With organizations across the globe looking to harness the potential of Artificial Intelligence (AI) and Automation to transform their businesses in the digital age, a plethora of technology companies are building capabilities to help accelerate the enterprise digital journey. The timing couldn’t be better because the new advancements are coming in at a time when there is an unprecedented openness to ‘new ways of working’. Traditional competitors are now collaborating to complement capabilities to help clients realize higher value from their investments. For example, Amazon and Microsoft are collaborating on Gluon, a deep learning library that will help developers build machine learning models more easily and quickly. The tech giants also recently announced a partnership to integrate the Microsoft Cortana and Amazon Alexa digital assistants.

The pace of data deluge today (more data has been created in the past two years than the entire history of the human race) is resulting in rapidly evolving AI advances, with enterprises building deep specialist capabilities based on the nature of data within their reach. However, the challenges regarding scalability and adoption invariably impede the journey towards becoming an AI-infused enterprise. Which begs the question – are we looking at these new technology advances with the right lens? When it comes to AI-powered digital transformation, is it just about the ‘right technology’ or is there something much more significant in play?  

The Automation Ecosystem Requisite 

A collaborative automation ecosystem approach is essential to address the realities of today’s enterprise. Here’s why.

1. Organizations are working with a hybrid automation portfolio, having invested in a number of providers over the years for automation projects. The approach worked well to leverage specific strengths of each technology provider. With this ‘mushrooming’ of automation engagements, however, the need to centrally govern and manage them becomes paramount which requires a certain ease of integration across technologies. The good news is that providers are also starting to realize that there is much to learn and gain from a collective effort to maximize the customer’s value rather than going at it alone.
2. Customers’ asks are maturing, with increasing understanding of what automation can do for them, leading to more pointed questions and expectations. What they are ultimately looking for is to get the best of all available technologies to amplify outcomes.
   
3. Some organizations have already chosen a partner for their automation journey. While the partner succeeds in addressing a few problems leveraging their strengths, they are still unable to truly deliver value for a whole host of other potential automation opportunities that lie outside their expertise, raising questions about the return on investment.
   
4. Freedom of choice and speed are critical in the quest to implement the best possible solution and cannot afford to be hindered by ‘service lock-in’ concerns. The freedom of choice, the freedom to quit when unused and the freedom of self-service are critical to success.
   
5. Time to value is key. Customers shouldn’t have to wait for a partner to build something that another provider’s portfolio may already have. Even where a solution doesn’t already exist, a collaboration to bring together capabilities may still ensure faster time to market and time to value for the customer.

To illustrate what is possible through an ecosystem approach, let’s look at some here-and-now examples that Wipro is driving.

Integration with a third-party conversational engine to enable a conversational interface. With the need for increased workplace productivity, an intelligent digital assistant that can interface with the user and respond to voice commands is becoming a popular ask from enterprise clients. The digital assistant, let’s call him Buddy, will act as the single conversational interface which gives users across business and IT processes a seamless experience.

If a user tells Buddy “I want to know the status of my tickets”, the conversational interface understands the user’s intent, searches for the relevant business / IT tickets and looks for their status. For this workflow to be seamless, the solution would have to work well with a host of traditional and cognitive systems, leveraging a heterogeneous architecture that spans across the organization’s vast IT landscape, including various ITSM ticketing tools and applications.

Wipro recently implemented this for a client by integrating the Wipro HOLMES^TM artificial intelligence platform’s Classifier engine with the client’s existing conversational interface. Buddy can now navigate the diverse data environment, answer queries with data from relevant sources and perform requested data processing. Most importantly, Buddy saves time, and the business user now has more time to evaluate, prioritize and act on the insights the data-driven world has to offer.

Resolving queries from field staff in real-time with the cognitive search. Field staff in the energy and utility domain currently grapple with a cumbersome, time-consuming process to get their queries answered to a limited level of accuracy. With the Natural Language Query (NLQ) capabilities brought in by Wipro’s Automation Ecosystem and Wipro’s process orchestration and architecture strengths, the users will be able to interact through multiple channels with a solution that understands their intent and can increase the relevance of its answers over time.

The solution understands the user’s query (“Find wells and associated data for block BL32”), carves out relevant data from numerous sources, and creates a table to represent the data for easy user access and readability.

The next two examples are a demonstration of the “Art of Possible” in an automation ecosystem driven approach. 

Marketing Spend Optimization and Personalization. The customer journey today is spread across multiple devices and hundreds of moments. Thanks to technology giants like Apple, Google, Facebook and Amazon, consumer touchpoints are proliferating, ranging from chatbots and smart speakers to wrist watches and connected cars. Marketers need to make the right moves to ensure their brand can live with the consumer across channels and interact with them intelligently at the right time and place. 

The idea that a single intelligent solution can keep up with this expanding need is ridiculous. The best strategy is to have control over data from varied sources, embrace APIs and ensure that new and emerging technologies can work harmoniously while being centrally governed.

AI outcomes enabled through Crowdsourcing. Crowdsourcing platforms are a robust ecosystem that can accelerate the learning of AI, and if leveraged well, can solve problems on an unimaginable scale. The Foldit project, which lets gamers play around with the tertiary structure of proteins, resulted in a crucial discovery in HIV research – something that scientists had been working on for over two decades was achieved in less than a month by passionate gamers. Dividing the problem in a manner that allows man and machine to work in conjunction and promoting the social evolution of an approach to solve it, is the mantra of these platforms. Let’s not forget here that those who are contributing to the solution need not be subject matter experts. How their actions result in solving a more prominent business problem is invisible to them.

All industries are looking up to Artificial Intelligence and cognitive services to improve experience, efficiency, and economics of their operations. The ‘Think Big, Start Small’ approach has been tried and tested with customers at various points of the adoption and maturity curve. It is important for service providers to look ahead, collaborate and actively build an Automation Ecosystem to leverage both home-grown and partners’ IP under one roof and accelerate the enterprise AI journey.