Artificial intelligence is becoming increasingly embedded within everyday business operations. From Microsoft Copilot and ChatGPT to AI agents, predictive analytics and automated workflows, organisations are finding new ways to use AI to improve productivity and support decision-making.
However, the success of these tools depends heavily on something organisations have been managing long before the emergence of generative AI: the quality of their data.
AI does not automatically correct inaccurate, incomplete or outdated information. In many cases, it can amplify existing data problems, producing outputs that appear credible but are ultimately based on information that cannot be trusted.
This article forms part of our The Dark Side of AI series, exploring the emerging security, governance and operational risks organisations should consider as AI adoption accelerates.
Why Does Data Quality Matter for AI?
The principle of “garbage in, garbage out” has existed in technology for decades, but AI has made it increasingly important.
During our recent Data for AI: The Foundation Everyone Skips webinar with BloomAI, we discussed how AI tools can only be as useful, accurate and safe as the data they are allowed to use.
AI systems can potentially draw information from across an organisation, including CRM and ERP platforms, finance systems, databases, SharePoint, spreadsheets, documents, emails and other business applications.
This creates significant opportunities, but it also means existing data-quality problems can quickly become AI problems.
If the information an AI system relies upon is outdated, duplicated, incomplete or inconsistent, the outputs it produces may reflect those same weaknesses.
What Do We Mean by Bad Data?
“Bad data” can take several different forms. It could include:
- Outdated data, such as policies or product information that are no longer accurate
- Incomplete data, where important information is missing
- Duplicate data, where multiple records exist for the same information
- Inconsistent data, where different systems contain conflicting versions of the truth
- Inaccurate data, where information is simply incorrect
- Siloed data, where important information exists but is inaccessible to the AI system
IBM identifies many of these as common data-quality issues and highlights the potential for poor-quality data to undermine analytics and AI outcomes.
The challenge is that AI may not necessarily know that the information it has been given is unreliable. It can process that information and produce a confident, convincing response regardless..
Bad Data vs AI Hallucinations
This is an important distinction.
An AI hallucination occurs when an AI model generates inaccurate or fabricated information that isn’t supported by its underlying information or context.
With bad data, the AI may be working exactly as intended. The problem is that the information it has been given is itself inaccurate, incomplete or outdated.
For example, imagine an organisation introduces an AI assistant to help employees answer questions about company policies. If the system is connected to an outdated HR policy, it could confidently provide employees with incorrect guidance.
The AI hasn’t necessarily invented the answer. It has simply relied upon the wrong source.
This is one of the reasons data quality is as important as ever for organisations as they move beyond experimenting with AI and begin connecting it to their own business information.
When Bad Data Creates Real-World Consequences
A useful example discussed during our webinar involved Air Canada’s customer service chatbot.
The chatbot provided a customer with incorrect information regarding the airline’s bereavement fare policy. The information ultimately resulted in a dispute that reached Canada’s Civil Resolution Tribunal.
The example demonstrates a wider problem for organisations deploying AI: if the information grounding an AI system is outdated or incorrect, the resulting customer experience can also be incorrect.
For businesses, the consequences could extend beyond an incorrect chatbot response.
Poor-quality data feeding AI systems could contribute to inaccurate customer communications, flawed analysis, unreliable forecasts, incorrect recommendations and poorly informed business decisions.
As organisations increasingly introduce AI into automated workflows and business processes, the potential impact becomes greater still. An error that might previously have affected one spreadsheet or report could potentially be repeated across hundreds or thousands of AI-assisted interactions.
AI Can Expose Data Problems You Didn’t Know You Had
Another challenge is simply knowing where your organisation’s information lives.
Business data is rarely contained within one perfectly organised system. Information may be spread across SharePoint sites, CRM platforms, databases, finance systems, spreadsheets, emails, legacy applications and individual locally saved documents.
During the webinar, we discussed how organisations can sometimes assume the information they need exists within a particular business system, only to discover that valuable context remains elsewhere, including older systems and spreadsheets still used by individual teams.
AI can make these existing weaknesses more visible.
An organisation might have numerous versions of the same policy, conflicting customer records or documents that haven’t been reviewed for several years. Once AI begins retrieving, summarising and acting upon that information, inconsistencies that previously remained relatively contained can influence outputs across the organisation.
The Business Risks of Bad Data and AI
As AI becomes more integrated into business processes, poor data quality can create a range of operational, financial and regulatory risks.
Incorrect information could influence decisions, produce misleading customer communications or reduce the reliability of automated processes. Where personal or sensitive information is involved, organisations must also consider their wider data protection and governance responsibilities.
There is also a risk to AI adoption itself.
Employees are unlikely to trust an AI tool that repeatedly provides inaccurate or inconsistent answers. During the webinar, we discussed how confidence in AI can be difficult to establish and quickly lost when employees encounter unreliable outputs.
An organisation can therefore invest significantly in AI technology only to see adoption stall because the underlying data wasn’t ready.
How Can Organisations Prepare Their Data for AI?
Data readiness does not necessarily mean rebuilding your entire data environment before experimenting with AI.
Instead, organisations should understand which data matters for their intended AI use cases and establish appropriate processes around its quality, ownership, security and accessibility.
During our webinar, we outlined a six-step approach:
1. Discover
Identify where important organisational data resides, including databases, applications, SharePoint sites, documents and other knowledge sources.
2. Classify
Understand what information you hold and how sensitive it is. HR records, customer information and financial data, for example, are likely to require different controls from publicly available information.
3. Assign ownership
Establish who is responsible for maintaining important datasets and ensuring the information remains accurate.
4. Improve quality
Identify outdated, duplicated, incomplete or inconsistent information and establish processes for maintaining its quality.
5. Secure access
Review who can access information and whether existing permissions remain appropriate before connecting AI tools to organisational data.
6. Monitor and improve
Data readiness isn’t a one-off exercise. Information changes continuously, meaning organisations should regularly review data quality, permissions, governance and controls.
AI Governance Starts With Your Data
Organisations often focus on which AI platform or model they should adopt. However, selecting the right technology is only part of the equation.
Effective AI governance should also consider the information those systems rely upon.
Organisations need to understand where important data is located, who owns it, who has access to it and whether it remains accurate and trustworthy. These questions become even more important as businesses move from standalone generative AI tools towards agents and automated workflows that can access multiple systems and act upon organisational information.
The webinar highlighted six recurring data challenges when organisations deploy AI:
- Poor-quality data
- Limited visibility of existing information
- Overexposure of data
- Regulatory blind spots
- Pilots that struggle when scaled
- A lack of clear ownership and review.
Addressing these foundations can help organisations deploy AI with greater confidence rather than discovering existing data problems after implementation.
AI Governance Starts With Your Data
AI has the potential to help organisations extract far more value from their data, improving productivity, automating processes and supporting better decision-making. But AI cannot compensate for information that an organisation cannot find, control or trust.
In fact, AI can make existing data-quality problems considerably more evident.
Before expanding AI adoption, organisations should therefore consider a fundamental question: is the data behind our AI good enough to trust the answers it gives us?
If your organisation is exploring Microsoft Copilot, AI agents or wider AI adoption initiatives, now is the time to ensure the right data, governance and security foundations are in place.
At HAYNE.cloud, we help organisations assess their readiness for AI through data and governance reviews, Microsoft 365 security assessments and practical guidance designed to support secure, responsible and effective AI adoption.
Bad data is just one of several emerging risks associated with AI adoption.
As part of our The Dark Side of AI series, we will continue exploring the security, governance and operational challenges organisations should understand as AI becomes increasingly embedded within the modern workplace.
Final Thoughts
AI has the potential to help organisations extract far more value from their data, improving productivity, automating processes and supporting better decision-making. But AI cannot compensate for information that an organisation cannot find, control or trust.
In fact, AI can make existing data-quality problems considerably more evident.
Before expanding AI adoption, organisations should therefore consider a fundamental question: is the data behind our AI good enough to trust the answers it gives us?
If your organisation is exploring Microsoft Copilot, AI agents or wider AI adoption initiatives, now is the time to ensure the right data, governance and security foundations are in place.
At HAYNE.cloud, we help organisations assess their readiness for AI through data and governance reviews, Microsoft 365 security assessments and practical guidance designed to support secure, responsible and effective AI adoption.
Bad data is just one of several emerging risks associated with AI adoption.
As part of our The Dark Side of AI series, we will continue exploring the security, governance and operational challenges organisations should understand as AI becomes increasingly embedded within the modern workplace.
