5 AI Initiatives Overcoming Data Quality Challenges

We’ve all heard the adage ‘data is the new oil’ and in the Middle East, where the distillation of crude has helped transform desertscapes into thriving modern cities, it’s often stated that a similar enrichment of data will drive the digital, knoweldge-based economy. This has become even more pronounced in the era of AI — and experts frequently highlight that for the region to realise the promised US$320 billion in economic value from AI by the turn of the decade, enterprises will first need to enrich their data stores.

But with data relentlessly pouring in from every department, domain, and device, it’s easy to understand why business leaders often feel overwhelmed. If you’ve caught yourself thinking, ‘my organisation’s data quality is poor, so we’re a long way from getting value from AI,’ then you aren’t alone. Data quality is every organisation’s dirty secret.

Sorting Through the Spectrum

Data quality isn’t a binary concept — an organisation’s data stores are never either ‘perfect’ or ‘awful’. Instead, they exist on a spectrum, with some areas in great shape and others far more opaque.

Often, when leaders express concerns about data quality, they are referring to specific datasets. Perhaps customer records in a CRM system contain too many duplicate entries, or financial data is fragmented across multiple legacy systems, patched together with spreadsheets and workarounds. However, alongside these challenges, there will typically be strong data assets — often due to system requirements, regulatory obligations, or business needs. Business leaders might be under the impression that all AI data sets need to meet such quality criteria, but in reality, there are many impactful AI use cases that aren’t nearly as data dependent. Let’s explore some of these possibilities.

1) AI Enablement

One of the most accessible ways to introduce AI into an organisation is through co-piloting tools that enhance productivity across various functions. For example, providing employees with access to AI-powered tools such as ChatGPT Enterprise or similar platforms can drive efficiency gains in areas ranging from software development to sales and marketing.

The key to success with AI enablement lies in user engagement. Organisations should ensure that employees receive the necessary training and support to integrate these tools into their workflows. Encouraging experimentation and sharing best practices across teams can accelerate adoption and maximise the benefits.

2) Checks and Balances

AI can be particularly effective in automating processes that function as ‘checks and balances’ within an organisation. Using agentic AI approaches, businesses can develop autonomous agents that perform real-time validations and compliance checks.

These AI-driven processes can be applied across various back-end functions, from customer workflows to IT operations. Unlike AI models that depend heavily on historical data, these implementations work by following predefined rules and interacting with APIs, making them a viable option for organisations concerned about data quality issues.

3) Customer Communications

Customer service teams often spend significant time handling repetitive queries or requesting additional information from customers when submissions are incomplete. AI can streamline these interactions by automating basic customer correspondence.

For instance, AI-powered systems can analyse incoming queries and automatically draft response emails requesting the necessary clarifications. This approach ensures consistency in customer interactions while reducing the burden on human agents, allowing them to focus on more complex issues that require a personal touch.

4) Content Creation

Generative AI techniques can be harnessed to automate content creation without requiring extensive historical datasets. By using predefined guidelines and specifications, AI can generate marketing materials, draft business proposals, or even create regulatory-compliant content.

Beyond content generation, AI can also serve as a quality control mechanism, reviewing documents to ensure they align with brand guidelines, regulatory frameworks, or audience personas. This capability enables organisations to maintain high standards of communication while reducing manual effort.

5) Insight Extraction from Documents

AI can extract valuable insights from unstructured documents, even in the absence of a rich historical dataset. Organisations dealing with large volumes of contracts, reports, or regulatory filings can leverage AI to parse through these documents and compile structured, actionable insights.

For example, legal teams can use AI to scan thousands of contracts and extract key clauses, obligations, and risks, transforming unstructured text into a centralised, trusted data backbone. This approach enables organisations to unlock the value of existing information without the need for extensive data cleansing or restructuring.

Success Breeds Success

The AI use cases outlined above provide organisations with a clear path to achieving critical ‘early wins’ in their AI journeys. These initial successes can generate momentum, building confidence within the organisation and demonstrating tangible value to stakeholders.

While more advanced AI initiatives will eventually require robust data foundations, proving AI’s value early on can help secure executive buy-in and unlock budgets for broader data transformation efforts. Rather than waiting for perfect data quality, organisations should therefore explore AI opportunities that can deliver immediate benefits, setting the stage for more ambitious projects down the line.With such apragmatic approach that leverages AI in areas where data quality is less of a constraint, organisations can start reaping the benefits of AI today — while laying the groundwork for even greater advancements in the future.

The article is authored by Richard Pugh, SVP Global – Head of Data & AI, Endava