Last week, at the Connexion trade show, we hosted a packed conference session to explore a question as exciting as it is complex: Is your organization ready for artificial intelligence?
The discussion echoed what we’re seeing on the ground. Interest in artificial intelligence is strong, but it often comes with a sense of uncertainty. Many organizations are eager to get started but want to make sure they are properly prepared. And they are right to be cautious.
According to a Gartner study, over 60% of AI projects fail. The reason isn’t the technology itself, but the lack of solid foundations: unreliable data, poorly integrated systems, and weak governance.
This article recaps the key takeaways from our session, supported by the latest Gartner insights, to help you evaluate your organization’s readiness for artificial intelligence.
Why Do So Many AI Projects Fail?
Before moving forward, it’s important to understand why so many AI initiatives fall short. Despite the excitement and investment, a large number of projects fail to deliver meaningful results.
According to Gartner, 63% of organizations either lack proper data management practices or are unsure whether their practices are sufficient to support AI efforts.
This challenge stems from several recurring issues, seen across both large enterprises and smaller businesses:
- Incomplete, disorganized, or outdated data
- No clear strategy for data governance and usage
- Poorly integrated tools or tools that don’t match actual needs
Many organizations jump into AI by focusing on the technology first, without laying the proper groundwork. But AI depends on data that is reliable, well-structured, and accessible.
As we emphasized during our conference at Connexion, the intention to do things right is there. What is often missing is a clear method for assessing readiness.
5 Requirements for AI Readiness
As mentioned earlier, many AI projects fail not due to a lack of ambition, but because essential foundations were missing. To avoid repeating the same mistakes, it is critical to validate a few key elements before selecting any tools or use cases.
Below are five areas to examine to determine whether your organization is truly ready to adopt artificial intelligence.
1. Is Your Technology Infrastructure Ready?
One of the main obstacles to AI adoption is the state of existing systems. Many organizations want to implement AI tools, but their tech foundations are not ready.
Before anything else, make sure your systems:
- Are up to date and properly maintained
- Can communicate with each other (CRM, ERP, business tools)
- Allow smooth data collection, storage, and access
- Can support task or data flow automation
If your software is isolated or data access is limited, AI is more likely to add complexity than deliver value.
Goal: A flexible, interconnected, and scalable architecture that can support both current and future AI projects.
2. Do You Have a Clear Data Strategy?
Having data is not enough. For an AI project to succeed, you need to know what data you have, where it lives, what purpose it serves, and who is responsible for it. Many organizations accumulate large volumes of data without a clear strategy. The result is often siloed information, duplicates, inconsistent formats, or data that cannot be used effectively.
Best practices include:
- Clearly identifying the data sources used across the organization
- Maintaining a centralized inventory or catalog of your data
- Providing teams with reliable, up-to-date, and easy-to-understand dashboards
- Supporting key decisions with consolidated, clean, and consistent data
If your data is not well organized, accessible, and properly governed, you risk making poor decisions or training your models on biased inputs.
Goal: A clear, business-aligned data strategy that is shared across teams, from IT to operations to leadership.
3. Is Your Data AI-Ready?
Having a data strategy does not guarantee that your data is ready for AI. Models require data that is reliable, structured, well-formatted, and aligned with the intended use cases. Too often, data is still unclean, filled with duplicates, errors, or missing values. It may also be scattered across systems, poorly structured, non-standardized, or lacking documentation.
Here are key actions to assess and improve your data quality:
- Clean data thoroughly and on a regular basis
- Standardize formats (dates, currencies, units, codes)
- Document fields, origins, and data transformations
- Set up update processes and quality control rules
If your data does not meet these standards, you increase the risk of bias, errors, and failure once in production.
Goal: Data that is usable, well-structured, and validated against clear quality criteria.
4. Do You Have the Right Tools to Prepare Your Data?
Manually preparing data quickly becomes inefficient as volumes grow or formats vary. AI requires tools that can automate, structure, and ensure the reliability of your data.
Here are the tools and approaches to consider:
- Data transformation platforms (ETL, automated pipelines, etc.)
- Tools to process unstructured data such as PDFs or images
- Automated data cleansing and updating processes
- Cataloguing and metadata to improve accessibility
- Synthetic data generation when real data is limited
If you rely on manual processes or tools that are not suited to your needs, your projects will slow down, errors will multiply, and the impact of your AI initiatives will be limited.
Goal: A robust, automated processing pipeline adapted to various types of data.
5. Is Your Governance Fit for AI?
Data governance is often underestimated, even though it is essential to the success of AI projects. Having good data is not enough. You also need to know who is responsible for it, how it is used, and what rules apply. AI also brings specific challenges, such as algorithmic bias, privacy concerns, and compliance with local or international regulations, including Bill 25 in Quebec.
Here are the practices to put in place:
- Define roles and responsibilities related to data (owner, manager, user)
- Set rules for data quality, security, and access
- Document data flows and transformations
- Use human-in-the-loop (HITL) validation for automated decisions
- Stay informed about evolving legal standards and requirements related to AI and data
If governance is missing or unclear, your organization faces legal risks, biased outcomes, and a lack of trust from both internal teams and customers.
Goal: Clear, proactive governance aligned with today’s expectations for AI ethics, compliance, and performance.
Innovation as the Next Step
Once your infrastructure, data, tools, and governance are in place, AI can become a true driver of innovation. The next step is to experiment, test targeted use cases, and then scale up.
The data speaks for itself. According to the Conseil de l’innovation du Québec (2024), SMEs that invest in R&D report, on average:
- +13% increase in assets
- +25% growth in sales
- +27% increase in profitability
In short, preparing for AI also means preparing your organization to innovate more effectively.
Conclusion
Artificial intelligence has the potential to transform your operations, your products, and your decision-making. But to create real value, you need to start with strong foundations: the right systems, well-prepared data, reliable tools, and clear governance.
Organizations that take the time to prepare properly increase their chances of success, reduce risks, and set the stage for long-term innovation.
Want to learn more? Contact our team today to find out how to start your AI project on the right track.
