets reality. Your choices in those first weeks sets the tone for whether AI becomes a driver of measurable value or another stalled experiment.
If your project feels stuck after the pilot phase, or if you are preparing to bring AI into your organization but feel uncertain about how to start, this guide is for you. It will walk you through the first 90 days and show you how to build a lasting foundation.
What Makes the First 90 Days Critical in Your Overall AI Adoption Success
The first 90 days set the tone for how AI and other initiatives will be received across your organization. Many leaders step into this phase with high expectations, encouraged by stories of AI transforming industries overnight.
As a result, you’ve invested time, budget, and hope, and you want results. But that pressure often leads to common missteps, such as :
- Pressure to move fast: Teams scramble for early wins without laying the right groundwork.
- Overconfidence in technology: Rand noted this as a case of misunderstanding what problems AI can solve, which leads to using AI to solve problems they aren’t meant to solve. Technology is only a tool whose successful implementation is rooted in preparedness around data, processes, and people.
- Lack of structured guidance: Pilot projects feel like an isolated experiment from long‑term business goals without a clear roadmap.
When these issues surface, the consequences are frustrating, leading to projects stalling, stakeholders losing trust, and investments not delivering. In fact, studies show that up to 80% of AI projects never scale beyond the pilot stage.
However, the good news is that with the right structure in the first 90 days, your team can avoid these pitfalls and build lasting momentum.
Key Areas Teams Overlook in the First 90 Days
It’s easier to treat the first few days of adopting AI as a playground for experimentation, yet this period is far more consequential than most teams realize.
When managed with all attention and process tracking, it sets the stage for confidence, trust, and measurable progress. But when handled poorly, risks ripple across budgets, teams, and leadership support.
What makes this phase tricky is that the issues appear technical at first. However, when critically analyzed, they stem from the process, data preparation, and people management.
Below are the critical areas to focus on to make a difference between stalled efforts and sustainable progress:
Clear Business Case Alignment
RAND’s research highlights a consistent challenge: many industry stakeholders either misunderstand or miscommunicate the real problem they want AI to solve. This disconnect usually appears when AI adoption is framed as a technology project rather than a business initiative.
AI adoption shouldn’t be about stacking new technologies but strengthening competitiveness by solving meaningful business problems more innovatively. When that distinction is blurred, organizations often get into experiments with models or platforms without asking the bigger question: What problem are we solving, and why does it matter to the business?
Without that clarity, AI efforts risk turning into “cool pilots” that showcase activity but deliver little impact. You might see automation applied to processes that don’t move the needle on revenue, efficiency, or customer experience, leaving executives skeptical about further investment.
The way forward is simple but powerful: define outcomes first.
Whether reducing feedback cycles in decision-making, cutting manual effort in operations, or creating more seamless customer interactions, every AI initiative should tie back to a core business goal. With a clear business case in place, gaining executive buy-in and demonstrating early wins that build lasting trust becomes far easier.
Data Readiness and Governance
AI is only as strong as the data behind it, yet many teams assume their existing datasets are “AI-ready.” In reality, most organizations struggle with fragmented, inconsistent, or incomplete data that slows adoption and undermines results.
One of the most common mistakes is believing that simply having data is enough. As KPMG’s AI leader, David Rowlands, notes, the quality of AI outcomes depends on the quality of the data feeding them. To stand out, organizations must become intentional about their data: where it resides, who owns it, how it is generated, and how consistently it is maintained.
At the beginning of the first 90 days, assess data accuracy, structure, and compliance with security and privacy requirements. Without this groundwork, AI models can easily produce biased or unreliable outputs, eroding trust among teams and executives. And if this kind of data fuels a client-facing product, it leads to more harm that can quickly get out of hand.
Governance is just as crucial as data readiness. Clear policies for collecting, sharing, and maintaining data build a foundation for long-term scalability. For many organizations, this means leaning on experienced data engineers who can bring structure and discipline to what often feels like a complex, overwhelming task.
Executive and Team Buy-In
Research from BCG shows that only one in four organizations succeed with AI, and those that do follow the 10-20-70 framework: 10% algorithms, 20% technology and data, and 70% people and processes.
Source: BCG
This split highlights a mindset shift that many miss. AI success is less about the tools and far more about the people driving adoption.
As Alexander Sukharevsky of QuantumBlack, AI by McKinsey, explains, true progress requires top-down commitment. AI thrives when the C-suite and, ideally, the board treat it as a strategic priority. Delegating it solely to IT or digital teams almost always leads to failure.
Without visible sponsorship, teams often dismiss AI as “just another experiment.” Meanwhile, frontline employees may fear job loss or added complexity. If these concerns aren’t addressed, resistance builds and success slows.
Genuine buy-in means alignment at both ends: leaders champion AI as part of the company’s future, while employees feel informed, supported, and involved in shaping how it integrates into their work. When AI is positioned as a tool that empowers rather than threatens, adoption becomes smoother and more sustainable.
Skills and Change Management
For many employees, AI feels uncertain. Some are unsure how it connects to their roles, while others fear being replaced by it. Even those open to change may lack the technical knowledge to interpret outputs or integrate AI into daily workflows. Without clear guidance, these gaps create hesitation that slows adoption and weakens results.
This is where change management becomes critical. Training programs, transparent communication, and hands-on learning opportunities help teams see AI as an enabler rather than a threat. By investing in people as much as technology, organizations build the confidence and capability to sustain AI initiatives beyond the pilot phase.
At Growth Acceleration Partners (GAP), we bridge this gap through our AI Acceleration Workshops. These workshops bring every department into alignment on AI initiatives while providing expert guidance to:
- Identify high-impact use cases
- Assess tools and requirements
- Build an implementation roadmap
- Design accountability structures that involve key stakeholders
- Document risks and governance measures
With this foundation, teams gain the skills and confidence to make AI a lasting part of their work process.
Measuring Early Wins
One of the biggest blind spots in the first 90 days is failing to define and measure success early. Too often, teams wait until projects are mature before showing results. But by then, leadership may already be questioning the value of the investment.
Early wins don’t need to be dramatic. They can be simple, measurable improvements, like reducing manual work in a single process, improving report accuracy, or shortening customer response times. These smaller milestones matter because they prove that AI can create value, build confidence with stakeholders, and keep support strong for larger initiatives.
The key is to set clear KPIs from day one and communicate progress openly at every stage of AI adoption. Transparency reinforces trust and helps your team refine and scale what’s working.
How to Set Your Team Up for Success in the First 90 Days
The first 90 days often feel overwhelming. There are new tools, shifting expectations, and pressure to prove results quickly. Without a clear roadmap, it’s easy for teams to lose focus.
Nevertheless, this early stage doesn’t have to be a scramble. With the right approach, you can turn the first 90 days into a launchpad for long-term AI success.
Here are five ways to set your team on the right path:
- Establish a clear AI strategy tied to business outcomes: Define success and ensure every initiative connects to measurable goals such as efficiency, revenue growth, customer experience, employee efficiency, or faster time to market of a product launch.
- Conduct a readiness assessment: AI readiness is a critical stage in AI experimentation. Review your data quality, team skills, and governance structures to identify gaps before they become roadblocks.
- Identify early use cases that balance quick wins with scalability: One of the winning secrets of successful AI adopters is choosing a few high-priority use cases to scale AI impact. Choose projects that prove value quickly while laying the groundwork for bigger, more complex initiatives.
- Prioritize communication and team engagement: Keep leaders, managers, and frontline employees informed and involved so they understand how AI benefits their work.
- Build feedback loops and continuous measurement: Track progress early, share results, and refine your approach to maintain momentum.
With these steps, your team can approach AI adoption with clarity that delivers results from day one.
Avoid Common Pitfalls in Your First 90 Days of AI Adoption
What happens if your first 90 days don’t go as planned? Projects stall, leaders lose confidence, and AI becomes another costly experiment rather than a strategic asset.
These early missteps set a precedent that’s hard to undo. Even worse, getting stakeholders’ buy-in for another initiative could be tougher.
However, the reverse is also true. With the right strategy, those 90 days can build trust, prove value, and secure momentum for years.
Growth Acceleration Partners’ AI Acceleration Workshops help you get there. Book a consultation today and turn your first 90 days into a lasting advantage.
