David Burden explaining recent developments around Virtual Humans
Author of Virtual Humans: Today and Tomorrow
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Automation is seen as the panacea for improving agility, effectiveness and efficiency of businesses. Unfortunately, the traditional approach to automation, often referred to as “robotic” or “run-book" automation, has failed to deliver scalable or sustainable benefits for many businesses. This is because robotic automation, which performs an activity by executing a fully-prescribed procedure, works well when there are a small number of standard ways of performing each activity, and the procedures for performing activities do not change over time. Both of these assumptions don’t hold true in the case of ever-changing business contexts and operations. To deliver scalable and sustainable benefits, automation must become intelligent and adaptive. This is where AI comes in. Using AI, one can design systems that can think and act like experts. AI-empowered systems can adapt to diversity and changes, and thereby deliver quick and sustainable value. Further, this enables automation of “knowledge work”, and not just repetitive activities; reduces dependence on tacit or tribal knowledge; and reduces operational risks. This also changes the role of people, from doers of work to creators and curators of knowledge. All this makes businesses agile (improve the speed of taking and operationalizing decisions), resilient (become proactive), and efficient (reduce cost).
Broadly speaking, I feel that the industry is somewhere in the middle. For some of the domains, solutions have matured to a point that they are ready-to-implement, while for some others we are at the early stages of experimentation. Many of the core technology pieces needed to implement AI for businesses exist. For instance, machine learning and deep learning algorithms for mining structured and unstructured data sources allow us to learn an enterprise’s context, time series and graph analysis algorithms have become accurate enough to derive recommendations, among others. The biggest challenge is in connecting and contextualizing the insights derived from these technologies to drive business outcomes.
Converting insights derived using AI techniques into actions requires deep knowledge about the business domain and the enterprise context. Without this, the outcomes of AI technologies remain as mere observations or suggestions. People who understand the business functions and domain are generally not experts in machine learning and AI, and vice versa; this makes it difficult to train AI-led systems with domain knowledge. Benefits, and hence adoption of AI technologies, will skyrocket only once the techno-functional integration becomes effective. Hence, rapid acquisition of knowledge about a business domain and context is, perhaps, the biggest hurdle in adopting and benefiting from AI technologies in businesses.
At a broad level, adoption and experimentation with AI technologies is driven by four objectives: 1) Reduce costs; 2) Improve experience; 3) Improve agility; 4) Reduce operational risks. The prioritization, however, depends upon the industry and the stakeholders. For instance, in the case of customer-centric industries (such as, financial services, retail, telecommunications, travel and hospitality, etc.), stakeholders responsible for driving business growth and differentiation are pioneering the use of AI technologies to improve customer experience through real-time and personalized services. For B2B industries (e.g., transport, logistics, supply-chain, manufacturing, etc.), AI technologies are getting leveraged to improve efficiency and agility. IT stakeholders in most enterprises are starting to experiment with AI technologies to reduce costs and operational risks.
We have launched ignio for IT operations, a cognitive system that applies AI technologies to convert IT operations services into intelligent software. It comes with pre-built knowledge for wide range of common enterprise IT technologies; this knowledge, packaged as ignio Capabilities, is available from the ignio Store. These Capabilities can be downloaded and activated onto ignio in minutes. Using this knowledge, ignio thinks and acts like an expert. In particular, ignio self-learns and profiles an enterprise’s context, derives prescriptive recommendations and automates operations without explicit instructions. This allows ignio to improve the speed of decisions and actions, reduce cost by eliminating manual work, and make businesses proactive and resilient by predicting and eliminating most operational risks. Thus, ignio for IT operations lays a strong foundation for building an AI-empowered business.
We are extending the coverage of ignio from information technology to business technology, and from IT operations to business operations. We expect to launch several industry solutions, powered by ignio, in the near future.
At The AI Summit, Harrick Vin will deliver his presentation on Scaling AI for Business Operations.
The AI Summit is the world’s first event dedicated to Artificial Intelligence for the business world. For more information, and to join us on 5 May at the Four Seasons Hotel, London, visit: theaisummit.com
Author of Virtual Humans: Today and Tomorrow