Simple but effective AI use cases for CAS

Practitioners who provide client advisory services (CAS) can address staffing pressures and rising client expectations through the implementation of artificial intelligence (AI) tools.

Using AI tools strategically to automate tasks can streamline workflows and free up capacity, allowing CAS firms to shift toward higher-value services, introduce new service models, and prioritize long-term, sustainable growth.

As demand for CAS services grows, firms can distinguish themselves by stepping up their offerings. Most CAS practices started with transactional accounting services, such as accounts payable or receivable, payroll, and cash management, which give a historical view of a company’s performance. Stepping up to higher-value services means providing business insights that are forward-looking and help clients make better business decisions. (For more information see, “Deliver Business Insights With CAS 2.0,” JofA, Nov. 1, 2022.)

One of the most overlooked aspects of AI tools for CAS is how simple tweaks can make a huge difference on the bottom line and profitability of a client engagement. Their use can provide a means to offering competitive pricing even if you do not have a global team or offshore options in your practice.

It can be tempting to jump straight into a comprehensive, transformative project, but implementation of AI automation should be well thought through to avoid common pitfalls.

CAS leaders should ask, “What are the areas causing my team the most time and effort, and which of those can be automated?” Opportunities for meaningful change are often smaller, more approachable projects, such as streamlining manual spreadsheet processes or automating business processes.

UNLOCK QUICK AI WINS IN 6 STEPS

The key to unlocking quick AI wins is understanding that even small improvements can impact your ability to serve clients and free up staff to do more interesting or value-adding work. Implementation is much easier with a stepwise, strategic approach that often starts with industry-based teams.

Consider this simplified, six-step road map:

Step 1: Conduct a workflow audit

Map core processes across your client life cycle, as well as across each of your top 10 clients. Highlight repetitive or highly manual tasks, data bottlenecks, and human error risks.

Step 2: Identify AI-embedded tools you already own

Most firms have access to Microsoft 365, Google Workspace, cloud accounting platforms, and/or collaboration software with embedded AI capabilities. Start by optimizing your firm’s existing systems, determining whether your go-to solutions are well suited, and assessing gaps where additional tools may be required.

Using AI in today’s business environment includes the use of powerful analytics tools such as Power BI or AI-enabled Excel add-ins (such as Tableau or Datarails). It’s likely that AI soon will be embedded in all of a firm’s integrated solutions, just as it has already become a natural AI extension of everyday tools.

Step 3: Before jumping in, define and pilot short-term success

Use the data from Step 1 to define the short-term problem. Then, assess the ability of the existing tools from Step 2 to solve those issues. Flag any problems that existing tools cannot solve and define the systems requirements for solving those issues before going to market, discussing with peers and doing research.

Choose one high-friction workflow, such as accounts payable, monthly reporting, or document requests, and run a 30-to-60-day AI automation pilot (taking into consideration timing where there may be seasonality or other factors). Measure time savings and added review time, error reduction, and client feedback before expanding.

When gaps are identified, such as limitations in automation or data synchronization, firms can explore AI-native applications or third-party tools designed to extend their existing tech stack’s capabilities. For example, while most enterprise resource planning systems that include a general ledger allow memorization and auto-posting of journal entries, calculating those entries dynamically based on multiple variables often requires an external AI solution. Similarly, data aggregation tools can bridge systems that lack prebuilt integrations, enabling more seamless workflows and unlocking additional automation potential.

Step 4: Involve your team

Operational success depends on the engagement and trust of CAS team members. Provide quick-reference guides and host short demos that focus on outcomes over features. Identify initiative and system champions while recognizing that not everyone has to be a leader to be a valuable part of the team.

Allow for check-ins, change management, and ongoing support to help alleviate any concern or resistance.

Step 5: Build a realistic strategy, tailored to your clients and industry

The gold standard that most businesses are trying to achieve is a real-time close — meaning perpetually updated and “closed” books. However, it’s crucial to recognize that once the AI journey begins, it can still take time before real-time close is achieved.

While keeping the big-picture goal in mind, identify the interim steps to get there based on a deep understanding of your clients and industry-specific challenges. For example, consider these priorities for different industries:

• Restaurants: Point-of-sale (POS) system integration, inventory automation, and real-time reporting.
• Professional services: Client invoicing, receivables, revenue recognition, and reconciliation.
• Renewable energy: Proper allocation of funds, expense recognition, and amortization.
• Real estate: Rent roll and common area maintenance reconciliations.
• Not–for–profits: Grant tracking, proper expense allocations, and compliance reporting.

Step 6: Rethink ROI

Don’t limit ROI to the cost reductions associated with the implementation of AI tools. Consider the costs of applying the next-best alternative to AI and the benefits gained if you did. Measure value with team capacity reclaimed, staff satisfaction, client retention, and advisory service expansion.

EXAMPLE AI USE CASES AND TOOLS

Many simple improvements can produce significant ROI without breaking the bank, by reducing time spent on error-prone manual tasks, improving data integrity and profit margins, and freeing up staff to focus on advisory conversations.

To implement high-impact uses of AI tools effectively, start with these six categories:

• Client onboarding.
• Spend and expense management.
• Bookkeeping.
• Reporting.
• Business insights.
• Client communication.

The following are examples of how to use AI tools to address common CAS challenges. Specific tool names are bolded.

Client onboarding: Automated document requests and intake

Challenge: Client onboarding often suffers from inconsistent data collection and inefficient communication, delaying service delivery.

AI tools: Implement intelligent forms that allow personalized data collection and automated workflows to help interpret client uploads, track crucial onboarding tasks, and flag missing information. Route responses through tools such as FloQast, Microsoft Power Automate, or Microsoft Copilot.

AI wins: Faster onboarding, reduced email volume, improved client documentation, and cleaner data from the outset.

Spend and expense management: Invoice processing and payment scheduling

Challenge: Manual entry of vendor invoices and payment coordination create bottlenecks for CAS teams, especially given vast differences in workflows across clients, industries, and systems.

AI tools: Use AI-enabled accounts payable solutions (e.g., BILL, Tipalti, or Ramp) to extract invoice details from PDFs and emails, categorize expenses automatically, match against purchase orders, enable multiple levels of approval, and schedule payments based on cash flow predictions.

AI wins: Significantly reduced manual workload, fewer errors, and better visibility into short-term obligations. Better categorization of expenses may generate new advisory opportunities.

Bookkeeping: Smart transaction categorization

Challenge: Staff spend hours categorizing transactions from banks and credit cards, especially when handling clients across different industries, geographies, and structures.

AI tools: Leverage AI-enabled bookkeeping platforms (e.g., Keeper, Truewind, or Botkeeper) or third-party data aggregation tools (e.g., Synder, DataBlend, or A2X) to learn client-specific categorization rules based on historical, global, and localized data. These tools continuously improve with user feedback, provide clear indications on confidence levels, offer predefined automation workflows, and enable teams to focus on identifying, analyzing, and managing unexpected anomalies in workflows and reviewing new or unusual transactions.

AI wins: Reduced manual work and greater accuracy in categorization, especially for high-volume clients.

Reporting: Auto-generated monthly reporting with commentary

Challenge: CAS teams often compile monthly financial statements manually, which means client-specific commentary is added late in the cycle.

AI tools: Use AI document-generation tools (e.g., Reach Reporting, Qvinci, or Spotlight Reporting) to automate reporting packages and draft tailored commentary based on variances, trends, and KPIs. These tools can pull from general ledger data, trend lines, and prior reports to recommend insights or flag anomalies.

AI wins: Faster turnaround times and higher-quality reporting that make it easier to scale across clients. Some of the insights could be passed on to executive-level teams to create more value for clients and generate more practice revenue.

Business insights: Scenario modeling with AI-assisted analysis

Challenge: Clients want insights embedded into core deliverables, not just numbers, but analysis and modeling remain time-consuming for most CAS professionals.

AI tools: Implement AI-enhanced analysis tools (e.g., Datarails, Kyriba, or Nectari) and automation add-ons that can run scenario analysis with simple input changes, explain outcomes, and recommend actions based on financial results within comprehensive deliverable reports and dashboards.

AI wins: CAS professionals can offer guidance with less prep time, enhancing their role as strategic business advisers. Real-time, insight-rich dashboards offer upgraded client deliverables. Also, the process, prompts, and templates for the inputs and meeting agendas become standardized, freeing the team to do more with the time, data, and initial insights created by the AI tools.

Client communication: Smart email triage and drafting

Challenge: Email overflow slows down CAS teams and distracts from higher-value work.

AI tools: Use AI inbox assistants (e.g., Gmail Smart Reply or Microsoft Copilot) to prioritize messages, suggest responses, and draft templated replies based on tone and context. These tools can even detect time-sensitive requests or client frustration and flag them for faster resolution.

AI wins: Reduced communication lag and fewer missed follow-ups, ultimately leading to improved customer service and retention while still freeing staff attention.

BENEFITS OF AI AUTOMATION

AI tools are enablers, not just an expense line. In fact, they can become the launch pad for a transformation of CAS culture and profitability. By harnessing the momentum and calculating the ROI of early achievements, you can bring to the table greater trust, visibility, and excitement around pursuing more complex AI initiatives to get closer to real-time accounting and reporting and offer robust insights for clients.

For CAS practices navigating competitive pricing models and labor shortages, AI-powered workflows can offer these benefits:

• Reducing labor dependency: Like cloud-based automation before, AI tools are the most current technological advancement to take on transactional tasks that traditionally consumed staff bandwidth, such as reconciliations, categorization, and document review. By reassigning team members to client-facing, higher-value activities, firms can improve accuracy through AI optimization while also reducing costs per engagement.
• Compressing engagement timelines: Tasks like report generation, variance analysis, and cash flow forecasting — which once took hours — can now be completed more quickly with AI automation. These faster, enhanced, AI-powered service delivery models promise to enable firms to service more clients using the same staff footprint, increasing revenue per employee and allowing for volume-based growth without compromising quality.
• Enabling tiered service models: AI tools allow firms to standardize core deliverables, including by industry and even by client, while offering custom advisory layers above them. This supports premium-pricing models without proportionally increasing labor costs. For example, core monthly reports can be automated, while strategic guidance is layered in by a combination of AI insights and advisory staff, often commanding higher fees based on the value delivered rather than the time incurred.
• Improving realization rates: AI automation can reduce administrative tasks that either go unbilled or are difficult to capture accurately. By automating tasks that typically fall outside client expectations, but still require manual time, firms can recover hours that might otherwise erode realization.
• Lowering error rates and rework costs: Fewer mistakes means fewer write-offs. When implemented correctly, AI-enabled processes reduce the risk of manual error in categorization, data entry, and reporting. This helps to protect margins and strengthen client trust, leading to better retention and referral growth, which positively impacts the practice’s stability and growth.

COMMON PITFALLS WITH AI ADOPTION

Even with simple goals, implementation missteps can derail progress. Watch out for these three risks:

• Over–automating without oversight: Automation should reduce effort, not introduce blind spots. Implement review steps to catch categorization errors or misunderstood context — especially in client-facing content. Don’t underestimate the value of assigning team members who are familiar with digital environments to help you reimagine the use of technology in your firm to avoid these types of issues.
• Ignoring the human element: AI is not a replacement for professional judgment. As important as it is to continue to build client relationships, interpret nuance, and make final calls when context matters, the practitioner and the firm also own the responsibility for the details, which makes it crucial to understand and stand behind the numbers prior to providing them to clients.
• Neglecting data governance and security: AI tools require access to sensitive financial data to work effectively, making robust data governance essential. A holistic data strategy, one that safeguards the firm, its clients, and the integrity of decision-making, is foundational. This strategy should encompass upfront due diligence, ongoing risk reviews, and clearly defined controls around permission settings, data encryption, and compliance with firmwide policies upfront. Importantly, a well-structured data strategy not only protects but empowers; it enables AI tools to unlock previously untapped data sources, fostering synergy across departments (such as CAS) and enhancing the firm’s overall analytical capabilities.

FUTURE WINS OF AI ADOPTION

CAS teams are in a prime position to lead the charge toward smarter business advisory. By strategically leveraging AI tools and optimizing operational structures, CAS teams can unlock strategic capacity that fuels margin expansion. Firms that deploy AI tools judiciously — focusing on simplicity and alignment with CAS practice strategy and goals — stand to transform not just how they work, but how they grow, positioning themselves to outperform their peers and experience higher quality, profitability, and client retention and satisfaction.

The future of CAS will center on guiding more informed, timely business decisions and outcomes for both the firm and its clients through AI-powered workflows.

It is by automating smarter, not harder, that CAS teams can build stronger relationships, support sustainable growth, and redefine their roles as true advisers to clients, helping them achieve both short- and long-term wins.

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About the authors

Kane Polakoff is a partner and CAS practice leader at CohnReznick in Detroit. Michelle Voyer, CPA, is a CAS leader with CohnReznick in Cumberland, R.I. To comment on this article or to suggest an idea for another article, contact Jeff Drew at Jeff.Drew@aicpa-cima.com.

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