April 29, 2026
In the digital world,
data is no longer a by-product of business systems, it’s a core generator of business value creation
Is your enterprise data AI ready?
A recent IDC survey documented that:
- 70% of CIOs reported a 90% failure rate for their custom-built GenAI projects
- 66% reported a 90% failure rate with vendor-led proofs-of-concepts
Here are some of the major sources of the 90% failure rate:
- Lack of sufficient quantity of data – AI needs a large amount of good, easily accessible data to be trained to learn from it
- Lack of sufficient quality of data – “garbage in garbage out”
- Underestimating the time and cost of aggregating data from multiple different sources around the company (sales, marketing, legal, finance, IT, HR, procurement)
- Absence of an enterprise data governance policy
In order for companies to move from data disorder to data readiness, data can no longer be seen as a by-product of business systems, but rather a generator of increased revenues, margins, and profits.
In most companies, data exists in many forms, e.g. structured and unstructured across multiple different systems which more often than not can’t communicate with each other. As such, it is nowhere near ready and able to train AI, GenAI, or Agentic AI.
In addition, many companies do not have a data governance framework and process in place and those that do were designed for human-paced consumption. AI significantly increases both the speed and volume of data demand and introduces non-human consumers.
To get the most competitive advantage out of the successful deployment of AI, you must be able to curate the different types and sources of data into one metadata resource. This resource must be easily accessible to your different AI tools while still having guardrails to make sure it is used safely and does not violate privacy or ethical policies.
An effective data governance strategy must now include both static systems of record and dynamic, self-evolving, decision-making, systems of engagement and systems of intelligence. Simply put, the resources and budget necessary to successfully leverage the business value of a company’s data must have a meaningful ROI to justify them.
Data debt is an existential risk to a company’s stability and performance
Early use cases have shown that the successful adoption and utilization of AI in all its forms requires a rock-solid data governance strategy and set of operating practices. Data debt is a major contributor to a company’s lack of data readiness. If your company has data debt, in all likelihood it’s a major source of AI project failures.
Data debt can accumulate from multiple sources and is a major source of trapped value across any organization. Here are some questions to help you identify and document these multiple sources of data debt:
- How many data management systems are in use in your company?
- How many versions of those are in use?
- How many of those versions are up to date?
- Of your data storage repositories, how many are clean, well-structured, and easily accessible?
- How many interfaces are in use to keep data in overlapping databases synchronized?
- Are they batch or real time?
- Are they custom-coded or connector-based?
Data debt is often the result of multiple teams using different data definitions from siloed data systems that operate independently with inconsistent data entry standards. For example, in work I did with a major software company, anyone in sales or marketing who wanted to know how many different products a given customer used had to open up 5 separate applications – none of which used a common data entry standard.
Companies have also accumulated data debt over years from mergers and acquisitions. Systems were layered in to meet specific departmental needs and regulatory requirements without consistent standards and processes which resulted in fragmented data environments.
Juan Nassif, regional CTO for software development provider BairesDev describes the challenge this way: “AI is different; it’s far less forgiving and it quickly exposes duplicates, inconsistent definitions, missing context and mystery fields with unclear linage. When you scale beyond pilots, those issues show up as model underperformance, higher iteration cycles and rising operational costs.”
The broader impact of this fragmented data environment is the legal, regulatory, security, and privacy risks it presents to any company. In order to leverage your company’s data as a source of business value creation with AI, you need the answers to these questions:
- Is the data used by AI systems trustworthy?
- Are the models legally and ethically compliant?
- Do the deployed AI solutions respect privacy laws across jurisdictions?
- Is there adequate control over the use of GenAI and Agentic AI systems?
- Can these systems explain their decisions when challenged?
Building an AI-ready data strategy that is a core generator of business value creation
The 2025 IBM study AI Ambitions are soaring, but is Enterprise Data Ready? documents how many companies are struggling to leverage data to drive new revenues and profits. Only 26% of the 1,700 CDOs worldwide who responded feel confident that their data can support new AI-enabled revenue streams. To achieve that revenue generating goal, companies must restructure their siloed based data strategies to create an AI-ready integrated data architecture capable of supporting enterprise-wide use cases.
IDC’s Content Creation in the Age of GenAI concluded that “traditional data strategies were built for reporting, business intelligence (BI), and automation, but AI requires far more dynamic, granular, and real-time data pipelines that can fuel iterative, model-driven workflow… The AI era demands that organizations evolve from a collect/store everything mentality toward intentional, value-driven data strategies that balance cost, risk, and specific AI outcomes they want to achieve.”
To embark on a transformational change of this breadth and scope first requires a mindset shift from seeing data as an operating cost to seeing it as a strategic business growth asset. As such, data should be seen as a set of products, services & solutions that can be monetized. In my work with clients who have embarked on this transformational shift, we have utilized these steps:
- Treat data as a product with individual product owners, with a P&L mindset, who are responsible for using it to generate revenues and profits and measured accordingly.
- Breakdown vertical business unit and departmental data silos and create cross-functional enterprise teams with incentives to share data rather than protect it.
- Invest in new data technologies for the AI era including data lakes, vector databases, and scalable object storage that treat data as a reusable product not just a single pipeline.
- Ensure that both structured and unstructured data is AI-ready.
- Implement a data governance program that ensures that any data used is secure, trustworthy, legally & ethically compliant, and respects privacy laws.
The speed and proliferations of new AI, GenAI, & Agentic AI products and services is unprecedented. Companies are still struggling to develop coherent and solid strategies to successfully adopt and deploy these myriad new disruptive digital technologies. What they are beginning to learn is that they need to treat their data as a strategic asset that is foundational to their future growth and success.
A recent Accenture study highlighted the competitive benefits for those companies that have taken that step:
- Only 16% of companies are “reinvention ready” with fully modernized data foundations and end-to-end platform integration to support automation across most business processes.
- These reinvention ready organizations have 2.5 times higher revenue growth and 2.4 times greater productivity improvement then their peers.
The evidence is clear that AI in all its forms is setting the new standard for competitive excellence.
|
