Mastering data potential: Advanced AI and big wins

No longer a moonshot experiment or the preserve of tech enthusiasts, Artificial Intelligence (AI) has evolved into a transformative force driving business innovation across industries. It is now integral to helping companies make data-driven decisions and stay competitive.

Yet, while more and more organisations embrace AI, few have mastered its full potential. This article outlines the strategic steps to scale AI-driven business growth and showcases how firms like Withum (HLB USA) have translated data into decisive action.

From data dashboards to AI-driven business

A typical AI journey starts with data visualisation, when businesses turn raw figures into easily digestible information in visual format. Tools like tables, charts, scatter plots, histograms, heat maps, and tree maps can clearly communicate even the most complex information. By visually representing data, businesses can analyse vast amounts of information and turn those insights into actionable decisions.

While a picture may be worth a thousand words, dashboards only show what has already happened, not what will happen. The true value of data analytics lies in its ability to look ahead. This is where predictive analytics comes in, allowing companies to analyse vast datasets to uncover key insights and, more importantly, forecast future risks and opportunities.

Transitioning from using historical data to understand the past to leveraging that data to predict the future is a significant leap from basic data visualisation. However, the most AI-mature companies don’t stop there. They integrate AI into their core operations, evolving from simply gaining insights to fully automated, AI-driven decision-making.

Logistics companies are using AI, machine learning, and predictive analytics to optimise delivery routes in real-time by analysing factors like traffic, weather, and historical performance, boosting efficiency and enhancing customer satisfaction.

Banks deploy AI models to assess transaction data, identifying patterns and flagging anomalies that may indicate fraud. Manufacturers are reducing downtime through predictive maintenance, leveraging sensor data to anticipate and prevent equipment failures.

There is virtually no area of business untouched by predictive analytics. At this level, AI is more than just a tool—it’s an intelligent engine driving the business forward.

The blueprint for scaling AI and avoiding pitfalls

Although a powerful tool, AI isn’t a magic wand and is certainly not an end goal. Achieving meaningful outcomes requires more than high expectations—it demands clear objectives, strategic planning, and, above all, a solid foundation of high-quality, well-governed data.

Without this groundwork in place, even the most advanced models are unlikely to deliver lasting value. This is often why, despite the promise of AI, an estimated 70-80% of all AI projects fail. As expectations continue to outpace results, a widening gap is emerging between what businesses hope for and what they achieve with AI.

Here’s how companies can move from experimenting with AI to mastering it and avoid common pitfalls along the way.

Business goal alignment

AI is a great tool for making predictions, but only for well-formulated problems. Start with the right questions—AI thrives on clear objectives.

Instead of vaguely wanting to "use AI" for the sake of innovating, define a clear business goal: Do you want to improve the customer retention rate? Optimise the supply chain for cost-efficiency and speed. Boost operational efficiency? Predict potential demand for a new product or service?

The key to success is aligning AI projects with your business goals.

Collaboration between the data science team and business units is crucial. Often siloed and with limited interaction across the broader organisation, data scientists may lack a deep understanding of the company’s strategy and can focus on projects that fail to deliver significant business value.

Data is your goldmine—but only if it is clean

Predictive models rely on clean, relevant, and representative data because AI is only as good as the data it ingests.

Many organisations falter by feeding AI incomplete, siloed, or biased data. As we explored earlier in the series, a strong data governance strategy is essential to avoid these issues. This is where partnering with experts like HLB becomes invaluable, helping organisations establish robust data governance frameworks to ensure their AI projects are built on reliable, well-managed data.

Furthermore, without the proper infrastructure and access to data for managing and deploying AI models, the risk of project failure rises significantly. Understanding the context, source, and collection methods and seeking expert interpretation of the results are crucial.

Build an AI-first culture

Just like AI is no longer a luxury exclusive to tech giants, it is no longer just for data scientists. Upskilling employees to understand AI’s role in decision-making ensures company-wide buy-in.

Today, as the demand for AI expertise is skyrocketing across the economy, many firms struggle to attract top AI talent. Partnering with external AI experts or leveraging AI-as-a-Service models can fast-track implementation.

Think big, start small

A successful AI rollout begins with pilot projects that demonstrate value before expanding across the organisation. These initial pilots start with clear objectives and a focused use case, leveraging high-quality data and testing the model in a controlled environment, with results driving continuous refinement before any full-scale rollout.

However, even after full-scale implementation, AI models require ongoing adaptation as market dynamics evolve and new data emerges. Continuously monitor, adapt, and improve.

Powering automation at scale: How a food manufacturer transformed operations

Let’s explore how predictive analytics can optimise business operations. A leading S&P 500 food manufacturer, with over 14,000 employees and $9 billion in annual sales, sought to maximise its investment in Microsoft 365, particularly its Power Platform tools—Power Automate, Power Apps, and Dataverse.

Despite extensive use of Microsoft tools, the manufacturer faced significant challenges: fragmented workflows, over-reliance on third-party vendors, and insufficient governance around automation.

Withum, a technology-driven advisory and accounting firm within the HLB network, stepped in to consolidate automation efforts, empower employees, and establish a governance framework for long-term success.

To execute this vision, Withum implemented the Microsoft Centre of Excellence (COE) Starter Kit, a governance tool designed for enterprise-wide Power Platform adoption. This framework enabled seamless integration of critical business workflows, defined governance roles to balance control and innovation, and replaced fragmented third-party tools with Power Platform solutions.

A key pillar of the transformation was data integrity. Clean, reliable data was essential to making automation work at scale. As the company transitioned from legacy systems, Withum standardised and centralised data within Dataverse, eliminating inconsistencies and silos.

Power Automate seamlessly integrated with existing business processes, ensuring accurate, real-time data flows and creating a single source of truth for greater visibility and control across operations.

The impact was immediate and far-reaching. The governance framework provided the structure needed to scale automation with confidence. Replacing third-party tools streamlined operations, reducing complexity and costs. Citizen developers gained the tools and guidance to drive automation independently, fostering a culture of innovation. Business processes became more efficient, reliable, and adaptable to future needs.

With a strong foundation in place, the company is now positioned to expand its automation capabilities, optimise workflows, and drive enterprise-wide innovation. For Withum, the success of this initiative reinforced its expertise in digital transformation, demonstrating measurable business impact while strengthening its role as a trusted partner in enterprise AI and automation strategies.

The AI-first future is now

AI is not just about improving processes and enhancing efficiency—it’s about rethinking how businesses operate, make decisions, and compete. Companies that master AI today will dominate their industries tomorrow. Those that hesitate risk being outpaced by more agile, data-driven competitors.

For organisations ready to take the leap, the next step is clear: Build an AI strategy that is focused on tangible business impact. With the right approach, AI doesn’t just optimise businesses—it transforms them.

At HLB, we specialise in helping businesses develop and execute AI strategies that align with their goals, ensuring seamless integration and long-term success. Our Data Analytics and Business Intelligence experts can guide you through every phase, from strategy development to implementation, maximising the full potential of AI for your organisation.

Contact us today to take the next step in your AI journey.