The Boardroom Mandate: "We Need AI, and We Need It Now"

Melissa Hayes
Oct 15
4 min read

Updated: 1 day ago

The call for AI is loud and clear. But the reality is sobering.

IBM's Global AI Adoption Index 2024 reveals that data complexity and data silos are the top barriers to AI adoption, affecting 24% of enterprises globally.

And, even more concerning,

Recent MIT Sloan Management Review research found that only 10% of companies achieve significant financial benefits from AI, with data infrastructure cited as the primary differentiator between success and failure.

The Verified Data Quality Challenge

The impact is measurable:

Gartner reports poor data quality costs companies an average of $12.9 million annually (2021).
IBM estimates that poor data quality costs the US economy $3.1 trillion per year (2016).
24% of enterprises cite data issues as their primary AI adoption barrier (IBM, 2024).

When you layer AI onto poor quality data, these costs are amplified rather than solved.

Common Challenges in Australian Enterprises

Based on our experience working with Australian businesses, we commonly observe:

Spreadsheet proliferation: Product data managed across multiple disconnected files.
Channel inconsistency: Different product information across different sales channels.
Vendor data chaos: Supplier information arriving in incompatible formats.
Classification confusion: Products categorised differently across systems.

These aren't just inconveniences – they're fundamental barriers to AI success.

PATTERN 1: *The Recommendation Engine Challenge*	PATTERN 2: *The Inventory Optimisation Problem*	PATTERN 3: *The Success Approach*
We've seen retailers invest heavily in AI-powered recommendation engines, only to find they recommend inappropriate products because seasonal or regional data is incomplete. Without complete product attributes, AI cannot make intelligent recommendations.	Distributors implementing predictive analytics for inventory often discover that missing weight and dimension data leads to miscalculated shipping costs and warehouse space allocation. The "optimisation" creates additional costs rather than savings.	Companies that succeed with AI typically invest first in comprehensive product data management. Major Australian retailers using platforms like Stibo STEP report that their AI initiatives deliver more consistent value because they're built on clean, standardised, governed data.

The Five Pillars of AI-Ready Product Data

Through our work with enterprises across Australia, we've identified five critical dimensions that determine AI readiness:

Completeness: The Foundation

AI models require comprehensive training data. Missing attributes create blind spots.

When your products lack complete specifications, descriptions, and metadata, AI can't understand what you're selling, let alone optimise it.
Successful AI implementations typically require near-complete core attributes and substantial coverage of extended attributes.

Consistency: The Language AI Understands

When "Large" in one system is "L" in another and "LRG" in a third, AI gets confused.

Inconsistent data formats, units of measure, and naming conventions confuse machine learning models.
The fix? Implement strict data standards and automated validation. This isn't glamorous work, but it's the difference between AI success and failure.

Classification: The Structure for Intelligence

Without proper taxonomy and categorising, AI can't identify patterns or relationships.

It's like asking someone to organise a library where books are randomly scattered with no cataloguing system.
Industry standards like GS1 or UNSPSC provide a foundation, which should be enhanced with company-specific classifications that reflect your business logic.

Connectivity: The Nervous System

AI needs real-time access to data.

If your product information is locked in silos, updated through batch processes, or requires manual extraction, your AI is always working with yesterday's information.
Modern PIM/MDM platforms provide the APIs and integration capabilities AI requires for systems and output requirements.

Governance: The Trust Framework

Who owns product data? Who can change it? How do you ensure quality over time?

Without governance, data degrades rapidly. AI trained on degrading data delivers degrading results.
Clear ownership, quality KPIs, and automated monitoring are essential. Data governance isn't bureaucracy – it's business continuity.

The Investment Perspective

Executives often ask about ROI. Based on our client experiences, organisations typically see:

Reduced time-to-market for new products through automated workflows.
Decreased operational costs in product management and maintenance.
Improved customer satisfaction from accurate, consistent product information.
Better foundation for AI initiatives that actually deliver value.

Note: Specific percentages vary significantly by industry, company size, and starting point.

The Competitive Reality

While you're reading this, keep in mind your competitors are in one of two camps:

Struggling with AI because they ignored data quality (the majority).
Succeeding with AI because they fixed their data first (your future threat).

The window for competitive advantage through AI-ready data is narrowing. Early movers in the Australian market have already built their data foundations. They're now scaling AI while others are still discovering the problem.

The Bottom Line

According to Gartner's prediction, 80% of enterprises will shut down their unmanaged AI pilots by 2025. The primary reason? Data quality issues.

Don't let your product data become the bottleneck to your digital transformation.

The gap between AI leaders and laggards isn't about who has the best AI technology. It's about who has AI-ready data. If you're serious about AI success, start with data reality.

Take Action Today!

Evaluate your AI readiness across 25 critical factors and understand which quick wins can deliver immediate value.

Because in the AI race, it's not the smartest algorithm that wins. It's the readiness of the data to deliver!