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Bots in the enterprise: cognitive solutions and the potential for artificial intelligence: Part 3

Bots present a fundamental step in the evolution of tech user experience and could help drive advancements in this area in the enterprise.

Enterprise software, used in global organisations, is characteristically bloated, misaligned with end user expectations, has resulted in vendor dependencies and doesn’t evolve quickly enough to keep up with advancements in the workplace. With the consumerisation of IT, where consumers comfortably use technology like smartphones, there is the expectation of intuitive experiences and simple to use interfaces – which is not reflected in the technology solutions these consumers use at work. The advent of mobile, iterative innovation approaches, along with the adoption of agile development practices and user centric design are helping change this.

Bots present a fundamental step in the evolution of tech user experience and could help drive advancements in this area in the enterprise. In ‘The rise of bots in the enterprise’ we covered the opportunity that the technology presents to the enterprise. We explore how automation and the collaborative capabilities of bots will help improve the user experience of enterprise software and in turn enhance productivity across the workforce.

If we take knowledge workers, as an example, much of their day can be spent on unproductive tasks like locating files, compiling research, scheduling meetings and a whole host of other mundane tasks. If these tasks were to be automated, this would help knowledge workers focus on their core tasks and help increase productivity and improve job satisfaction. Bots, at the primitive end will accommodate repetitive tasks with a limited set of outcomes.  

An example might be the user asking the bot to schedule a meeting in their calendar. With some additional intelligence these same bots could lead a conversation based on certain contexts such as time of day, in or out of the office or in a given location; which assists the user and helps make the scheduling of the meeting more relevant for the user, based on their movements on that day. It is likely that initial bot implementations will be used to carry out mundane tasks, which will reduce the load on the user to a few simple commands.  

Primitive bots, however, are not just limited to a set of responses based on correct user input. Since the server that handles the responses is standalone, it can be extended to return information from other systems. A user could type in or speak keywords to perform a search of a product database or content repository, for the bot to respond with the latest content or invite the user to continue the conversation. Imagine an Operations Director, from a construction company, urgently needs a replacement heavy duty vehicle following the breakdown of one their vehicles on a very important site. He asks the bot to look up where he can off hire a replacement vehicle from. The bot selects a fleet management provider based on a previous transaction. Then using the location the Ops Director is based at, along with a selection of different types and configurations of vehicles that have been used on the site, the bot presents various options, for example tipper or short wheel base. The Ops Director simply confirms a selection and the bot carries out the action and has the vehicle delivered to the construction site.

On the other end of the bot scale is Artificial Intelligence (AI). Advancements in AI are helping make the technology a commercially viable option for companies to leverage to drive returns. There are already a few players who have made impressive progress in the AI space, including Google DeepMind, Siri, Cortana, Amazon Echo and the anticipated Viv engine, which focus on a providing natural language processing and understanding. The premise here is that conversation will power the future solutions, but there certainly is question around user adoption for some of these approaches.

Solutions like IBM’s Watson are using AI and machine learning to leverage knowledge to help deliver business outcomes. This sort of cognitive solution can learn from experience and then use predictive capabilities to facilitate better decision making and business outcomes for an organisation.

There is a middle ground appearing where AI providers are taking out some of the heavy lifting and making it easy to leverage these capabilities within dedicated interfaces.  These natural language processing and understanding models, hosted in the cloud, can be used by apps to leverage the AI technology. Once again this will lower implementation barriers. However, this approach will still require building a user base and driving user adoption; which potentially makes the messenger apps a more attractive proposition in the short term.

The opportunity for enterprise bots is vast; as is the potential for the technology to gain mass adoption at a faster pace than we are seeing in the consumer market. Bots will help streamline operations, automate tasks and eventually start using cognitive learning to transform the business decisions we make, the speed at which work can be delivered and possibly the very nature of our jobs. Automating mundane and repetitive tasks is the first step, but involving the users at the early stage is key to success. Bots present an exciting opportunity and the barriers are low to experiment and explore; the enterprise really needs to embrace it.

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