AI-Powered Digital Transformation: Get Your Data and AI Ready

Key Takeaways

• Leverage AI to achieve digital transformation goals: enhanced efficiency, decision-making, customer experiences, and more.
• Address common challenges in managing SAP master data by using AI tools to automate SAP processes and ensure data quality.
• Create an AI-driven data and process improvement loop to continuously enhance your business operations.

Digital business transformation remains a top priority for organizations across industries.

More recently, the spotlight has shifted towards artificial intelligence (AI) as an enabler for many of the benefits that top transformation priority lists – including operational efficiency, improved decision-making, and enhanced customer experiences. At the core of these initiatives is a renewed focus on readying the diverse data and processes needed to build, train, and optimize sophisticated new AI applications.

Let’s explore what makes AI so critical to digital transformation, how to overcome common challenges, and what you need to ensure ongoing success in your initiatives.

AI for Digital Transformation

Digital business transformation is about moving from traditional, analog processes to digital ones – making your operations faster and more efficient.

A recent TDWI Best Practices Report found that top priority outcomes for digital business transformation in 2024 include:

• increased operational efficiency
• better data-driven decisions
• superior customer experiences

How are businesses planning to achieve these goals and more?

The research found that organizations view AI as essential in these digital transformation initiatives, with approximately 75% of respondents rating it as “very important” or “somewhat important” to their success; only about 10% said AI was either somewhat unimportant or very unimportant.

So, it’s clear that businesses see AI as a driver of efficiency, cost reductions, sales growth, and more – but what does that look like in practice? Who are the key players in your organization who will develop these applications?

Key Players in AI Development

Enterprises increasingly rely on AI to automate and enhance their data engineering workflows, making data more ready for building, training, and deploying AI applications. This involves various professionals. For example:

• Data scientists and machine learning specialists develop AI applications, and their expertise is crucial for transforming data into actionable insights and automating business processes.
• Business analysts and subject matter experts provide domain knowledge and ensure that AI applications meet business needs.
• Data engineers focus on tasks like cleansing and managing data, ensuring its quality and readiness for AI applications.

These roles, along with others like data product managers, software developers, and more all contribute to the development and optimization of AI applications.

The Growing Importance of Generative AI (GenAI)

The TDWI research found that as modern businesses increasingly depend on a deepening stack of AI, advanced analytics, and decision-support technologies, GenAI is making a fast climb to the top of priority lists.

GenAI includes technologies like ChatGPT and other large language models. These technologies are used for various applications, from automating content generation to enhancing decision support systems.

When asked how their company is using GenAI, some of the top responses in TDWI’s survey showed that:

• 40% of organizations are exploring how to use GenAI to build applications that use their own company data.
• 28% are experimenting with private models built or trained internally – to build both internal applications and data products.
• 24% say different parts of their company are experimenting with public models to improve productivity (for example, writing marketing content or creating slides).

When it comes to digital transformation, GenAI can bolster your efforts across the business. It’s transforming the way we work – and this is especially true for data engineers and scientists.

An increasing number of GenAI tools use large language models that automate key data engineering, governance, and master data management tasks. These tools can generate automated outputs including SQL and Python code, synthetic datasets, data visualizations, and predictions – significantly streamlining your data pipeline.

This automation helps make your data AI-ready, enabling more powerful AI applications and pushing your business transformation journey forward. Let’s dive deeper into data readiness next.

Making Your Data AI-Ready

Using AI in data engineering workflows can automate processes including data acquisition, profiling, transformation, and cleansing – all with the goal of creating high-quality, accurate data that can be used to build and train effective AI models.

But there are complexities and challenges you need to be aware of along the way, particularly when you’re looking to apply AI to master data processes in your SAP ERP system.

Multi-Dimensional Master Data Complexities

Master data includes critical business information about your customers, vendors, materials, and financial data – making it central to your business operations. However, managing this data comes with several layers of complexity that can be broken down into the following categories:

• Data complexity: Master data often consists of numerous data objects (a single SAP material master record typically has hundreds), making it complex to manage and maintain.
• Process complexity: Creating and managing master data requires coordination across multiple departments, each with different priorities and workflows.
• Organizational complexity: Aligning different departments and gaining consensus on data governance and quality standards can be time-consuming and challenging.

Addressing Master Data Challenges

Given the complexity of master data, ensuring its quality and governance is crucial.

Think about material master data, for example. Accurate and robustly governed material master data supports various business processes, from production and inventory management to finance and marketing. But when data quality and governance are missing, you’re going to run into problems in several areas:

• Process complexity and inconsistencies. Different individuals handle processes differently, leading to inconsistencies and potential issues within your organization.

• Data creation and management processes. These processes are complex and involve many people across various parts of your company, making coordination and accuracy challenging.
• Data quality and maintenance. Maintaining high data quality isn’t just about the initial entry; it requires ongoing updates and changes to records, such as location or plant extensions.
• Process simplification and standardization. Manual processes can lead to variations in how tasks are performed, which complicates tracking, governance, and problem resolution.

These challenges highlight the importance of effective master data management in ensuring accurate and reliable data across all of your business operations. That’s where predictive AI can help.

Predictive AI for Master Data Management

While generative AI tools like ChatGPT are revolutionizing content creation and decision support, predictive AI is invaluable for managing master data processes – helping you address many common challenges by automating data entry and improving data quality.

Creating and maintaining high-quality master data traditionally involves complex business rules. These rules require significant input from and reliance on subject matter experts (SMEs), and achieving cross-functional agreement and alignment can be time-consuming.

What if you could eliminate the need for many of these rules by using AI?

Predictive AI can do just that. For instance, in the material master creation process in SAP, predictive AI models can analyze historical data to predict and pre-fill data fields. Instead of manually entering hundreds of data objects, the model uses past data to suggest values for these fields.  The model might color-code these predictions based on confidence levels: green for over 95% confidence, blue for 80-95%, and other colors for below 80%.

This approach transforms the process from a tedious data entry exercise to a data review and correction exercise. Every time you agree with a prediction or correct a value, the model learns and improves its future predictions. Over time, the confidence in these predictions increases

The result? More accurate data with less manual effort.

This efficiency saves you valuable time and effort, and feeds directly into an AI-driven data and process improvement loop.

The AI-Driven Data and Process Improvement Loop

An AI-driven data and process improvement loop is essential for ongoing digital business transformation.

This loop begins with using AI to enhance process automation, which in turn improves initial data quality. Better data quality leads to more accurate AI models and recommendations – which means you’ve created a continuous cycle of improvement.

Why is this loop so critical? As data quality improves, AI models become more reliable, leading to better business decisions and processes. High-quality data allows AI (including GenAI) to provide more accurate predictions and decision-making insights, which in turn drives bigger improvements in your business operations. Think greater efficiency, agility, scalability, and beyond.

This continuous improvement loop is at the heart of AI-driven digital business transformation.

Empowering Your Business with AI-Ready Data

Digital business transformation is a journey that requires a strategic focus on AI and data readiness. By ensuring your data is AI-ready and leveraging AI to automate and enhance your workflows, you can make great strides toward achieving your top transformation priorities.

If you’re ready for AI-driven digital transformation, it all starts with your data.  Sign up for the Automate Studio Free Trial today.