What if I told you I have a new member of your sales team that, after an initial round of training, will be able to look at an RFP, decide if it’s a worthy opportunity, manage its response from start to finish… and won’t be on your payroll? This team member would only be paid in data, which it would use to further improve its management of RFPs and pipelines.
It’s called AI and machine learning, and it’s the next evolution in asset management that you need to know about.
The success of many asset management firms depends on the complicated and time-consuming process that is RFPs, RFIs, and general requests. Each one is unique and based on varying degrees of information and effort needed. As it stands, it’s likely that your firm doesn’t respond to every single one due to time and bandwidth constraints, but what if the ones that end up in the slush pile could have been major opportunities for your asset management company?
The choice to move forward with an RFP or toss it depends largely on the gut of the person reviewing it. Perhaps the salesperson has worked with the client before or has a good relationship with them, or your asset management firm can easily deliver what the RFP has outlined and your strategies align. There’s a lot of business criteria that comes into play when making these decisions, but there are a lot of human factors as well. This qualitative information is not the best for anyone to use and make business decisions. Plus, once you do decide to pursue a request, developing the response can require hundreds of man hours to research, verify, and complete… all because someone had a gut feeling about winning a client.
There’s a better way with AI and machine learning for asset management firms.
Using Data to Educate Your AI
Think of AI as any new team member: it needs information about your clients, processes, and business to get going-- it just doesn’t need as much time as a human team member. Machine learning starts with collecting data from your team entering information, but that doesn’t mean it requires data entry! Sure, the system can read structured data off a spreadsheet, but it can also read unstructured data off of a scanned document or e-mail. It will read that information, look for patterns, and use those patterns to make decisions based on what you’ve told it you believe to be the overall effort for winning that opportunity. With these advantages, your asset management team will immediately begin saving time.
Your AI will optimize for win percentage and AUM, telling you which opportunities should have the best close rate or the highest ROI. Over time and as you continue to feed it more information, its richer data set will become better at tracking your wins and losses. It will look at what’s driving a number, help you make smarter decisions, and refine your model.
With experience, your AI will empower you to improve the chance of winning a deal by suggesting ways to change the criteria. This will allow you to go back to your consultants with data-driven recommendations, you close more deals, and that consultant is more likely to work with you again in the future.
AI and machine learning can ingest tremendous amounts of data, learn from it, and make recommendations faster and without the bias others on your sales team would. Yes, you must feed data into the machine. This could be data already sitting in your system, but chances are that someone will need to set up and input more into the system. The more you give it, the more you’ll get out of it in the end.
Curious about AI and machine learning for asset management?
Ready to see machine learning and learn more about how it can improve your business? Enhance the capabilities of your team and back up those gut instincts with an AI engine of your own.
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