AI Investment in Australia and New Zealand Drives Data Focus

Organisations across Australia and New Zealand (ANZ) are accelerating artificial intelligence (AI) adoption, integrating intelligent technologies into business operations, predictive analytics, and decision-making. However, growing AI investment does not automatically translate into measurable business value.

The underlying data foundation matters more than you might assume. The performance of an AI model might take a hit because of unclear ownership, inconsistent records, fragmented information, or poor integration.

Hence, if reliable and scalable AI deployment is your lookout, focusing on data quality and governance is non-negotiable.

Organisations Aren't Ready Enough for AI Adoption

Recent Boomi-Omdia research indicates a hard-to-ignore gap between ambitions around AI and operational preparedness.

Over 70% of organizations in Australia and around 65% of those in NZ reported active AI initiatives. However, when it came to estimating AI success, close to 30% respondents in AU and over 30% respondents in NZ mentioned they couldn't do so effectively.

Why's this concerning? Say, a business automates document processing, introduces AI-supported analysis, or deploys a chatbot. But if it can't measure the consequent performance reliably, there's no way to know if the investment is boosting productivity, reducing costs, or enhancing customer experience.

Here's something else that the study revealed. Many organizations (39% in Australia and 43% in New Zealand) were found to be lacking a dedicated budget for AI. They were, however, on the verge of increasing AI investment soon.

So, as a business in ANZ, what to do? Should you bring down your AI adoption pace? Not really. Rather, consider investing in foundations that ensure sustainable AI adoption. Instead of simply buying yet another AI tool, it's time to check if the information going into it is trustworthy, accessible, and consistent.

Investing in AI Means Prioritizing Data Quality

AI systems are dependent on data for spotting patterns, churning out responses, and supporting decisions. Hence, duplicate, incorrect, obsolete, or incomplete information impacts the output quality.

Imagine a financial establishment that operates across ANZ. Its customer details might exist across transaction databases, onboarding platforms, customer service applications, etc. And the same individual might be represented with slightly different names in each system. Or their contact information might vary.

This means, an AI model might end up treating one customer as multiple persons while identifying patterns or personalizing communications. In other words, the analytics might not be as useful as expected or consumer insights might be skewed.

So, it isn't surprising that the Ministry of Business, Innovation and Employment (MBIE) in NZ makes data quality a priority for responsible AI usage. Also, data validating, de-duplicating, standardizing, and monitoring shouldn't stop ever.

Governance Is Essential for AI to Scale Efficiently

While data quality revolves around information reliability, governance establishes the methods for information:

• Access

• Management

• Protection

• Usage

Without governance, different organizational departments might have differing definitions for data fields. They might end up using information without proper ownership or creating overlapping integrations. AI teams might have a tough time zeroing in on authoritative sources or confirming if information is updated.

No wonder around 90% of organizations across ANZ are considering investing heavily in AI risk, governance, and compliance. However, less than 50% of them have formal AI-oriented governance policies.

Does the gap matter? Yes. Sure, informal checks and close oversight might be enough for a pilot project. However, if you use a system across several departments, responsibilities must be defined and data sources should be documented. You also need access controls and error review processes in place.

Policies around responsible AI usage in AU and NZ also reinforce a practical principle. You must weave AI governance in when picking a system, deploying it, and monitoring. It's more effective than adding governance after issues crop up.

Integration Fruitfully Connects Data with AI Value

No matter how high-quality your information, if it's stuck in disconnected applications, it doesn't offer much value. Hence, investing in data integration is critical. It collates information from multiple systems and makes the same available for relevant business processes.

In fact, over 90% of organizations across ANZ consider data integration (as well as orchestration) a priority. But fewer than half have a platform-driven approach for that. What's the risk?

Say, a retailer intends to leverage AI for demand forecasting by combining inventory, sales, and customer and supplier details. If these datasets aren't linked dependably, the AI model might end up working with inconsistent product identifiers or outdated stock figures.

Proper integration makes sure that the information available to AI is up-to-date and relevant.

Also note that over 70% of ANZ organizations found data quality and trust being affected by unmanaged shadow integrations. These are usually built for addressing immediate operational requirements.

Unmanaged integrations can become challenging to track, preserve, and secure though. The good news is, uniting integration tools and outlining clear standards can be helpful.

Data Measurement, Privacy, and Security Must Go Hand-In-Hand

As you continue connecting more and more information to AI systems, don't forget the importance of data protection and its appropriate usage. Over 90% of organizations across ANZ are making data quality, privacy, and security a priority.

Data residency is garnering increasing attention too, particularly in case of sensitive customer details, cloud-powered AI services, or cross-border operations. So, as a business in Australia or New Zealand, you must have clarity on:

• Location of information storage

• Who has access

• What providers do with the data

• What regulatory obligations are applicable

You can start by categorizing data as per sensitivity. Define access-related permissions next and check if information suits a specific AI use case. Before deployment, don't forget to establish baseline metrics either.

These might include duplicate rates, cost per transaction, record accuracy, error rates, etc. based on the application. Measuring these metrics pre and post implementation can help you differentiate between actual improvements and activities that only seem innovative.

Make Your AI Investment in ANZ Worthwhile

As a business operating in Australia or New Zealand, don't think of data quality, integration, and governance as distinct technology projects. They are interconnected investments.

So, identify the information necessary for every AI use case, assign ownership, and standardize key fields. Also automate validation and track quality of data as systems change. Ensure teams can trace the data behind outputs generated by AI. They should also be able to estimate if such outputs offer business value.

How to establish this foundation? Invest in tools for verifying, de-duplicating, correcting, and standardizing customer details across global markets. They will seamlessly complement broader practices surrounding security, integration, governance, and AI oversight.