If one were to believe the buzz it would appear that artificial intelligence (AI) has recently become that silver bullet most organizations have been looking for to achieve a competitive edge. For those of us who have followed and participated in technology trends over the years, it is clear that while it has now come to occupy attention that is front and center, the notion of AI and related techniques have been around for years. In my current role, I am asked fairly frequently about AI, related technologies and how best it can make a difference; often I am also asked whether one really needs a full-fledged platform when needing to leverage these technologies. So let me attempt to break it down in the context of what we are doing at Infosys.
First, with 35 years’ experience across many industries and geographies, we know services get easily commoditized so carving out differentiation is critical. This is a step forward on behalf of all our clients. How so? If we can bring to the table a set of technologies in a coherent and cohesive manner that have been tried and tested to address the issues of the day for a client, that gives them a tremendous boost toward gaining that competitive edge they have been seeking. This is very different from the old approach of a one-off effort to cobble together relevant technologies and capabilities in an attempt to solve the problem.
Second, a smart next gen platform not only drives unprecedented efficiencies but also becomes an enabler for new areas. Let me explain with a couple of examples. For instance, in the realm of managing application services, we are familiar with the notion of L1, L2, L3 support. In our experience, there are many inefficiencies in how this is handled traditionally. As an issue goes through the 3 steps there is significant loss of context in each step which leads to inefficiencies. Moreover, often companies outsource one or more steps to third parties which compounds the problem further. Thus while the need for a proper handshake here is critical, there is unfortunately often some duplication of effort involved. A platform, with unified capabilities, that spans across this set of steps can eliminate redundancies and most inefficiencies. Another example that we commonly encounter is the inefficient processing of transactions in a, say, business process outsourcing type of scenario. At one client we found that a single individual had to go between 70 – yes, 70 – different screens to complete one transaction. Often there are manual steps involved as well that are error prone. Again, a platform that can enable end to end processes eliminating the redundant steps can alleviate such situations.
Data processing and analytics, automation, and AI are not capabilities unique to our platform. The uniqueness is in how we approach the matter of unifying these capabilities and bringing to bear for our clients the full force of the platform.
To begin with, the heart of our platform is knowledge. Yes, knowledge. One definition (Merriam-Webster) of knowledge states: “knowing something with familiarity gained through experience or association, or apprehending truth or fact through reasoning or cognition (where cognition is defined as: of, relating to, being, or involving conscious intellectual activity such as thinking, reasoning, or remembering)”. We have found that to deploy intelligent systems in an organization there is a need to harness and leverage all that is known about the processes, existing solutions, and thoughts about future growth. Since this knowledge in organizations is often scattered across systems and individuals we need a systemic way to capture it and then use it to create new solutions or renew existing ones. This is where we begin.
The challenge is the ability to represent knowledge in a structured manner. We use a comprehensive ontology model (OWL based), which makes the knowledge model readable by machines to perform correlation and inferences. Using a big data repository to store all the information generated from various fragmented tools in a knowledge base, we then apply machine learning algorithms, artificial intelligence techniques such as natural language processing and clustering to extract knowledge models to apply on various domains and industries. Knowledge models are constructed for various complex scenarios, such as business domain, software, machines, inventory, operational process, system events, design and development, etc. The models form the basis for correlation, providing structured and insightful ways for analysis, diagnostic and prognostic, and recommendations on operation, business, production, and product lifecycle management. The knowledge can then be consumed within various channels through chatbots, APIs and automation tools. As patterns within and across models emerge, it leads to learning that needs to be internalized by the organization. This makes it possible to automate smartly – whether it is simple robotic automation or automation that is more cognitive and predictive in nature. As this cycle continues, we find that new facts and recurring events come to light which provide additional opportunities in areas that are unprecedented. AI capabilities are employed in learning and updating/enhancing the knowledge in order to further improve efficiencies and build new solutions. This cycle of processing data for knowledge, automation, and learning with AI is not one that is of a stop-and-go nature. It must be ongoing and must have a pervasive reach into all aspects of knowledge gained, stored, and learned.
This is the primary focus of our AI Platform, Infosys Nia.
Since our approach to the platform employs knowledge as a foundational pillar, our approach is one that seeks to combine the power of software with the ingenuity of people. To give this shape we rely on core techniques and technologies that include knowledge engineering, automation, machine learning, natural language processing, reasoning, user and data interaction, and learning.
Capabilities include OCR, Advanced Machine learning, Infrastructure Management, and Robotic Process Automation. The platform is also flexible and modular in nature. In other words, instead of a loose collection of capabilities, our clients can take advantage of the functionality as a well-integrated set of capabilities within a platform leading to better interoperability. Of course, the platform gives you the freedom to employ only those aspects that are applicable to your context.
The capabilities and technologies to address the challenges of our times exist. With the proliferation of data and the ability to compute and process it cost effectively, we can now have increased sample sizes. These have helped us get better at learning and employing smart algorithms. But true differentiated value with greater efficiency comes from employing the strength of a platform to address these needs. Infosys Nia, our next gen AI platform, is built with a conscious design principle that knowledge – deep understanding with context – is fundamental in solving problems. Working with it in tandem, it has state of the art data processing and data analytics capabilities and the tools necessary to smartly automate processes in the most relevant manner. It is important to note that deep understanding of context and applying systems in the most relevant manner are not mere platitudes. They represent how we get to intended outcomes. Infosys Nia is the platform that strives to deliver Purposeful AI™ to an enterprise.
Please join the conversation here and let’s work together for a more creative and purposeful future.