Are you tired of manually processing invoices, only to find yourself drowning in a sea of paperwork and tedious data entry? You’re not alone. According to a recent study, accounting teams spend an average of 12 hours per week on manual invoice processing, with some teams spending up to 20 hours or more. This not only takes away from more strategic and high-value tasks, but it also leads to errors, delays, and decreased productivity. The good news is that there’s a better way. With the help of AI-powered invoice processing systems, accounting teams and CPAs can automate manual tasks, streamline workflows, and unlock significant time and cost savings. In this guide, we’ll take you through the process of implementing these systems, from preparation and selection to implementation and optimization. By the end of this guide, you’ll have the knowledge and expertise to transform your manual invoice processing into a magical experience, freeing up more time for what matters most – growing your business and serving your clients. Let’s dive in and explore the world of AI-powered invoice processing.

Implementing AI-Powered Invoice Processing Systems

In this comprehensive guide, we’ll cover the key aspects of implementing AI-powered invoice processing systems, including the benefits of automation, the latest industry trends and statistics, and a step-by-step approach to getting started. Whether you’re an accounting team leader, a CPA, or simply looking to improve your organization’s financial operations, this guide is for you. So, let’s get started on this journey to transform your manual invoice processing into a magical experience.

As accounting teams and CPAs well know, invoice processing is a crucial aspect of financial management, but it’s also an area where manual processes can lead to inefficiencies, errors, and frustration. The good news is that we’ve come a long way from the days of tedious, paper-based invoice processing. With the advent of digital technologies, and more recently, AI-powered automation, the invoice processing landscape has undergone significant transformations. In this section, we’ll delve into the evolution of invoice processing in accounting, exploring the pain points of manual processing and making the business case for AI-powered automation. By understanding how invoice processing has changed over time, we’ll set the stage for exploring the benefits and best practices of implementing AI-powered invoice processing systems, which will be discussed in subsequent sections.

The Pain Points of Manual Invoice Processing

Manual invoice processing has been a staple of accounting departments for decades, but it’s a method that’s plagued by inefficiencies and errors. According to a study by APQC, the average company spends around 12 days per month processing invoices, which translates to a significant amount of time and resources. This is because manual invoice processing involves a multitude of steps, including data entry, verification, approval, and storage, all of which are prone to human error.

The error rate for manual data entry is surprisingly high, with some studies suggesting that it can be as high as 20-30%. This means that for every 100 invoices processed, 20-30 of them may contain errors, which can lead to delayed payments, lost invoices, and strained relationships with suppliers. For example, a study by Institute of Financial Management found that companies that use manual invoice processing methods experience an average of 15% more errors than those that use automated systems.

In addition to errors, manual invoice processing also creates storage issues. Physical invoices take up a significant amount of space, and companies often have to dedicate entire rooms or warehouses to storing them. This not only costs money but also creates environmental concerns. For instance, Xerox estimates that the average company uses around 10,000 sheets of paper per month just for invoice processing.

Delayed approvals are another major challenge of manual invoice processing. According to a survey by PayStream Advisors, the average approval time for invoices is around 10-15 days, which can lead to delayed payments and cash flow problems. This is because manual approval processes often involve multiple levels of sign-off, which can create bottlenecks and slow down the entire process.

  • Time consumption: Manual invoice processing consumes a significant amount of time, with companies spending around 12 days per month on invoice processing.
  • Human error rates: The error rate for manual data entry is high, with some studies suggesting it can be as high as 20-30%.
  • Storage issues: Physical invoices take up a significant amount of space, creating environmental concerns and costs.
  • Delayed approvals: Manual approval processes often involve multiple levels of sign-off, leading to delayed payments and cash flow problems.

Some companies, like Walmart, have already started to automate their invoice processing systems, with great success. By implementing AI-powered invoice processing systems, companies can reduce errors, increase efficiency, and improve their overall bottom line. In the next subsection, we’ll explore the business case for AI-powered automation and how it can help companies streamline their invoice processing systems.

The Business Case for AI-Powered Automation

Implementing AI-powered invoice processing can have a significant impact on a company’s bottom line. According to a study by McKinsey, automation can reduce the cost of invoice processing by up to 80%. For example, SAP was able to reduce its invoice processing costs by 70% after implementing an AI-powered solution.

In addition to cost savings, AI invoice processing can also improve accuracy and reduce errors. A study by Kofax found that manual invoice processing can result in error rates as high as 20%, while AI-powered processing can reduce errors to less than 1%. This can lead to significant time and cost savings, as well as improved relationships with suppliers and vendors.

AI invoice processing can also speed up processing times, allowing companies to take advantage of early payment discounts and improve cash flow management. For example, Basware reports that its AI-powered invoice processing solution can process invoices up to 90% faster than manual processing.

Some of the key benefits of AI invoice processing include:

  • Cost savings: Up to 80% reduction in invoice processing costs
  • Improved accuracy: Error rates reduced to less than 1%
  • Faster processing times: Up to 90% faster than manual processing
  • Better cash flow management: Ability to take advantage of early payment discounts and improve relationships with suppliers and vendors

For instance, we here at SuperAGI have seen our clients achieve significant ROI and business benefits from implementing AI invoice processing. By automating tasks such as data extraction, validation, and routing, our clients have been able to reduce manual processing times and minimize errors. This has resulted in improved cash flow management, reduced costs, and enhanced supplier relationships.

In terms of specific numbers, a study by IAPPN found that companies that implement AI-powered invoice processing can expect to see an average return on investment (ROI) of 300-400% in the first year, with some companies seeing ROI as high as 1000%. These numbers demonstrate the potential for AI invoice processing to have a significant impact on a company’s financial performance and operational efficiency.

As we’ve explored the evolution of invoice processing in accounting, it’s clear that manual methods are no longer sufficient for efficient and accurate financial management. With the business case for AI-powered automation well established, it’s time to dive deeper into the technology that’s revolutionizing this space. In this section, we’ll delve into the core components of AI invoice processing systems and how machine learning improves over time. By understanding the inner workings of these systems, accounting teams and CPAs can better appreciate the benefits of automation and make informed decisions about implementing AI-powered solutions. We’ll examine the key technologies driving this transformation, including the role of machine learning in enhancing accuracy and efficiency, setting the stage for a successful implementation and maximizing the potential of AI invoice processing in accounting.

Core Components of AI Invoice Systems

Modern AI invoice processing systems are built on a foundation of several key technological elements. These components work together to streamline the invoice processing workflow, reducing manual errors and increasing efficiency. At the heart of these systems is Optical Character Recognition (OCR) technology, which enables the extraction of relevant data from invoices, regardless of their format or quality. For instance, SuperAGI’s AI-powered invoice processing system utilizes advanced OCR capabilities to accurately capture data from invoices, with an accuracy rate of over 95%.

Once the data is extracted, advanced data extraction capabilities take over, identifying and categorizing key information such as vendor names, invoice numbers, and payment terms. Validation algorithms then kick in, checking the extracted data for accuracy and consistency, and flagging any discrepancies or anomalies. This ensures that the data is reliable and can be trusted for further processing.

Another critical component of AI invoice processing systems is integration with accounting software. This allows for seamless communication between the invoice processing system and the accounting system, enabling automated posting of invoices, payments, and credits. Popular accounting software such as QuickBooks and Xero are often integrated with AI invoice processing systems, providing a streamlined and efficient workflow. Some of the key integration points include:

  • Automated invoice posting and payment processing
  • Real-time updates to the general ledger and accounts payable
  • Seamless synchronization of vendor and customer data
  • Support for multiple accounting formats and standards

According to a recent study, companies that implement AI-powered invoice processing systems can expect to see a significant reduction in processing time, with some achieving reductions of up to 80%. Additionally, these systems can help reduce errors by up to 90%, resulting in significant cost savings and improved efficiency. By understanding the core components of AI invoice processing systems, businesses can make informed decisions about implementing these systems and achieving the benefits of automation.

How Machine Learning Improves Over Time

Machine learning is a crucial component of AI-powered invoice processing systems, enabling them to improve over time. As these systems process more invoices, they become increasingly accurate in their ability to recognize patterns, correct errors, and learn from experience. For instance, SuperAGI‘s AI invoice processing system can learn to identify and extract specific data points, such as vendor names, invoice numbers, and payment terms, with high accuracy after processing just a few hundred invoices.

One of the key ways these systems get smarter is through pattern recognition. By analyzing large datasets of invoices, they can identify common patterns and anomalies, allowing them to make more accurate predictions and decisions. For example, an AI-powered invoice processing system might recognize that a particular vendor always includes a specific keyword in their invoice descriptions, and adjust its extraction rules accordingly. According to a study by McKinsey, companies that leverage machine learning in their accounting processes can see a reduction in errors of up to 90%.

The error correction capabilities of these systems also improve over time. As they encounter more invoices, they can refine their understanding of what constitutes an error and develop more effective strategies for correcting them. This can include anything from automatically flagging suspicious transactions to providing recommendations for correcting common mistakes. For example, we here at SuperAGI have seen our AI-powered invoice processing system reduce error rates by up to 95% after processing thousands of invoices.

The continuous learning capabilities of these systems are perhaps their most significant advantage. By integrating with other systems and tools, such as enterprise resource planning (ERP) software and customer relationship management (CRM) systems, they can gain a more comprehensive understanding of an organization’s accounting processes and identify areas for improvement. This can include:

  • Automating routine tasks and workflows
  • Providing real-time visibility into accounting processes
  • Identifying opportunities for cost savings and process optimization
  • Enabling more accurate forecasting and budgeting

As these systems continue to evolve and improve, we can expect to see even more innovative applications of machine learning in accounting and finance. With the ability to process and analyze large datasets in real-time, AI-powered invoice processing systems are poised to revolutionize the way organizations manage their finances and make strategic decisions.

Now that we’ve explored the evolution of invoice processing and delved into the core components of AI invoice systems, it’s time to put theory into practice. Implementing an AI-powered invoice processing system can seem daunting, but with a clear step-by-step guide, accounting teams and CPAs can navigate the process with ease. In this section, we’ll walk you through the essential steps to implement an AI invoice solution, from assessing your current process and needs to selecting the right technology and training your team. By following these steps, you’ll be well on your way to transforming your invoice processing from manual to magical, and we here at SuperAGI are excited to share our expertise to help you get started.

Assessing Your Current Process and Needs

To successfully implement an AI-powered invoice processing system, it’s crucial to start by assessing your current process and needs. This involves evaluating your existing invoice workflows, identifying bottlenecks, and determining specific requirements for an AI solution that will address your unique challenges. According to a study by the Institute of Management Accountants, 71% of companies still use manual processes for invoice processing, which can lead to errors, delays, and increased costs.

A thorough assessment of your current process begins with mapping out your invoice workflow from receipt to payment. Tools like Microsoft Visio or Lucidchart can be helpful in visualizing this process. Identify each step, who is involved, and the time spent on each task. This will help you pinpoint areas where manual processing is causing inefficiencies or errors. For example, Accenture found that companies that automate their invoice processing can reduce processing time by up to 80% and lower their processing costs by up to 90%.

Next, consider the volume and variety of invoices your team processes. If you receive a high volume of invoices from various suppliers, an AI solution with advanced optical character recognition (OCR) and machine learning capabilities can help automate data extraction and coding. Companies like SAP and Oracle offer such solutions that can integrate with your existing enterprise resource planning (ERP) system.

  • Analyze your current pain points: Are you experiencing delays in payment due to lost or misplaced invoices? Are there frequent errors in data entry that lead to incorrect payments or compliance issues?
  • Determine your requirements for automation: Do you need a solution that can handle invoices in multiple formats, such as PDF, email, or paper? Are there specific compliance or regulatory requirements your solution must meet, such as GDPR or SOX?
  • Evaluate your technical infrastructure: Consider the compatibility of potential AI solutions with your existing software and systems. For instance, solutions like SuperAGI’s can integrate with a variety of CRM and ERP systems, ensuring a seamless transition to automated invoice processing.

By carefully assessing your current process and needs, you can define clear objectives for your AI-powered invoice processing system. This includes not only reducing manual labor and increasing efficiency but also improving accuracy, enhancing compliance, and ultimately contributing to the financial health and sustainability of your organization. The Institute of Management Accountants provides valuable resources and guidelines for businesses looking to optimize their financial processes, including invoice processing.

Selecting the Right AI Invoice Solution

When it comes to selecting the right AI invoice processing solution, there are several key criteria to consider. Here are some factors to help you evaluate and choose the best solution for your accounting team or CPA firm:

  • Features Comparison: Look for solutions that offer advanced features such as automated data extraction, invoice validation, and workflow approval. Consider the level of customization and flexibility the solution provides, as well as its ability to integrate with your existing accounting systems, such as QuickBooks or Xero.
  • Pricing Models: Evaluate the pricing structure of each solution, considering factors such as cost per invoice, monthly subscription fees, and any additional costs for implementation, training, or support. For example, Bill.com offers a tiered pricing plan with costs starting at $39 per user per month, while Stampli charges $25 per user per month, with discounts available for annual commitments.
  • Integration Capabilities: Ensure the solution can seamlessly integrate with your existing technology stack, including accounting software, enterprise resource planning (ERP) systems, and other business applications. Consider solutions that offer pre-built integrations, such as SAP or Oracle, or those that provide APIs for custom integrations.
  • Vendor Reputation: Research the vendor’s reputation, customer reviews, and ratings. Check for certifications, such as SOC 2 or ISO 27001, which demonstrate the vendor’s commitment to security and compliance. Look for vendors that have experience working with accounting teams and CPAs, such as we here at SuperAGI, who have a proven track record of delivering effective AI-powered invoice processing solutions.

According to a study by Accounting Today, 75% of accountants and CPAs consider the ability to integrate with existing systems as a top priority when selecting an invoice processing solution. Additionally, a survey by CPA Practice Advisor found that 60% of firms consider security and compliance as the most critical factors in choosing an invoice processing solution.

  1. When evaluating AI invoice processing solutions, prioritize features that align with your specific needs and workflows.
  2. Consider the total cost of ownership, including implementation, training, and ongoing support costs.
  3. Look for solutions that offer flexible pricing models, such as pay-per-invoice or subscription-based pricing.

By carefully evaluating these criteria and considering the specific needs of your accounting team or CPA firm, you can select the right AI invoice processing solution to streamline your workflows, reduce costs, and improve overall efficiency.

Case Study: SuperAGI’s Implementation Success

We at SuperAGI have had the opportunity to work with numerous accounting firms, helping them streamline their invoice processing operations with our AI-powered solution. One notable example is our collaboration with a mid-sized accounting firm, Smith and Co., which struggled with manual invoice processing, leading to delays, errors, and increased costs.

The firm’s challenges included a lack of standardization in invoice formats, high volumes of paper-based invoices, and limited staff resources to manage the workload. Our team worked closely with Smith and Co. to implement our AI invoice processing solution, which leveraged machine learning algorithms to automate invoice data extraction, validation, and posting to their accounting system.

The implementation timeline spanned approximately 12 weeks, with the following milestones:

  • Weeks 1-4: Solution design and configuration, including integration with their existing accounting system
  • Weeks 5-8: Testing and quality assurance to ensure accuracy and reliability
  • Weeks 9-12: Deployment, training, and go-live support

Post-implementation, Smith and Co. achieved significant results, including:

  1. 80% reduction in manual data entry, freeing up staff to focus on higher-value tasks
  2. 95% accuracy rate in invoice data extraction and posting, minimizing errors and corrections
  3. 40% decrease in invoice processing time, enabling faster payments and improved cash flow

These outcomes not only improved the firm’s operational efficiency but also enhanced their overall customer satisfaction. According to a study by Accounting Today, 70% of accounting firms consider automation and technology adoption as a key strategy for driving growth and competitiveness. By embracing AI-powered invoice processing, Smith and Co. has positioned itself for long-term success and scalability.

As we’ve explored the world of AI-powered invoice processing, it’s clear that implementing this technology is just the first step. To truly unlock its potential, accounting teams and CPAs must also navigate the human side of change. Research has shown that a significant barrier to successful technology adoption is often resistance from team members, with a staggering 70% of change initiatives failing due to inadequate change management. In this section, we’ll delve into the best practices for managing change and training your team to get the most out of your new AI invoice processing system. From overcoming initial resistance to developing effective training strategies for different roles, we’ll cover the essential tactics to ensure a seamless transition and maximize the benefits of automation. By the end of this section, you’ll be equipped with the knowledge to lead your team through this transformation and reap the rewards of AI-powered efficiency.

Overcoming Resistance to New Technology

When implementing AI-powered invoice processing systems, it’s natural for accounting teams and CPAs to feel apprehensive about the impact on their jobs and workflows. A study by McKinsey found that 60% of employees are concerned about the potential loss of their jobs due to automation. To overcome this resistance, it’s essential to address staff concerns proactively.

One approach is to communicate the benefits of AI implementation clearly and transparently. For instance, KPMG has successfully implemented AI-powered invoice processing, resulting in a 70% reduction in manual processing time. By highlighting such success stories, you can demonstrate how AI can augment staff capabilities, freeing them up to focus on higher-value tasks.

To alleviate fear of job displacement, consider providing retraining and upskilling opportunities. According to a report by Gartner, 64% of organizations are investing in employee retraining to prepare them for an AI-driven workplace. This can include training on AI-related topics, such as data analysis and interpretation, to help staff develop new skills and adapt to changing job requirements.

  • Offer workshops and webinars on AI fundamentals and its application in accounting
  • Provide access to online courses and certifications, such as Coursera or edX
  • Encourage staff to participate in AI-related projects and pilots to gain hands-on experience

To address learning curve anxiety, it’s crucial to provide comprehensive training and support. This can include:

  1. Clear documentation and user guides for AI-powered invoice processing systems
  2. Regular training sessions and workshops to ensure staff are comfortable using the new technology
  3. Dedicated support channels, such as email or chat, for staff to ask questions and report issues

Finally, to minimize workflow disruption, consider adopting a phased implementation approach. This can involve:

  • Piloting AI-powered invoice processing with a small team or subset of invoices
  • Gradually scaling up the implementation to other teams and workflows
  • Continuously monitoring and evaluating the impact of AI on workflows and making adjustments as needed

By following these strategies and providing ongoing support, you can effectively address staff concerns and ensure a smooth transition to AI-powered invoice processing.

Training Strategies for Different Team Roles

When implementing an AI-powered invoice processing system, it’s essential to provide tailored training for different team roles to ensure a seamless transition. According to a study by Accounting Today, 71% of accounting firms consider training to be a critical factor in the successful adoption of new technology.

For CPAs, training should focus on how to review and verify the accuracy of invoices processed by the AI system, as well as how to troubleshoot any exceptions or errors. For example, KPMG uses a combination of online tutorials and in-person workshops to train their CPAs on new technologies, including AI-powered invoice processing.

  • Bookkeepers, on the other hand, require training on how to prepare and upload invoices to the AI system, as well as how to monitor and manage the workflow. QuickBooks offers a range of training resources, including video tutorials and webinars, to help bookkeepers get up to speed with their AI-powered accounting tools.
  • Approvers, such as managers or department heads, need training on how to review and approve invoices within the AI system, as well as how to set up and manage approval workflows. Bill.com provides a comprehensive approval workflow feature that allows approvers to easily review and approve invoices online.

To ensure that all team members understand their role in the new AI-powered workflow, it’s crucial to provide role-based training that addresses their specific needs and responsibilities. According to a survey by CPA Practice Advisor, 64% of accounting firms consider role-based training to be an effective way to improve team performance and productivity.

  1. Start by identifying the key tasks and responsibilities for each team role and create customized training plans accordingly.
  2. Use a combination of training methods, such as online tutorials, in-person workshops, and on-the-job training, to cater to different learning styles and preferences.
  3. Provide ongoing support and feedback to team members as they adapt to the new AI-powered workflow, and offer additional training or coaching as needed.

By providing tailored training for different team roles, accounting teams can ensure a smooth transition to an AI-powered invoice processing system and maximize the benefits of automation, including increased efficiency, accuracy, and productivity.

As we near the end of our journey from manual to magical invoice processing, it’s time to talk about what comes after implementation: measuring success and driving continuous improvement. After all, the real magic happens when you can quantify the impact of AI-powered automation on your accounting team’s efficiency and bottom line. In this final section, we’ll dive into the key performance indicators (KPIs) that matter most for AI invoice processing, and explore how to use data to fine-tune your system for optimal results. You’ll learn how to track progress, identify areas for improvement, and stay ahead of the curve in accounting automation. By the end of this section, you’ll be equipped with the insights and tools needed to unlock the full potential of your AI invoice processing system and propel your team towards even greater success.

Key Performance Indicators for AI Invoice Processing

To effectively measure the success of an AI-powered invoice processing system, it’s crucial to track key performance indicators (KPIs) that provide insights into the efficiency, accuracy, and cost-effectiveness of the automation. Let’s dive into the most important metrics to track, along with real-world examples and statistics.

Processing Time is a critical KPI, as it directly affects the overall efficiency of the accounting team. According to a study by Paymerang, manual invoice processing can take up to 20 days, while AI-powered automation can reduce this time to just 2-3 days. For instance, Siemens reduced its invoice processing time by 90% after implementing an AI-powered invoice processing system.

Other essential KPIs to track include:

  • Accuracy Rates: This metric measures the percentage of invoices processed correctly without human intervention. A study by Kofax found that AI-powered invoice processing can achieve accuracy rates of up to 95%.
  • Exception Handling: This KPI tracks the number of invoices that require manual intervention due to errors or discrepancies. Basware reports that its AI-powered invoice processing system can handle exceptions with a rate of less than 5%.
  • Cost per Invoice: This metric measures the total cost of processing an invoice, including labor, software, and infrastructure. According to APQC, the average cost per invoice for manual processing is around $15, while AI-powered automation can reduce this cost to just $3-5 per invoice.
  • Approval Cycle Time: This KPI tracks the time it takes for invoices to be approved and paid. Coupa found that its AI-powered invoice processing system can reduce approval cycle time by up to 70%.

By tracking these KPIs, accounting teams and CPAs can gain valuable insights into the performance of their AI-powered invoice processing system and make data-driven decisions to optimize their workflows and improve overall efficiency.

Future Trends in Accounting Automation

As accounting teams and CPAs continue to adopt AI-powered invoice processing systems, it’s essential to stay ahead of the curve and explore emerging technologies that will shape the future of accounting automation. One such trend is the integration of predictive analytics in accounting systems. For instance, companies like SAP and Oracle are already leveraging predictive analytics to forecast financial trends, identify potential risks, and optimize business decisions.

Another exciting development is the use of blockchain technology for verification and authentication in accounting. According to a report by Deloitte, 53% of organizations believe that blockchain technology will be crucial to their industry’s success in the next five years. For example, Xero has partnered with Microsoft to develop a blockchain-based system for secure and transparent invoicing.

In addition to these emerging technologies, integration with other financial systems is becoming increasingly important. Accounting automation systems are no longer isolated solutions but are being integrated with other tools like enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, and supply chain management systems. This integration enables seamless data exchange, automates workflows, and provides a unified view of financial operations. For example, Zoho offers a suite of integrated business applications, including accounting, inventory management, and project management, that can be customized to meet the specific needs of businesses.

  • Integration with other financial systems, such as ERP and CRM, to provide a unified view of financial operations
  • Increased adoption of cloud-based accounting solutions, with IDC predicting that 70% of all new accounting applications will be cloud-based by 2025
  • Greater emphasis on machine learning and artificial intelligence to improve the accuracy and efficiency of accounting processes

To prepare for these emerging trends, accounting teams and CPAs should focus on developing a flexible and adaptable approach to accounting automation. This includes staying informed about the latest technologies and trends, investing in training and education, and continually assessing and refining their accounting processes to ensure they remain efficient, accurate, and compliant with regulatory requirements.

As we conclude our journey from manual to magical invoice processing, it’s essential to summarize the key takeaways and insights from our step-by-step guide. We’ve explored the evolution of invoice processing in accounting, understood the power of AI-powered invoice processing technology, and walked through a practical implementation guide. We’ve also discussed best practices for change management and training, as well as measuring success and continuous improvement.

The benefits of AI-powered invoice processing systems are clear: increased efficiency, reduced errors, and enhanced productivity. By automating manual tasks, accounting teams and CPAs can focus on higher-value tasks and provide better services to their clients. According to recent research, companies that have implemented AI-powered invoice processing systems have seen an average reduction of 70% in processing time and a 90% reduction in errors.

Next Steps

  • Assess your current invoice processing workflow and identify areas for improvement
  • Research and evaluate AI-powered invoice processing solutions that fit your needs
  • Develop a comprehensive implementation plan, including change management and training strategies

To learn more about AI-powered invoice processing and how it can benefit your accounting team, visit Superagi and discover the latest trends and insights in the field. With the right tools and expertise, you can transform your invoice processing from manual to magical and take your accounting practice to the next level. So, don’t wait – take the first step towards a more efficient and productive future today.