Putting the “I” in “AI” – Reinventing the time recording process

CloudCapcha

Accountancy practice management software has come a long way. Today, features like automated billing and reconciliations are easily integrated into the day-to-day practice workflow of Wolters Kluwer Tax & Accounting UK customers.

Our employees work side by side with our customers to create and manage these solutions – driven by a deep understanding of their needs and addressing the rapid changes in their environment.

However, it’s often hard to look beyond improving performance in day-to-day operations. Amid Brexit, the COVID-19 pandemic and other disruptions, accountancy practices and their clients are dealing with an unpredictable economic landscape. Future business planning can appear daunting.

However, technology can support accountancy practices (and their clients) in making informed business decisions, and planning for the future. In the first part of our Accountancy Practice Management for Future-Fit Growth series, we’ll explore how they can use technology to define and easily track Key Performance Indicators (KPIs). Doing so gives practices closer control of performance tracking, and deeper insights that will inform strategic growth plans.

Saving Time

For several decades, business technology platforms have enabled practices to track performance metrics that they have customised. This highlights areas that qualify for improvement and underpins strategic planning.

Contemporary technology, such as CCH KPI Monitoring, makes setting up KPIs faster and easier for accountancy practices than ever before. This is vital today. The current business landscape demands that firms assess and amend KPIs more frequently, based on fresh market variables. KPIs such as client retention rate and business time-to-recovery have become increasingly prominent performance indicators in the past year. If clunky technology makes KPI management difficult, practices have less time and insight to plan future growth.

Reducing Risk
CCH KPI Monitoring makes it far easier to track KPIs and report on them. This is fundamental in minimising risk. For example, if a KPI is set to track and escalate debt filtered by overdue dates, the ability to easily set alerts and automatically generate reports is critical to practice performance management.

Some practices are manually running monthly reports to measure KPIs. Others are running real-time reporting engines, a key feature of CCH KPI Monitoring. This latter solution allows practices to review essential data at any time – covering both performance management and compliance requirements. They can do so remotely or on-premise.

This means that firms can assess issues before they become problems, and thus act proactively. Real-time reporting is a true asset in building a future-fit practice.

The Proof is in the Practice
A number of Wolters Kluwer customers have been using CCH KPI Monitoring for several years now. Our customers look to us when they need to be right. Ryecroft Glenton has successfully integrated CCH KPI Monitoring with its own system. This consolidates information from several sources, including CCH Central and CCH Practice Management.

“We can use the year end date to trigger a sequence of reminders. Have we asked for the books? Have they been received? If a request to a client has been outstanding for a certain period, the partner will receive an alert via email. For limited companies, we can monitor the corporation tax and Companies House filing deadlines – as well as the different deadlines for pension schemes”

– Ian Smith, partner at Ryecroft Glenton

Corporate events agency who benefited from greener graphics initiative

“Apogee are not just aprinting company, theyconsult with us and go onto deliver a full end to endservice from concept toinstallation. They go aboveand beyond and we lookforward to continuing ourjourney with them”

Corporate events agency who benefited from greener graphics initiative

“Apogee are not just aprinting company, theyconsult with us and go onto deliver a full end to endservice from concept toinstallation. They go aboveand beyond and we lookforward to continuing ourjourney with them”

Corporate events agency who benefited from greener graphics initiative

“Apogee are not just aprinting company, theyconsult with us and go onto deliver a full end to endservice from concept toinstallation. They go aboveand beyond and we lookforward to continuing ourjourney with them”

Corporate events agency who benefited from greener graphics initiative

“Apogee are not just aprinting company, theyconsult with us and go onto deliver a full end to endservice from concept toinstallation. They go aboveand beyond and we lookforward to continuing ourjourney with them”

How does Amazon predict your shopping preferences, Spotify curate your playlist, or Netflix suggest the perfect show? The secret lies in advanced pattern recognition and adaptive learning loops. At CloudCapcha, we’ve applied these machine-learning techniques to revolutionise time recording, making it more intuitive, accurate, and efficient.

Understanding AI in Context

Before diving into our AI module, it’s important to clarify what AI truly entails. Many systems labelled as AI are simply automated processes. While ChatGPT is a familiar “face” of AI, the foundational work is done by technologies like machine learning and deep learning. These behind-the-scenes engines power CloudCapcha’s efforts to transform time recording for professionals.

The Challenge: Automating Work Categorisation

WorkCapcha has already succeeded in capturing an individual’s work activities across applications. The next step was automating their categorisation—linking activities to clients, projects, tasks, or milestones. Our goal: eliminate manual data entry and offer users a pre-populated daily timesheet to review and confirm.

This challenge is far from simple. Data inconsistencies across applications pose significant hurdles, as does the variability in user behaviour. Some work in a structured manner, while others juggle multiple tasks. Add interruptions to the mix, and interpreting these signals becomes a formidable task.

Our Approach

To tackle these challenges, we developed a proprietary blend of data science methodologies, including clustering, rules-based logic, non-generative natural language processing (NLP), and neural networks. No off-the-shelf solution could address this complexity, so we created our own process.

The AI identifies patterns in user behaviour and continuously improves its predictions. Each recommendation—client, project, and task—is scored and ranked, with the most likely options presented for confirmation. Users’ choices further train the system to refine its accuracy.

Breaking It Down: Step-by-Step   

Signal Deduplication: WorkCapcha receives multiple signals for a single activity. For instance, editing a document in SharePoint and Word generates numerous signals. Using rules-based logic and clustering, we aggregate these into one meaningful signal.

Filtering and Pre-Processing: Datasets with high confidence levels are pre-processed using rules-based models and NLP. This step recognises patterns and scores potential outcomes to improve accuracy.

Model Training: Techniques like decision trees, clustering, and neural networks are applied in combination to predict clients, projects, and tasks. Predictions are scored and ranked for users to confirm or adjust, further enhancing the model.

Continuous Learning and Improvement 

The AI relies on learning loops to refine its performance. Every interaction—whether confirming a suggestion or choosing an alternative—teaches the model. Over time, the system adapts to individual users’ preferences.

Models are retrained weekly, ensuring continuous improvement. Each firm has a unique model stored securely in CloudCapcha’s Azure tenancy. As users interact with the system, the AI learns their work patterns, steadily moving toward the goal of a low-touch timesheet experience.

Optimised User Interface and Performance  

We also prioritised user experience and system efficiency. Activities are pre-scored before appearing in the WorkCapcha DayBook, enabling instant recommendations. For instance, selecting a client immediately displays the most relevant project options. If a less likely client is chosen, the system recalculates project recommendations in real-time, ensuring a seamless workflow.

The Vision: Low-Touch Timesheets  

The ultimate goal is to eliminate manual data entry altogether. Over time, the AI will present users with an accurate, pre-filled record of their workday. Users can confirm or make minor edits, significantly reducing effort while maintaining precision.

With each interaction, the AI becomes better at predicting user behaviour. Eventually, its confidence levels will be high enough to deliver an entirely automated experience, allowing users to focus on their work rather than time recording.

Confidence in the Future  

Our optimism stems from the success of similar AI-driven systems in other industries. E-commerce and entertainment platforms rely on neural networks to deliver personalised recommendations that learn from user choices.

WorkCapcha applies this approach to time recording, pairing robust logic with a user-friendly interface. As users train the AI with real-world data, the system will evolve into an indispensable tool for professionals, streamlining time management like never before.

The manual timesheet is becoming obsolete. With CloudCapcha AI, we’re building a future where time recording is seamless, smart, and effortless. The “timesheet to end all timesheets” is closer than ever, offering unprecedented convenience and efficiency.

Dec 2024

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