Whether you provide legal, accounting, marketing, technology, or other types of professional services, people are at the heart of your business. To be successful, you need skilled, productive employees that can profitably deliver a great outcome for your customers.
While this sounds simple, it’s often difficult to optimize profitability and outcomes. When people forget to track, can’t accurately recall, or don’t enter project hours and other critical information, it makes it tough to measure key performance indicators and make needed adjustments to improve.
While still in at the early stages, artificial intelligence (AI) and machine learning (ML) can provide professional services firms with more efficient and effective ways to ensure that you have the information you need, when you need it, to meet your profitability and performance goals.
A Brief AI and ML Explainer
AI and ML take process business automation to the next level by making applications “smart.” AI algorithms direct an application to perform certain tasks, and ML enables the algorithm to continually learn and get better at doing the job. Combined, these technologies help businesses to easily crunch through and analyze vast quantities of data, predict patterns, spot anomalies, make recommendations, take action, and learn and improve over time.
You probably already use AI and ML every day. Google Maps and Waze navigation systems employ AI and ML to analyze traffic speed, get the best route to a destination, and offer notifications about construction and accidents along the way. Email spam filters use these technologies to aggregate and analyze different signals, such as specific words in the message, metadata (where it’s sent from, who sent it, etc.), and your own personal preferences to keep junk out of your inbox. And, when you deposit a check through a mobile app, AI and ML are working behind the scenes decipher and convert handwriting on checks into text via OCR.
Only about half of small and medium businesses (SMBs) believe that AI and ML will be very or somewhat important to their future success. But software vendors are already starting to embed native AI and ML capabilities into their applications to help SMBs solve specific problems, including challenges that professional services firms struggle with.
Firms that understand and take advantage of these applications will quickly realize that although the technology behind AI and ML is very complex, it can be delivered in a very practical, valuable manner.
Professional Services Objectives and Challenges
All companies want to improve efficiencies, productivity, and customer experience. Due to the labor-intensive nature of the business, professional services share some more specific objectives as well, such as:
- Improving employee utilization rates.
- Avoiding scope creep, revenue leakage, and drains on project profitability.
- Creating more profitable projects and improving margins.
- Retaining top employees.
- Improving customer satisfaction and retention.
But achieving these goals isn’t always easy. Recalling, reconstructing and manually entering timesheet, expense, and other data results in late, inaccurate, and incomplete information about project costs and resources. This leads to issues with revenue leakage, over-billing customers, and uncertainty about project costs, resources, and profitability.
The Importance of the Cloud in Making AI and ML Capabilities Accessible and Valuable
Multi-tenant cloud-based application platforms with embedded AI and ML capabilities are well-positioned to help professional services companies solve these problems. The combination provides unique advantages to help optimize the value of AI and ML, especially for small and medium businesses (SMBs).
Cloud-based business solution vendors can pinpoint specific business processes and streamline them with AI and ML. Then, they can deliver a new, easier, and better way to perform those tasks within their applications—which users already know how to use.
Next, data flows from the AI and ML enabled app into the bigger cloud platform in real-time. This ensures that the information in the system is up-to-date, is trustworthy, and provides a consistent version of the truth for all users.
In addition, multi-tenant cloud platforms can aggregate and anonymize data from thousands of customers. The more data that intelligent AI and ML applications can harness, the faster the learning curve. As they get smarter, they can provider people with better insights.
Putting AI and ML into Action with Sage Intelligent Time
Sage’s recently announced timesheets application, Sage Intelligent Time, provides an excellent example of how this combination benefits professional services firms.
Sage Intelligent Time replaces tedious, error-prone manual processes of searching through calendars and emails and entering hours and activities into the system with an intelligent personal time assistant. The AI and ML powered personal assistant automates time entry and accurately captures billable hours, which then flow into Sage Intacct’s cloud financial management system.
The assistant alerts users to complete time sheets and suggests what they should enter based on activities during the work week. Users review the suggestions, make needed adjustments and drag the information to their timesheets—from both PCs and mobile devices.
Users have complete control over what types of information they provide to their personal assistant, as only the user can see the activities the assistant suggests. Users can review and make any adjustments they’d like before entering into the system.
The app frees up managers from having to nag people to get their timesheets done and decreases the likelihood of hours slipping through the cracks. This gives managers more accurate employee utilization data, which they can use to make better decisions for both current and future projects.
Since Sage Intelligent Time is built directly into Sage Intacct financial management, timesheet info flows directly into the system and into the hands of financial team as well—reducing the need hunt down overdue timesheets to close the books.
AI and ML Use Cases for Professional Services Will Continue to Unfold
Professional services offer fertile ground to put AI and ML capabilities to work. Other relevant applications and use cases are already surfacing, such as those that can:
- Suggest “best-fit” employees for a project based on project requirements, skills, and availability.
- Recommend supplemental training employees need to work on a project.
- Scan external databases to find suitable external candidates for a project based on skills, availability, location, and other criteria.
- Predict which employees may be a flight risk and prescribe actions to better engage and retain talented employees.
- Automate contract review using image recognition to identify patterns and anomalies in credit agreements.
- Provide customers with intelligent self-service options, such as bots, who can listen to customer needs and respond to requests—freeing up employees for more complex, higher-value tasks.
Professional services organizations need the right combination of people, processes, and technology. AI and ML capabilities, embedded in multi-tenant cloud platforms and applications, can help firms manage this combination to optimize profitability, productivity, and outcomes.
Today, many employees must perform routine, redundant tasks, such as timesheet entry. AI and ML powered apps can take these tasks off their plates and give them more time to spend on delivering successful, profitable project outcomes.
At the same time, these apps also provide decision-makers with much more consistent, accurate and up-to-date information to manage projects more proactively. As these applications get smarter, they also have the potential to provide recommendations, identify new opportunities and uncover potential risks that they may not otherwise have come to light.
Professional services need to lean in now to understand the potential of these technologies and start to put them to work to execute more successfully on people, process, and project goals.