In my last blog post I briefly explored three ways that we are using artificial intelligence (AI) to transform operations:
Focusing on specific business outcomes is central to adopting an as-a-Service model for buying IT and business process services. A focus on business outcomes shifts the enterprise mindset from consuming services on a transactional basis to zeroing in first on an enterprise’s most important business needs, and then to figuring out which processes and technologies to deploy to meet those needs.
So let’s take a look at how we can wield AI to improve business outcomes.
Using intelligent automation
One important technology in an as-a-Service context is intelligent automation—something that can be considered a stepping stone into the world of artificial intelligence. Here, we automate routine or repetitive tasks that involve analysis or crunching of data. In these cases, we use intelligent automation to help us perform these tasks at lightning speed and with few to no errors. So, increasing productivity and capacity, improving efficiency and reducing cost of operations are some of the first business outcomes to be expected from AI. The numbers can be pretty impressive, by the way. For one major high-tech company, we used intelligent automation to help their Finance organization close their books faster, improving productivity by 45 percent.
Going up the AI sophistication scale
But things get a little more complicated when so-called “unstructured data” is involved—things like emails, analyst reports or news items. Here, more sophisticated artificial intelligence tools can ingest this unstructured data, analyze and categorize it, report on it, and route it appropriately.
Accenture’s internal IT team is developing this kind of solution for its IT help desk. The Accenture Intelligent Email Advisor analyzes emails using natural language processing, understands the requestor’s intent and retrieves relevant data from systems to help the team resolve issues. It can also predict the consumer satisfaction score by using machine learning and advanced statistical analysis. It leverages natural language generation to produce a relevant response for email closure, which reduces the number of handoffs for email communications. In its current form, the Accenture Intelligent Email Advisor can detect 55 languages and classify emails with more than 90 percent accuracy.
Accenture is using similar AI techniques to help one high-tech and software firm interpret supplier inquiries and execute follow-up processes automatically, which is designed to improve customer service by accelerating response times and increasing accuracy. Accenture will also apply advanced predictive and prescriptive analytics to provide data-driven insights, further improving the effectiveness of compliance activities and increasing the accuracy of global cash forecasting.
Getting strategic with AI
Another AI solution we are applying with several of our clients is the Accenture Procurement Advisor which creates market intelligence reports that people use to formulate market development plans and supplier development strategies across multiple industries and geographies. This advisor prepares reports by combining data mining, text and data-analysis skills to analyze publicly available digital information including financial publications and business news, with far more coverage and 10-times faster than human teams.
One implication of these stories is that as we increase the sophistication of AI technologies, we can help clients deliver more sophisticated business outcomes: increasing customer satisfaction, formulating market development plans, accelerating response times, improving compliance and increasing accuracy of forecasting.
As time goes on, one can expect to see AI applied to produce these kinds of business outcomes not only more effectively, but faster as well.
Top 5 technology trends 2017: technology for people
As-a-Service: plug-in, scalable, consumption-based services