When professional services firms don’t choose the right opportunities to pursue, or don’t select the right people and practices for particular client projects, it results in wasted resources and other inefficiencies. Even worse, it can lead to unsatisfied customers, damaged reputation and lost business.
Get a handle on your people
Historically organizations had to rely on their experience and the availability of individual managers to cope with these challenges. People are the central asset within every professional service organization. Creating the right environment for these individuals to network efficiently is therefore one of the most important goals. But this model has its limits. The right people are not always available, knowledge is spread over dozens of locations and departments and the organization succeeds or fails according to each individual manager’s own network.
An innovative strategy for addressing these challenges utilizes statistics to inform professional services project plans. Machine learning is a type of artificial intelligence (AI) that gives systems the ability to learn without being explicitly programmed. It evolved from the study of pattern recognition and computational learning theory in AI.
Embedded machine learning systems can tap directly into the organization’s stored business and project history to identify valuable patterns and insights. They can identify similar historical project experiences to support planning decisions or simply link people together; they can find successful planning patterns that can be reused for new opportunities. This can help to improve planning efficiency, risk management and overall project results. Automation of repetitive tasks also frees up valuable time for key resources, which they can better spend on value-generating tasks.
Professional services analytics has come of age
Machine learning involves the use of algorithms that can learn from and make predictions on data. Along with predictive analytics, machine learning applications have never been more relevant in the enterprise than today, because vendors are moving from building generic tools to applying applications for a specific purpose.
In an age of ever-growing data these intelligent technologies are the key to generating value and they are soon to become a significant competitive factor in every market.
That’s exactly why professional services firms need to deploy the kinds of analytical solutions that can give them an edge over their competitors.
Mitigate project risk almost entirely
Emerging new solutions, leveraging machine learning, enable professional services project managers and teams to review past projects and predict which combination of people and practices will be most profitable and successful. They can also identify risk elements before they impact project performance.
These applications provide managers with a fast and easy way to assess the financial performance of projects and calculate a project forecast based on a firm’s historical project data. They help businesses identify historical project experiences and turn them directly into project estimation and planning input. Managers can base new project bids on actual costs, revenue and profit data from past projects and plan them accordingly to ensure success and profitability.
Through the use of powerful data analytics, companies can assess prior projects, including timesheets submitted while on the project, and forecast what will happen with future endeavors, given a certain set of criteria. Managers at the earliest stages of project planning can identify similar historical projects and run predictive analytics against these experiences.
Self-driving project forecasts
Among the reasons many firms do not conduct project forecasts today is that they are often time-consuming and complex. New integrated solutions can speed the process and reduce the complexity significantly. Combined with predictive analytics they can add an additional element of foresight to the picture. As a result, project managers will be more likely to submit project forecasts on a regular basis, and firms will have a clearer picture of project performance and risk.
Using these kinds of solutions can improve risk management, project tracking and cash flow management through more frequent project forecasting.
Machine learning based solutions are adding predictive capabilities to these traditional metrics systems but the day-to-day impact is just as important. They not only deliver information to support user decisions, they can also be used to automate repetitive decisions and related project parts. Just like the most advanced cars, they are becoming self-driving. By doing so, they are combining new insights with the next level of process efficiency, resulting in an intelligent and productive business application user experience.
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