Enterprises of all kinds can expect to see shifts in modern IT workflows as human teams work more collaboratively with AI, maybe without even knowing it. The good news is that humans are excited about this shift. A recent Juniper surveyOpens a new window found that 95% of respondents believe their organizations would benefit from embedding AI into their daily operations, products and services. Additionally, 88% of respondents say they want to use AI as much as possible.
While the future looks bright, it hasn’t always been that way, as chatbots incapable of understanding even simple prompts have long been a source of user frustration. However, like all technology, chatbots are evolving. The industry has moved beyond the rudimentary first generation of chatbots, which were rife with inconsistent user experiences. Now, the next stage is on the horizon.
With the addition of AI and natural language understanding (NLU), chatbots are evolving into something new —full-fledged conversational assistants. In contrast to early chatbots, the latest conversational assistants incorporate user intent and context into responses. This innovation is only getting started, setting the stage for further advances in enterprise IT user support and efficiency.
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Making Conversational Assistants Helpful
For enterprise IT and networking teams, advances in NLU and AI are empowering the next level of conversational assistants. Sometimes also referred to as virtual network assistants (VNA), they go beyond simple, canned responses to providing actionable insights and problem-solving recommendations from an informed perspective.Â
NLU helps conversational assistants process user intent and goals to return specific results and streamline actions based on user feedback. NLU, when combined with robust knowledge graphsOpens a new window , enables these assistants to understand an ever-growing range of questions to predict and understand what users are looking for, even when users don’t know the best way to ask about a particular problem they are experiencing.Â
Beyond improved dialogue and understanding, these conversational assistants are becoming capable of proactively identifying service-impacting events, determining the root cause and offering auto-remediation or suggested steps to resolution. This is made possible through the growing use of data science tools like Long Short-Term Memory Recurrent Neural Networks to analyze user and network experience over time and detect anomalies with a high degree of accuracy. Once anomalies are detected, conversational assistants work through a well-populated and mature knowledge graph to correctly identify the root cause and recommend actions to remedy the issue.Â
As with any AI-based solution, these conversational assistants become more intelligent (and accurate) the more data they have. Over time as they learn and evolve, their value steadily increases as they become capable of diagnosing more uncommon problems, including those that fall outside what less experienced human operators typically manage. AI can also learn what actions will most often successfully resolve issues, allowing them to recommend higher-quality suggested actions over time or even perform auto-remediation for some problems.
While technology isn’t at a point where conversational assistants can fully operate a network, there are already vendors offering assistants that can perform self-driving actions to diagnose and correct issues, all without operator intervention. For the self-driving network to be realized, IT teams need to provide feedback to assistants, to improve their efficacy and boost trust in the assistants as they become a more critical part of the IT team.
How to Transition Conversational Assistants Into the Enterprise IT Workflows
New API integrations are bringing conversational assistants into popular messaging applications such as Slack and Microsoft Teams. This gives users new and fluid ways to troubleshoot and transition these assistants into daily workflows.
A significant obstacle to adoption has been overcoming friction among IT teams, many of whom may feel burned by previous experiences with early generation chatbots. Integrating with common collaboration platforms like Slack or Teams used by IT and across organizations makes the conversational assistant a natural extension to existing workflows.Â
Natural interactions with these assistants through platforms that employees use daily can help to not only reduce IT ticket demands and streamline operations but also improve employee experiences and free up time so they can focus on the tasks that are central to their jobs. As one example, ServiceNow reduced user-generated wireless tickets in their corporate network by over 90% through the implementation of AI-enabled conversational assistants.
However, before enterprise IT teams can truly experience the full benefits of these advancements, a key preparation step must be made — preparing AI-ready technology stacks and infrastructure. Recent research shows that the top technology stack challenges revolve around creating and managing the data sets that AI solutions need to function at a high level. To address this, IT teams will need to invest in systems to manage incoming data from raw inputs and transform it using labeling systems into information these virtual network assistants can use.
AI also requires powerful computing solutions that are most easily provided via the cloud. If an organization wants to leverage conversational assistants effectively, it must ensure that its network can handle the increased load. Doing this legwork ahead of time will speed adoption and more quickly convert AIOps investments into long-term value.
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The Impact of Conversational Assistants
Implementing the next level of AI-enhanced conversational assistants in the enterprise will unlock doors to enhanced productivity and insights, significantly decreasing user-generated IT tickets. Traditional chatbots can effectively collect data and move users towards a resolution, but only conversational assistants meaningfully reduce the burden on human operators. In the future, expect to see these assistants not only identify problems independently but also act to fix problems before end-users even know they exist proactively.Â
Have you incorporated AI-powered conversational assistants in your IT workflows? What benefits have you seen? Share with us on FacebookOpens a new window , TwitterOpens a new window , and LinkedInOpens a new window .
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