How an AI-Powered CLM Optimizes Financial Ops

August 22, 2024 • Guide • 16 minutes

Today’s financial operations teams face mounting pressure to handle complex contracts with speed, accuracy, and efficiency. While traditional contract lifecycle management (CLM) systems have served their purpose, they often fall short in providing the agility and insights needed in a data-driven environment.

Enter AI-powered CLM — a transformative solution that not only automates routine tasks but also delivers actionable insights to enhance decision-making.

Here, we’ll explore the advantages of integrating AI-powered CLM into financial operations. We will examine how these advanced systems address common pain points, such as manual data extraction and the challenges of accurate budgeting and forecasting.

By the end, you will see why adopting an AI-powered CLM is not just an upgrade but a strategic necessity for financial leaders looking to optimize operations and drive success in a competitive market.

2024’s financial operations landscape

As the role of the CFO continues to expand, the influence of financial leaders on a company’s strategic direction becomes increasingly pronounced. With the global economy in a state of flux, marked by unpredictable geopolitical shifts and evolving regulatory landscapes, financial operations must be agile, data-driven, and resilient.

The year 2024 presents unique challenges and opportunities for financial leaders. With an increasing emphasis on digital transformation, the need for advanced technologies that streamline processes and provide actionable insights has never been more critical.

Manual processes and outdated systems no longer suffice in an environment where speed, accuracy, and transparency are paramount. As a result, the ability to quickly adapt to market changes, manage risks effectively, and make informed decisions is essential for maintaining a strong financial foundation.

Current challenges in financial operations

  • Manual processes and their inefficiencies: Relying on manual workflows leads to delays, increased labor costs, and a higher likelihood of errors. These inefficiencies can hinder a company’s ability to respond swiftly to market changes.
  • Lack of visibility and control over financial data: Without a centralized, real-time view of financial data, CFOs struggle to maintain control and stay in compliance, leading to potential risks and missed opportunities.
  • Time-consuming data extraction and analysis: Extracting and analyzing data manually is not only labor-intensive but also prone to inaccuracies, making it difficult for finance teams to generate timely and reliable insights.

The role of technology in financial ops

  • Evolution of financial technology: Over the years, financial technology has evolved from basic accounting software to sophisticated platforms that integrate various aspects of financial management. This evolution has laid the groundwork for more advanced solutions like AI-powered CLM.
  • The growing importance of AI and automation: As companies face increasing pressure to optimize operations, AI and contract automation have emerged as critical tools. They enable finance teams to streamline workflows, reduce manual intervention, and enhance data-driven decision-making, shaping the future of financial operations.

In 2024 and beyond, AI-powered CLM systems offer a strategic advantage by addressing these pain points head-on, paving the way for more efficient, accurate, and agile financial operations.

Saving time and labor by automating data extraction

Efficient financial operations are the cornerstone of strategic agility and long-term profitability. In a landscape where data-driven decision-making is paramount, the traditional methods of data extraction are increasingly seen as liabilities rather than assets. The reliance on manual processes not only drains valuable resources but also introduces risks that can compromise the integrity of financial data.

Traditional data extraction methods

  • Manual data entry and its drawbacks: Manual data entry, once considered a necessary function, now stands as a significant obstacle to operational efficiency. The labor-intensive nature of this process consumes substantial time and resources, diverting attention away from higher-value strategic initiatives. Moreover, as the scale of operations expands, the limitations of manual entry become glaringly apparent, undermining the finance team’s ability to respond swiftly to market dynamics.
  • The risk of human error and data inaccuracies: In the high-stakes environment of financial operations, accuracy is non-negotiable. However, manual data entry is inherently susceptible to human error, introducing discrepancies that can lead to financial misstatements, compliance breaches, and misinformed decisions. The inconsistency of manually entered data poses a significant risk, making it increasingly untenable as a reliable method for managing financial information.

AI-powered data extraction

  • How AI automates the extraction of key contract data: Forward-thinking CFOs are turning to AI-powered CLM systems to eliminate the inefficiencies of manual data extraction. These systems employ advanced algorithms to automatically identify and extract critical financial data from contracts with unparalleled speed and precision. By integrating AI into the data extraction process, finance teams can rapidly process large volumes of contracts, significantly reducing the time and effort required while ensuring data accuracy and completeness.
  • Real-time data extraction and updating: The ability to extract and update data in real-time is a game-changer for financial operations. AI-driven tools make the most current and relevant information available, enabling CFOs to make informed decisions based on up-to-date financial insights. This real-time capability enhances the responsiveness and agility of the finance function, allowing organizations to navigate market changes with confidence.

Impact on financial operations

  • Significant reduction in time spent on manual tasks: By automating the data extraction process, AI-powered CLM systems free up crucial resources within the finance team. This shift not only reduces the time spent on low-value manual tasks but also reallocates talent toward strategic activities that drive enterprise value, such as financial planning and analysis, risk management, and capital allocation.
  • Enhanced accuracy and consistency in financial data: The integration of AI in data extraction mitigates the risk of human error, leading to greater consistency and reliability in financial reporting. Accurate, consistent data is the foundation upon which sound financial strategies are built, enabling CFOs to maintain rigorous compliance standards and deliver precise insights to stakeholders.

For financial officers focused on optimizing financial operations, the adoption of AI-powered data extraction is a strategic necessity. By embracing this technology, finance leaders can enhance the efficiency, accuracy, and strategic value of their operations, positioning their organizations for sustained success in an increasingly competitive marketplace.

Leveraging AI-extracted data for budgeting and forecasting

As financial operations become increasingly complex, the accuracy and timeliness of budgeting and forecasting are critical to maintaining a competitive edge. Traditional methods often struggle to keep pace with the rapid changes in market conditions and the growing volume of data that finance teams must analyze.

In this context, AI-powered CLM systems offer a transformative solution, enabling CFOs to harness real-time data for more precise and dynamic financial planning.

Challenges in traditional budgeting and forecasting

  • Incomplete or outdated data sources: Traditional budgeting and forecasting often rely on static data, which can quickly become outdated. The lag between data collection and analysis introduces risks, as decisions may be based on information that no longer reflects current conditions. This can lead to inaccurate projections and suboptimal financial planning.
  • Time-consuming data gathering and analysis: The manual processes involved in gathering and analyzing financial data are both time-consuming and resource-intensive. Finance teams must sift through numerous contracts and documents to extract relevant information, which not only delays the forecasting process but also increases the likelihood of errors. This inefficiency hampers the ability to respond swiftly to market changes, putting the organization at a strategic disadvantage.

Benefits of AI-driven insights

  • Using AI to analyze historical contract data for trend analysis: AI-powered CLM systems excel at processing vast amounts of historical contract data, identifying patterns, and uncovering trends that may not be immediately apparent through manual analysis. By leveraging AI, financial experts can gain deeper insights into past performance, helping to inform more accurate and strategic forecasts. This data-driven approach enhances the reliability of financial planning and enables better anticipation of future trends.
  • Real-time data for accurate budgeting and forecasting: One of the most significant advantages of AI-powered CLM systems is their ability to provide real-time data. This ensures that financial forecasts are based on the most current information, allowing for more accurate and timely budgeting. Real-time data enables finance officers to adjust forecasts dynamically in response to market fluctuations or internal changes, keeping financial plans relevant and aligned with the organization’s strategic goals.
  • Improvements in forecast accuracy: Organizations that leverage AI-powered CLM systems consistently report improvements in forecast accuracy and efficiency. The ability to integrate real-time data into financial models and rapidly adjust forecasts in response to changing conditions is a key driver of these benefits. The increased accuracy of forecasts not only supports better decision-making but also enhances the organization’s ability to allocate resources effectively and mitigate financial risks.

For finance professionals seeking to optimize their planning processes, the integration of AI-extracted data into budgeting and forecasting represents a powerful tool. By enhancing the accuracy, timeliness, and relevance of financial forecasts, AI-powered CLM systems enable finance leaders to make more informed, strategic decisions that drive long-term success.

Key reasons to adopt an AI-powered CLM platform

Adopting an AI-powered contract lifecycle management (CLM) system is not just a forward-thinking move; it’s a strategic necessity. For CFOs aiming to streamline processes, enhance accuracy, and drive business value, AI-powered CLM offers a host of benefits that address critical operational challenges.

Enhanced visibility and control

  • Centralized data repository and real-time insights: AI-powered CLM systems consolidate all contract data into a centralized, easily accessible repository. This centralized approach eliminates the silos that often plague traditional systems, providing CFOs with a unified view of all contracts across the organization.Tip: Schedule a quarterly review of the top 10 highest-value contracts using your CLM’s real-time insights dashboard to identify any performance issues or renegotiation opportunities.
  • Better contract compliance and risk management: AI-driven CLM systems enhance compliance by automating the monitoring of contractual obligations and deadlines. For instance, the system can automatically flag contracts that are nearing expiration or require renegotiation, helping to avoid lapses that could lead to financial penalties or missed opportunities.Tip: Set up automated alerts in your CLM platform to notify key stakeholders at least 90 days before critical contract milestones like renewals or expirations, allowing ample time for necessary actions.

Cost efficiency and savings

  • Reduced need for manual labor: One of the most immediate benefits of AI-powered CLM systems is the significant reduction in manual data entry and processing tasks. By automating these labor-intensive activities, finance teams can focus on higher-value tasks such as financial analysis and strategic planning.Tip: Implement a bi-weekly audit of automated data entries to improve accuracy, gradually reducing the frequency as confidence in the system’s reliability grows.
  • Minimizing financial risks through accurate data: AI-powered systems provide a higher degree of accuracy in data extraction and analysis, minimizing the financial risks associated with human error.Tip: Use your CLM platform to generate a monthly report on data discrepancies, and establish a routine for finance teams to review and correct any identified issues, thereby reducing the risk of costly errors.

Improved decision-making

  • Data-driven insights for strategic decisions: The rich, real-time data provided by AI-powered CLM systems enables CFOs to make more informed and strategic decisions. By analyzing trends and patterns in contract data, finance leaders can identify opportunities for cost savings, optimize vendor relationships, and negotiate more favorable contract terms.Tip: Develop a quarterly report template that highlights trends in contract pricing and vendor performance, and use this report in leadership meetings to drive negotiations for better terms.
  • Ability to quickly adapt to market changes: In today’s dynamic market, the ability to quickly adjust financial plans in response to changing conditions is crucial. AI-powered CLM systems provide the agility needed to revise contracts and financial forecasts in real-time, keeping your organization competitive.Tip: Set up a protocol within your CLM system to automatically adjust payment terms in response to key market indicators (e.g., commodity prices or interest rates), allowing for agile financial planning.

Scalability and flexibility

  • Adapting to growing volumes of contracts and data: As organizations grow, so does the volume and complexity of contracts they must manage. AI-powered CLM systems are designed to scale effortlessly, handling increased data loads without compromising performance.Tip: Regularly review your CLM software’s contract categorization criteria to make sure it aligns with current business priorities, making adjustments as your organization scales.
  • Customizable solutions for different business needs: One of the key advantages of AI-powered CLM systems is their flexibility. These solutions can be tailored to meet the specific needs of your organization, whether that involves integrating with existing financial systems or customizing workflows to align with your business processes.Tip: Work with your CLM provider to develop a custom workflow that integrates with your existing ERP system, streamlining financial operations and improving data consistency.

Competitive advantage

  • Staying ahead of the competition with advanced technology: In a competitive market, leveraging advanced technologies like AI-powered CLM system can provide a significant edge. These systems not only streamline operations but also offer insights that help organizations make smarter, faster decisions.Tip: Annually benchmark your contract management processes against industry best practices using your CLM tool’s analytics, and use the findings to set improvement targets for the upcoming year.
  • Better financial planning and execution: The combination of real-time data, enhanced accuracy, and strategic insights provided by AI-powered CLM systems results in more effective financial planning and execution.Tip: Create a scenario analysis spreadsheet to simulate different financial planning outcomes based on various contract negotiation strategies, helping to refine your approach for maximum effectiveness.

Whether you’re looking to enhance visibility, reduce costs, improve decision-making, or gain a competitive advantage, an AI-powered CLM is a powerful tool that can help you achieve your financial goals.

Addressing common concerns and misconceptions about AI-powered CLM

As with any significant technological advancement, the adoption of AI-powered Contract Lifecycle Management (CLM) systems often raises concerns and misconceptions. For CFOs considering this transformative shift, it is crucial to address these issues head-on to provide a smooth implementation and maximize the benefits of the new system.

Concerns about data security

  • Safeguarding data within an AI-powered system: One of the primary concerns surrounding AI-powered CLM systems is data security. Given the sensitive nature of financial and contractual information, safeguarding this data is paramount. AI-powered CLM systems are built with advanced security features, including encryption, multi-factor authentication, and access controls, to protect data from unauthorized access and breaches.Tip: Conduct a semi-annual review of your CLM tool’s security settings, including encryption protocols and access controls, to make sure they align with the latest industry standards and internal security policies.
  • Compliance with financial regulations and standards: Another critical aspect of data security is regulatory compliance. AI-powered CLM systems are designed to help organizations adhere to various financial regulations by providing tools for tracking contract compliance, generating audit trails, and verifying that all contractual processes meet legal standards.Tip: Set up a review schedule of documents within your CLM system, to highlight any gaps in adherence to key financial regulations, and review these reports with your compliance team on a monthly basis.

Perceived complexity and implementation challenges

  • Simplifying the adoption of AI-powered CLM: The perceived complexity of implementing an AI-powered CLM system can be a significant barrier for CFOs. However, modern AI-powered CLM solutions are designed with user-friendliness in mind, offering intuitive interfaces and seamless integration with existing financial systems.Tip: Start with a pilot program that involves a small, cross-functional team using the AI-powered CLM on a limited set of contracts. This allows your organization to familiarize itself with the system in a low-risk environment before scaling up.
  • Realistic timelines and steps for implementation: Setting realistic expectations for the implementation process is key to its success. While AI-powered CLM systems offer significant long-term benefits, the transition requires careful planning and execution.Tip: Break down the implementation process into phases, with clear milestones such as system configuration, user training, and data migration, and track progress weekly to keep the project on schedule.

ROI considerations

  • Analyzing the cost-benefit ratio: One of the most common concerns CFOs have when considering AI-powered CLM systems is the return on investment (ROI). While the initial costs of implementing an AI-powered CLM can be significant, the long-term benefits — such as reduced manual labor, increased accuracy, and improved compliance — often outweigh the costs.Tip: Use your CLM platform to generate a detailed report of time and cost savings from automated tasks, comparing these metrics against the initial implementation costs to regularly assess ROI over the first year.
  • Long-term financial benefits and return on investment: The financial benefits of AI-powered CLM systems extend beyond immediate cost savings. Over time, these systems can drive significant value by enabling more strategic decision-making, improving compliance, and enhancing operational efficiency.Tip: Create a dashboard within your contract management software, to monitor key performance indicators (KPIs) like contract cycle time, error reduction, and compliance rates, and use these metrics to quantify the system’s impact on long-term financial performance during quarterly reviews.

By addressing these common concerns and misconceptions, Finance professionals can approach the adoption of AI-powered CLM systems with confidence. Understanding the security features, simplifying the implementation process, and evaluating the ROI will help make sure that the transition to AI-powered CLM is not only smooth but also strategically beneficial for the organization.

Case study: PAAY

Concord’s customer PAAY successfully deployed an AI-powered CLM system to streamline its financial operations. This transformation reduced the need for manual intervention, improved the speed and accuracy of data processing, and allowed the finance team to focus on more strategic initiatives.

The results included more efficient contract management, enhanced compliance, and improved financial reporting accuracy, showcasing the tangible benefits of adopting AI-powered solutions in financial operations.

“[Concord’s] level of automation has really led to better data collection, and cleanliness too,” says Melissa Hyde, Senior Sales Operations Analyst at PAAY. “Now that we’ve got Google Drive connected to Concord, we can bring in PDFs and Word docs that were created and signed in any platform, and Concord pulls out the data we need with just a click.”

PAAY’s experience demonstrated that dopting an AI-powered CLM system is more than a technological upgrade — it’s a strategic decision that can drive substantial long-term value. By enhancing visibility, improving accuracy, and enabling data-driven decision-making, AI-powered CLM empowers finance leaders to optimize operations, mitigate risks, and seize new opportunities.

As the role of the CFO continues to evolve, those who embrace AI-driven solutions will be better positioned to lead their organizations with agility, precision, and confidence, securing a competitive advantage in a rapidly changing market.

Optimize financial operations with Concord’s AI-powered CLM software.

Book a demo!

Create, collaborate, negotiate, e-sign, manage, and analyze all agreements on one platform.

See what Concord can do for you.

Book a demo