Words like experimentation and innovation used to dominate the AI conversation, but that’s changed fast. Now that AI has become a real business imperative, the words we hear more often are expectation and impact. And as a business leader, you’re the one expected to deliver that impact — often faster than budgets, talent, or infrastructure can realistically support.
But some organizations are struggling to achieve meaningful results from AI initiatives, with numerous projects quietly abandoned before reaching scale.
According to Gartner's 2025 Current State of AI in Finance report, adoption is growing slowly — 59% of finance functions reported using AI in 2025. However, even among adopters, 91% said early-stage impact was “low or moderate,” reflecting persistent challenges such as poor data quality and limited internal technical expertise.
Without a strong foundation — including reliable data, well-defined processes, and organizational readiness — ambitious back-office AI projects are at high risk of faltering or being rolled back into legacy systems.
To help you avoid scrutiny and to drive greater impact from your AI journey, we will explore three main reasons why AI initiatives fail to meet expectations, and how you can avoid them.
Three reasons most AI initiatives fail
Across industries, the same three issues show up again and again. These are core reasons so many AI initiatives stall before they create real value.
- The business has a lack of clearly defined business outcomes.
- AI is not embedded into workflows resulting in poor adoption (usage).
- Business data is not integrated and is unreliable — leading to unpredictable, inconsistent outputs.
How to protect your AI investments from failure
The question becomes: how do you deliver real value without falling into the traps that stop so many AI projects?
It's not easy. While pressure has increased for CIOs, for most organizations the foundation on which AI depends is still shaky. Many CIOs are being asked to deliver on an AI mandate without the right building blocks. Siloed systems, inconsistent data, and weak governance can derail even the most well-intentioned efforts and turn advanced models into expensive experiments with little business impact.
These challenges don’t just limit impact — they undermine trust in the system. Responsible AI is now a must-have, not a nice-to-have. CIOs are expected to deliver transparency, auditability, and explainability, as well as demonstrate confidence that insights flowing through the business are accurate, compliant, and secure.
Let's look at each mistake in turn and how to avoid them.
Mistake 1: AI fails when companies start too big and too broad
It’s easy to get swept up in the excitement of what AI can do. The potential is huge, and many organizations leap straight into multi-year transformation programs. But these big initiatives often struggle to move beyond the pilot phase. The gap between ambition and practical delivery becomes too wide, and ROI slips away.
A better approach: Start small and strategic with high-value workflows
The biggest AI returns come from more focused operational areas — finance, procurement, supply chain, workforce planning — where automation cuts cost, speeds analysis, and improves resilience.
To make this happen, you should:
- Start with focused, high-value use cases for AI within the business.
- Use AI to accelerate planning cycles and automate repetitive analysis.
- Measure value through speed, accuracy, and financial impact.
This approach creates early wins that make future investment easier to secure, while ensuring a change management process is built in from the start.
Mistake 2: AI adoption fails when insights don’t change how people work
Even when AI produces good insights, they often sit in the wrong place — hidden in dashboards, trapped in specialist tools, or delivered in reports that arrive too late to matter. Teams stick to familiar habits, data scientists get pulled into translation work, and AI sits outside the everyday flow of operations.
A better approach: Embed AI into workflows to drive absorption
For AI to create real value, it must appear at the exact point where decisions are made — within planning cycles, scenario models, approval flows, and day-to-day operational processes. When intelligence is delivered in context, users don’t need to interpret complex outputs or rely on technical teams to translate them. This improves the employee experience by reducing friction, making insights easier to act on, and encouraging consistent adoption. Decision-making simply gets faster and better.
To be a champion for this kind of transformation in your organization, you should:
- Use AI inside key planning, forecasting, and operational processes.
- Ensure the insights and intelligence generated are accessible to non-technical users.
- Measure success by workflow impact, not feature usage.
When AI lives inside workflows, adoption becomes organic rather than forced.
Mistake 3: AI fails without integrated, trusted, high-quality data
This is the biggest failure point of all. AI is only as good as the data underneath it, and in most organizations, that data is scattered across ERP, CRM, HCM, and operational systems — all with different definitions and rules. These systems weren’t built to deliver real-time, cross-functional intelligence. When AI pulls from inconsistent data, its outputs become unreliable. Teams lose trust in the findings, and adoption breaks down.
A better approach: Make data integration and data quality the highest priority
AI only reaches its potential when it is built on a single, governed, connected layer of business data. This creates a reliable foundation for forward-looking insight and ensures that predictions and recommendations are both explainable and actionable.
To make this happen, you should:
- Create a single, governed source of truth.
- Integrate planning data across systems and functions.
- Use AI confidently with transparent, consistent business logic.
With unified data behind it, AI can finally do what you most want it to do — support better, faster decisions across the business.
Build the foundation now to unlock AI's real value
Most AI failures come down to the same core issues of unchecked ambition, poor user adoption, insights that never make it into real decisions, and data foundations that simply aren’t ready.
When CIOs start with the workflows that matter, put intelligence directly into processes, and build on clean, connected, well-governed data, AI changes from being a risky experiment to become a meaningful driver of performance. It’s a way to make faster, better decisions in a time when they're needed most, and to give your business more clarity and confidence in those decisions — wherever and whenever they are made.
To get the most out of AI, make sure the platforms you choose are built for real-world complexity, not isolated experiments. This is how CIOs shift from hype to real impact and set their organizations up to succeed as AI continues to evolve. That is why organizations turn to Anaplan. The Anaplan platform is built with AI at its core and designed to support real-world complexity, enabling teams to move beyond experimentation and apply AI across the entire enterprise.
