BOSTON, Nov. 20, 2018 (GLOBE NEWSWIRE) -- The hype around artificial intelligence (AI) today is exciting but it also creates a lot of confusion and skepticism in the minds of both data scientists and their business colleagues. Despite its promise, and its growing adoption, there is still too little users can point to in terms of real business results. This is especially true when it comes to the plethora of unstructured and document-based content that makes up more than 80% of the data in most enterprises.   

Working with clients in banking, insurance, industrial services and manufacturing, Indico, a provider of enterprise AI solutions for intelligent process automation, has identified some of the key considerations and characteristics that separate successful AI initiatives from unsuccessful ones. It has also outlined a framework to help enterprise users evaluate opportunities within their own organization and increase the likelihood of tangible and measurable ROI.

“If we put aside all the hype around AI, we’ve seen tremendous progress in the ability to deploy the technology to automate manual, document-based work processes and drive valuable business results,” said Tom Wilde, CEO of Indico. “If we understand the real capabilities of these tools, and work with the business to identify the right use cases and workflows to apply them against, we can generate significant ROI in a tangible and practical way.”

Three critical success factors for any AI initiative
Indico has identified three core factors that every initiative must address to be successful:

  1. Data Access & Training Data - Users need a well-formed set of inputs and outputs of sufficient volume to make even the most basic machine learning problems tractable. And the training data must provide quality examples of the desired outcome. Data requirements vary based on the complexity of the problem, but a standard dataset size for training a model can be between 10,000 and 100,000 labeled examples. This is typically where many implementations fail. Fortunately, newer approaches to AI use frameworks such as Transfer Learning to dramatically reduce the amount of training data required - in some cases, to just a few dozen examples.
  2. Data Science and Line of Business Collaboration – As is the case in most technology initiatives, business users and technical staff need to collaborate effectively to produce the desired outcomes. This is especially true with AI where the subject matter experts (SMEs) play such a critical role in the definition of success, and the underlying technology is so complex. With AI, the importance of data science expertise is typically well understood. However, the role of the business SME is often undervalued.
  3. Identification of High-Potential Use Cases - The most vital component for realizing ROI is a clear understanding and definition of a desired outcome. This enables the project team to work backwards in terms of identifying the steps that can be augmented, enhanced or automated, the data available, and a set of previously identifiable outcomes that can be used for training the models. Is the goal to fully automate a process, or augment a manual process? These carry different considerations and ROI implications.

Where to Find ROI
Use case ROI is driven by an understanding of the goals of the specific use case. These typically take one of the following forms:

  • Accelerating Existing Transactional Processes and Workflows - The process being augmented or automated already has a transactional nature and a specific dollar amount tied to it in the form of hours of labor or process cycle time. These can be translated into a set of hard costs or opportunity costs. It’s rare that any AI-based approach can completely automate an existing process, but rather is likely to augment a large portion of it.
  • Increasing Capacity for Overburdened Processes - AI can be very useful in classifying and “routing” high volumes of unstructured content such as inbound communications or service requests. In addition to expanding the capacity of the current process, ROI also comes in the impact on employee and customer satisfaction by making it easier to respond to these communications more quickly and accurately.
  • Enhancing Existing Products or Creating New Products with AI - This can be a challenging ROI assessment because it is more speculative and lacks a reasonable comparison. One way is to calculate what the price of such a process would be if it were implemented completely manually. While it’s inaccurate to claim any improvement here as true ROI, it can be very helpful in determining the amount of leverage derived from the addition of AI.

About Indico
Indico is a provider of Enterprise AI solutions for intelligent process automation. Our focus is on helping to automate tedious back-office tasks, improving the efficiency of labor-intensive document-based workflows, and extracting valuable insights from unstructured content, including text and images. Our breakthrough in solving these challenges is an approach known as transfer learning, which allows us to train machine learning models with orders of magnitude less data than required by traditional content analysis techniques. With Indico, enterprises are now able to benefit from the dramatic advantages of machine learning in a fraction of the time. For more information, visit.

Media Contact:
Tim Walsh, for Indico