AI offers tremendous advantages, but it demands more than simply layering it into your business.

AI is driven by data, which is derived from processes. This makes process optimization paramount in the move to AI adoption: If you don’t have the right processes, you don’t have the right data, and you can’t get full value from your AI. But, conversely, well-implemented AI can also help optimize processes, toward greater efficiency and value. 

Read on for top opportunities for AI-driven process improvements, and learn the key steps to harnessing this potential effectively.

Process/operational challenges

Process-related challenges are common across business operations.

Finance today can be a discipline of “doing and building,” at the expense of analyzing financial data. Manipulating spreadsheets, manual AP processing, and other time-intensive processes mire talent in the data, limiting their time for more value-added activities.

Procurement faces increased costs and supply chain disruptions due to inefficient workflows and manual supply planning and material resource planning. Inventory management can suffer from excessive touches and storage inefficiencies, which can also impact e-commerce availability.

Marketing and sales are vital in connecting past performance with future goals, but their planning may be hindered by subpar forecasting processes that rely on historical data. Additionally, processes that limit the collection of customer information, or leave it in silos, can impede the ability to provide a robust buyer experience.

AI-driven process opportunities

When added to optimized processes, AI promises to be transformative, extracting more value than ever before with enhanced decision-making and previously unexplored opportunities to expand.

Direct integration, enhanced answers 

AI applications can integrate directly with modern data platforms. This enables working in a new, user-centric way built around using “real language”: You can ask questions of your data, get synthesized answers, and self-service analytics without macros or coding. Compared to current processes without AI, this can free up significant time spent searching for information.

A finer approach to finance

A combination of automation and AI can eliminate labor-intensive processes, freeing up time and attention for value-added analysis. Consider accounts payable and the complex three-way match process. AI can cut the time it takes to compare purchase orders, goods receipts, and invoices – all while recognizing anomalies and raising red flags. From ERPs like NetSuite to finance automation solutions like Tipalti, AI is built into many of today’s financial technology solutions.

A greater purchase for procurement

AI offers procurement an expanded and more exacting perspective. With clearly defined attributes and processes that access the right data, AI can synthesize information and make recommendations that help improve purchase decisions – selecting from multiple vendors, finding better pricing, accurately evaluating vendor performance. AI can also provide greater insight into possible fraud, such as identifying orders that don’t meet authorization criteria. And in the context of receiving and picking First In, First Out (FIFO) inventory, AI can streamline the retrieval process and help minimize touches by predicting order demand and timing.

Broader markets, better sales

Part of the appeal of AI is its ability to reduce costs, but there is also potential for increased revenues through AI-enhanced marketing and sales. More expansive and accurate forecasting leads to more impactful marketing, whether the focus is expanding existing markets or building new or not-yet-optimized markets. AI can pull in econometric, affluency, and CPI data to predict marketing and pricing model performance both in e-commerce and across zip codes.

Elevate your process readiness for AI

Making the most of AI hinges on having standardized, digitized, and optimized processes. Here’s how to assess and enhance your readiness.

Define your use cases: What do you want AI to help you do? 

As with data readiness, process readiness should be driven by the end goal – what challenges or opportunities you want AI to help you solve for, and what you need to do to get there. This typically involves two central options, 1) reduce costs or 2) increase revenue; everything else is a derivative of those functions. 

Evaluate your current state

Once use cases are identified, ascertain the current state of your technological maturity – people, processes, technology, and data in aggregate – to determine whether you’re ready to support those use cases. This assessment approach includes identifying gaps or opportunities to modernize your processes, leveraging technology to capture the necessary data for executing your AI strategy. What gaps do you have; what do you need to change? This may include: 

• Moving any paper-bound processes into a digital environment
• Replacing potentially siloed or multistep processes – e.g., those conducted via spreadsheets or email – into a more unified solution that allows for data aggregation and smoother approvals
• Putting systems in place to capture data about customers, e.g., their buying habits, buying trends, and demographics
• Adding sensors onto machinery to capture operations data
• Researching industry standards to implement best-practice systems that keep up with – or outpace – the competition

Check for standardization and synchronization

AI tools work from the information available to them. Thus, it’s critical to make sure that all team members are using the same technologies and processes – the modern way of working – so that AI can effectively analyze your entire ecosystem. It’s not enough for each department to be optimized in the technology solutions that will support their own AI needs; those technologies must all align and communicate with one another to support seamless data flow and availability. 

• Note: This may especially be an issue for organizations that have expanded through M&A, who may have not yet re-evaluated to standardize and unify their operations. Departments may be running different processes to deliver the same end product. 

Optimize processes, then proceed forward

Much like a clearly defined and refined process, maximizing the potential of AI requires taking things one step at a time. Before you take the first step on your AI journey, make sure your processes are optimized to create a robust foundation. A thorough evaluation of goals and gaps – identifying the issues to address and the processes that can help resolve them – can serve as a solid starting point from which to build your road map for the future.