The Autonomous Enterprise | Deloitte US has been saved
The rising adoption of AI
The rapid evolution of artificial intelligence (AI) has brought a wave of profound transformation for business operations. As industries stand at the cusp of the Generative AI (GenAI) revolution, the concept of an autonomous enterprise is no longer futuristic—it’s imminent. But what does this shift entail, and what are the potential challenges and solutions? Let’s dive into the journey of becoming an autonomous enterprise.
AI is rapidly becoming a central topic in business discussions, with its global market value projected to grow at an impressive 35% compound annual growth rate (CAGR),¹ reaching $1339.1 billion by 2030.¹
Likewise, the market for machine learning (ML) and AI-enabled platforms is anticipated to expand significantly in the coming years. Leading technology executives highlight AI as their primary budget priority for the coming year, emphasizing its significance across all industry sectors.²
However, despite this enthusiasm, companies may face several challenges in fully adopting AI:
- Lack of C-suite knowledge
- Complex IT infrastructures
- Absence of AI governance
In the realm of adoption, most organizations are primarily relying on off-the-shelf AI-enabled solutions. However, a growing trend is expected toward industry-specific applications and customized solutions, both private and open-source. These solutions will empower AI to strengthen human capabilities, boost cognitive efficiency, redefine and reimagine work processes, and introduce the concept of virtual agents. This progress is anticipated to drive a profound cultural transformation within organizations.³
Shifting to an autonomous enterprise: Augmented AI automation
For business executives, the primary tasks remain: scale operations, boost productivity and refine processes. In today's economic landscape, the dual pressures of reducing costs and enhancing revenue continue to persist. This necessitates strategies that integrate financial and technological advancements seamlessly.
From the workforce management perspective, top talent is increasingly disinterested in repetitive tasks. The modern workforce craves engagement in innovative solutions. Meanwhile, organizations operate with multiple enterprise resource planning (ERP) systems and aim to seamlessly maintain their processes. The ticket to enhancing these processes lies in adopting AI-fueled microsolutions that automate existing workflows.
Let’s explore specific scenarios where these microsolutions can considerably enrich efficiency.
AI-driven microsolutions: Set for growth
- Error management in finance transactions
Consider the immense transaction volumes organizations handle daily. For instance, imagine a scenario where an organization processes between 5 million and 7 million transactions each day through its front office. The impact of any errors is noticeable even when assuming a failure rate of just 1% at the back end. Consequently, the organization spends about six days each month rectifying these errors. This extensive error-resolution period can lead to serious delays in decision-making.
The solution: Implement an autonomous accounting microsolution. Training a model to predict and remediate missing data in transactions can eliminate the need for manual intervention. The microsolution utilizes AI and ML capabilities to drastically minimize the time spent on labor-intensive month-end activities, facilitating a faster and more automated financial close.
The solution can improve data monitoring and correction, automate data updates between systems and the validation of business rules, and help to identify and create new master data—streamlining operations and speeding up decision-making.
- Invoice processing within source-to-pay
In many shared service centers, a sizeable portion of staff is dedicated to processing invoices and ensuring they are approved by the correct personnel. For example, consider the accounts payable (AP) department, which handles a vast influx of invoices. Depending on the size of the center's client, this can mean managing millions of documents each month, with thousands arriving daily. Typically, these documents are scanned to create PDF files. However, these PDFs often lack crucial information needed for processing.
With these documents come key questions: Which client company is the invoice for? What business unit or profit center does it pertain to? What types of charges are involved, and what is the associated general ledger (GL) coding? Depending on the stage of a company’s digital transformation, these are the types of data that AP processors must validate or input manually. In a typical AP shared service center, various data elements require human decision-making and/or validation.
Imagine a scenario where more than 300 shared service processors manage large volumes of invoices, even with a packaged solution designed for invoice processing. How can we simplify this effort?
The solution: Adopt an autonomous invoice management microsolution. This solution can efficiently read and analyze vast volumes of PDF data, pinpoint the relevant business units, cost centers, GL codes and identify any missing data related to invoices. A strong advantage of such microsolutions is their ability to self-learn and fine-tune over time. Results have demonstrated that these microsolutions can help organizations lower invoice management costs by up to 30%, showcasing their potential to substantially transform and streamline AP processes.⁴
- Reconciliation errors and exceptions in journal entries
From a finance perspective, consider some common challenges cash management teams face, despite implementing robust ERP systems:
Due to having multiple line items, discrepancies exist between bank statements and open accounts receivable.
Another big challenge within the finance function involves managing exceptions in accounting journal entry transactions. These exceptions, often arising from data discrepancies, are typically transferred from front office to back-end solutions. Such differences can lead to the blocking of cost centers and profit centers, complicating financial management.
The solution: An AI-enabled autonomous finance microsolution can predictively identify and categorize the types of exceptions encountered. Over time, it can autonomously correct these transaction postings, helping to improve data monitoring and correction, integrate data updates between systems, facilitate the development of new master data, and automate the validation of business rules—cutting down on manual intervention while upping efficiency.
Deloitte is ready to assist your AI journey with AIOPS.D™
As businesses trek toward autonomous enterprises, it’s growing clear that AI holds metamorphic potential across business functions. Adopting microsolutions presents a compelling alternative to traditional packaged solutions. By focusing on these flexible technologies, businesses can help themselves boost efficiency, decrease manual workloads and accelerate their pipelines—and become fully autonomous entities. Welcoming these innovations is not just about keeping up with technology, it's about actively designing a future where an enterprise peaks in efficiency and adaptability.
With Deloitte’s AIOPS.D™, an AI-powered microsolutions platform, you can transition from automated to autonomous. AIOPS.D™ offers a diverse and expanding portfolio of subscription-based solutions, adaptable to meet the needs of any organization across various industries and ERP functions.
Interested in learning more? Visit our AIOPS.D website and let us know how we can support you on your journey toward becoming an autonomous enterprise.
Author:
 |
Karthik Sukumar |
Endnotes:
¹ MarketsandMarkets, Artificial intelligence (AI) market, May 2024.
² Ophelia Anwah and Eric Rosenbaum, “A.I. is now the biggest spend for nearly 50% of top tech executives across the economy: CNBC survey,” CNBC, 23 June 2023.
³ Deborshi Dutt et al., Now decides next: Insights from the leading edge of Generative AI adoption, Deloitte, January 2024.
⁴ Karthik Sukumar and Abdi Goodarzi, "AIOPS.D™ Autonomous Source to Pay,” Deloitte, 4 November 2024.
