Jul 05, 2018 | Mary Grace Glascott
ROI Roadmap for Your AI Strategy
The Artificial Intelligence (AI) revolution is here – implementing an AI solution is no longer a nice-to-have for a business, it’s a need-to-have. According to our Outlook on A.I. in the Enterprise Report, AI adoption grew over 60% from 2016 to 2017. With almost ⅔ of enterprises utilizing some form of artificial intelligence-- be it natural language processing (NLP), machine learning, image recognition, natural language generation (NLG), or some other machine-enabled capability meant to mimic human abilities-- it’s clear that businesses understand the potential that AI can deliver. But do they understand the value?
Not necessarily. In fact, only 31% of respondents in our Outlook on AI survey cited that they are either not tracking ROI or not seeing returns on their AI investment. To reverse this alarming trend, we’ve put together a quick ROI roadmap for your AI strategy. Of course, return on investment can be tricky to track because there are so many metrics that could be assessed, but in our experience working on NLG implementations for some of the largest enterprises worldwide, we’ve identified these three categories that truly capture the overall value-add.
Time is a precious resource, and given the opportunity costs involved on the time spent on manual, mundane tasks, time is extremely expensive. There will always be activities that are very routine, yet important, that need to get done, but they do not require a ton of analysis, strategy, or higher-level of thinking. These tasks are the sweet spot for AI and some sort of robotic process in place. If you can automate a task or process that is very time consuming, you leave more time in your day for more high-value and creative tasks.
By automating time-intensive reporting processes, for example, we helped a leading logistics provider cut down time spent on reporting from 20 hours to 20 minutes.
A report or task that runs at high volume and frequency, and often times whose output needs to be delivered to various stakeholders, is a perfect candidate for an AI solution that delivers scale. This goes hand-in-hand with time – if you do not have enough time in your day to reach all your clients or get every task done because there are simply too many, implementing an automated workflow process will lighten your load.
A great example of this is investment research reporting. If you have a team producing an analysis on thousands of institutions, it is almost impossible to ensure the output is consistent and timely, particularly as the underlying data constantly changes. Typically, research groups are forced to focus on the top 10% or 20% of institutions. Through automation, however, you can deliver the same attention to the 80% of your audience as you do to the top 20%. By auto-generating well-written investment research summaries for thousands of companies on-demand, for example, we helped one financial services company increase its coverage by 300%.
Competition is more intense than ever. Tracking the turn-around time is important for a business to assess its ability to get their product or service out the door as efficiently as possible. To have a solution that allows you to reach your clients/customers more quickly provides a tangible ROI that is often easier to measure than the other categories listed.
Take portfolio commentary reporting, for example, a process that typically takes 2-3 weeks at the beginning of each quarter to produce. By using Quill to write a by-standard, resource-intensive client report, we now have the capability (or opportunity) to get the asset management firm, Clearbridge, get to the market faster -- saving 60 labor hours in the first 2 weeks of every quarter.