Finance Transformation: Unleashing the Power of AI and Analytics in Manufacturing

In today's rapidly evolving world, manufacturing organizations are seeking innovative ways to stay ahead of the competition. One area where significant transformation is taking place is in the finance departments of these organizations. With the advent of artificial intelligence (AI), manufacturing finance is undergoing a remarkable revolution, enabling organizations to make more informed decisions, improve operational efficiency, and drive sustainable growth. Industry trends are contributing to a "new normal," requiring finance to enhance the delivery of decision-supporting insights into the business. The global AI market size is expected to grow at a compound annual growth rate (CAGR) of 36.8% to reach $1,345.2 billion by 2030 from $150.2 billion in 2023 per a research report by MarketsandMarkets[1]. This will be fuelled by advancements in generative models, traction in edgeAI with decentralized AI processing and decision-making, and explainable AI enhancing the interpretability and transparency of AI models.

According to Deloitte's CFO Signals TM 1Q 2023 report [2], more than half of CFOs pointed to inadequate technologies/systems, immature capabilities, and lack of experienced talent as their greatest roadblocks in driving data to insights. A vast majority of surveyed CFOs have taken actions to address those challenges, such as investing in new systems and automation and upgrading existing systems, implementing talent and organizational changes, streamlining data structures, and evaluating processes and controls.

Common Challenges in Finance

Many finance organizations are challenged to make the most of their FP&A processes with limited resources, making it critical to drive efficiency and organizational alignment through leading practice processes and supporting technology infrastructures. While the vast majority of surveyed CFOs in Deloitte's CFO Signals TM 1Q 2023 report [2], are focused on planning for a mild recession in 2023, many are simultaneously taking actions to prepare for a recovery or rebound. The top three actions CFOs are taking are investing in growth, sales, customers, and new markets; controlling costs/increasing operational efficiency; and building inventory and/or production capacity to meet demand.

One of the top hurdles that almost all manufacturing finance teams face is the failure to develop an end-to-end FP&A vision that aligns financial planning to broader business strategy through purposeful planning cycles. This can lead to inefficient cycle times and disconnected forecasting assumptions. In addition, reliance on non-standardized and disconnected processes can create inefficient planning cycles that could hinder the ability to efficiently and consistently plan and measure business performance.

Even today, a vast majority of finance teams still rely on offline spreadsheets and fragmented planning tools that severely constrain analytical agility, and they tend to fail to support increasingly complex planning & reporting environments, creating bottlenecks in deriving business insights. Lack of global data standards, including common data model, master data governance, and data catalog can result in significant efforts to aggregate and validate planning and reporting information and insights.

However, the challenges are not just related to strategy and technology. Modernized technologies can only drive business outcomes if they are backed by the right operating model with well-defined roles and responsibilities. Lack of ownership of the process, responsibilities, and financial commitments can lead to slower, more ineffective decision-making with decentralized FP&A teams that operate without the help of a COE, contributing to process fragmentation and increasing the cost of operations.

What is Artificial Intelligence?

Artificial Intelligence mimics human intelligence by combining large amounts of structured and unstructured data, including text, images, voice, and others using algorithms that learn automatically from patterns and features in the data to take specific actions. It enables machines with human-like abilities to sense, perceive, learn, reason, and generate the best responses to impulses from the external environment. Per a Statisca report [3], AI is expected to contribute a significant 21% net increase to the United States' GDP by 2030, showcasing its impact on economic growth.

Machine learning, intelligent automation, speech recognition, Natural Language Generation (NLG), and computer vision form the crux of the key AI capabilities. ML algorithms can detect patterns from vast volumes of data and interpret their meaning, automation technologies like RPA created rules-based automation of routine tasks, combined with analysis of unstructured data and capabilities that mimic human learning and decision-making.

Speech recognition helps to accurately transcribe and understand human speech, and it typically combines with Natural Language Processing (NLP) to allow for the understanding and interpretation of human speech. Natural language generation (NLG) is an automation technology that generates narratives and commentary from structured data and can be applied for commentary to accompany a monthly financial reporting package for executive audiences. Lastly, computer vision extends the analytical power of artificial intelligence to the vast quantities of visual data generated in the world today; it includes optical character recognition (OCR), and analysis of video and image for object detection, anomaly detection, and motion analysis.

What can Artificial Intelligence do for Finance?

CFOs today are facing a multitude of challenges, including maintaining the quality and reliability of large financial datasets originating from a variety of sources, maximizing forecasting accuracy and additive analysis of financial data sets, reducing the finance organization's cost footprint while maintaining customer satisfaction and driving top-line growth, and driving regulatory compliance, audit, and quality assurance. Digital technologies are helping to reshape how finance does business— lowering operating costs, effort, and risk while increasing the analytic value and transparency of financial data.

By employing AI technologies, finance departments in manufacturing industries can automate profiling, remediation, and integration activities to expedite and optimize data cleanup exercises, optimize financial planning, insight generation, and algorithmic model identification, streamline high-volume, repetitive tasks, and enhance trust and transparency of financial data.

AI solutions are critical in cost reduction and speed to execution as they can automate business processes, tasks, and interactions, increasing efficiency, ensuring predictability, and minimizing latency. These solutions are also improving understanding and decision-making by deciphering patterns, connecting the dots, and predicting outcomes from increasingly complex data sources. AI helps to fuel innovation in finance, by generating deeper insights on "where to play" and "how to win", enabling the creation of new products, market opportunities, and business models. Most importantly, organizations are seeing increased application of AI to reduce risks, including fraud, waste, abuse, and cyber intrusion. These are critical in fortifying trust amongst stakeholders and enhancing trust in customers.

Finance AI Opportunities

AI can serve as a powerful tool in helping CFOs address complex challenges, with opportunities for CFO being most prevalent in transactional finance, FP&A, and Controllership. Businesses' [4] need for deeper and timelier insights from data moved to the forefront during the pandemic, and CFOs continue to grapple with challenges in getting the right data and translating it into insights for decision-making as they plan for the rest of 2023 and 2024. Their three greatest challenges: Inadequate technologies and systems and immature capabilities, cited by 64% and 62% of CFOs [2], respectively, and lack of experienced talents such as data scientists and business analysts, cited by 58% of CFOs [2],. The most widely acclaimed applications of AI in finance are in Financial Planning, Finance Operations, and Decision Support.

For Financial Planning, AI plays a key role to shift from spreadsheet models and intuition to automated, analytics-based models that integrate cloud planning systems with data lakes to address combined internal and external data needs and ensure consistent data categories and federated aggregation processes from the corporate core.

In Finance Operations, AI is crucial in creating hierarchies that can handle evolving management, financial, and regulatory reporting, streamlining workflows and automating reconciliations across sources to increase journal entry traceability and audit responsiveness, and leveraging advanced analytics using machine learning for exception and risk identification.

AI can also clarify information needs across business units, geographies, and source systems, unlock insights using modern cloud-based data-staging environments so data is accessible anywhere it resides, including the ERP, and create interactive reports that let users drill down through multiple layers of information to support decision-making.

Gartner Inc.[5] predicted in 2023 the three ways autonomous technologies will impact the FP&A and Controllership functions in Finance.

By 2025, 70% of organizations will use data-lineage-enabling technologies such as graph analytics, machine learning (ML), artificial intelligence (AI), and blockchain as critical components of their semantic modeling.

By 2027, 90% of descriptive ("what happened") and diagnostic ("how or why it happened") analytics in finance will be fully automated.

By 2028, 50% of organizations will have replaced time-consuming bottom-up forecasting approaches with AI, resulting in autonomous operational, demand, and other types of planning.

Transforming the FP&A Organization

A holistic approach to FP&A strategy is required for manufacturing organizations to unlock the full potential of their finance organization. It starts with an end-to-end framework to define how organizations strategically approach, execute, and deliver FP&A services to the business, creating an integrated solution that connects processes, capabilities, and assumptions across the business. As companies plan for the remainder of 2023 and 2024, CFOs[2] suggest several improvements for their companies to enhance decision-making: the most frequently mentioned improvements are implementing digital technologies, AI, and automation, and improving forecasting, scenario planning, and consistency in measuring key performance indicators.

First and foremost, leaders need to create an end-to-end FP&A ecosystem that links strategic planning, budgeting, and forecasting processes. This will help to maximize organizational involvement to drive cohesion and accountability. The next focus is on connecting assumptions and business drivers across financial statements by leveraging digital capabilities and advanced analytics to deliver financial projections quickly and more accurately.

Investments in data, analytics, and AI can ensure optimized business adoption of analytics solutions that provide the right insights at the right time by leveraging digital platforms that enable the self-discovery of insights for finance and business partners. These digital investments can however only be successful if there is an established foundational technology infrastructure to facilitate integrated and connected planning to deliver near real-time data and financials to support planning and reporting activities.

A modernized delivery model will help ensure that centralized standards and transactional processes are in place to create capacity for more value-added activities, and position finance resources where they are most valuable to the organization.

Integrated Technology & Data Platform

Establishing a foundational technology infrastructure enables the FP&A organization to operate in a connected planning environment, driving more accurate forecasts and generating new business insights to enhance organizational decision-making. Surveyed CFOs[2] most frequently cite investing in new systems and automation and upgrading existing systems as the most effective step they've taken to improve the data and insights their finance teams provide the business.

A "north star" data infrastructure provisions advanced planning by creating a technology infrastructure that can support processes that rely on warehouses of historical company and market data, statistical algorithms chosen by experienced data scientists, and modern computing capabilities that make collecting, storing, and analyzing data fast and affordable.

Modernized cloud-based data platforms are now allowing manufacturing organizations to store and manage data and applications, enhancing scalability, driving speed to execution, and appropriately aligning support costs with overall usage. The foundational capabilities are critical for the success of cognitive systems, including RPA, data analytics, and machine learning capabilities to deploy technologies that intelligently extract relationships from data, determine the correct application of those insights, and learn over time by interacting with data and users.

Critical Success Factors

Transformations require a significant cultural shift that is often underestimated. Strong executive sponsorship helps to garner universal buy-in, whereas lacking transition management often results in abandonment after significant investments have been made. Executive sponsorship is critical for setting aggressive yet realistic goals and providing accountability for delivering along an agreed-upon timeline.

Too often, organizations attempt to effect change within the four walls of Finance. Understanding organizational nuances, finance systems, and process details is critical. Transformations require broader business integration and a strong partnership between Finance, IT, and HR. It's crucial to blend cross-functional perspectives to determine the overall "characteristics of the ideal end state" for FP&A.

Leading finance organizations routinely leverage best practices and lessons learned by others to influence their strategy and future-state design. The vision should not be hindered by the inertia of current practices and the complications of organizational change. Lastly, organizations need to embrace digital. Maintaining a "we can do it in Excel" mentality – when creating a vision for the future of FP&A, considering the latest technology and digital enablers, which can greatly enhance the speed, quality, and insights of planning and reporting, as well as promote global process and data standards. Layering in next-gen capabilities also enables a more efficient, cost-effective, approach to business partnering.

Employees often resist change, especially those rooted in established processes. A strong change management plan can guide employees through the upcoming disruption in their lives. Consistent, open, and clear communication between management and employees will ensure a smooth transition and generate trust.

Starting the AI journey

Manufacturing organizations can enable their AI transformation by aligning stakeholders, prioritizing AI opportunities, demonstrating value through initial use cases, and scaling capabilities through a well-defined operating model. Aligning executive leadership on an enterprise-wide strategy is the first step to building ownership and alignment with key stakeholders from IT and functional teams and allows for articulation of why and how AI will drive value across the business. Scaling AI requires establishing an operating model with well-defined governance and talent models. Organizations also need to enable robust processes for the identification, development, deployment, and operation of AI solutions.

Identifying and prioritizing AI opportunities by reviewing the value chain for AI suitability will help to establish a use case prioritization framework that prioritizes opportunities based on data availability, solution readiness, and overall business value. This in turn can lead to the successful development of prototypes that prove the value of AI investments by delivering quick wins, identifying additional opportunities, and building an integrated team of functional and technical resources that work towards a singular goal.

About the author:

Rajarshi Ghose Dastidar is a manager of Artificial Intelligence & Data at Deloitte Consulting LLP. He has over 10 years of professional experience in management consulting, with proven expertise in managing large-scale digital transformation and analytics modernization programs. Rajarshi works with global manufacturing and consumer organisations in strategizing, designing, and delivering analytics solutions for their operations, finance, and supply chain business functions.

Email: rghosedastidar@deloitte.com

Citations:

[1] MarketsandMarkets Research Report on AI Global Forecast for 2023

[2] Deloitte CFO Signals™: Quarterly CFO survey

[3] Statista Report: Incremental GDP increase based on impact of artificial intelligence by economic driver in the United States by 2030

[4] Deloitte Perspectives: Reinventing FP&A for the pandemic and beyond CFO Insights

[5] Gartner Predicts Three Ways Autonomous Technologies Will Impact the FP&A and Controller Functions in Finance

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