Energy Company Cuts Costs While Achieving Employee Satisfaction and Productivity

Challenges

The IT service desk team at this financial technology (fintech) company used a public-facing IT channel on Slack as a means for employees to get help. This caused a strain on the service desk since agents had to constantly monitor the channel, negatively impacting MTTR and employee productivity. In addition, top use cases required agents to handle them, taking significant agent bandwidth for highly repetitive tasks. Also, while the company invested in building knowledge content in Confluence, they had no visibility into what content was working.

Solutions

By making Espressive Barista available via direct messages on Slack, the number of questions going to the public IT channel was dramatically reduced. Barista integrates with Slack as a native app, answering employee questions or creating tickets in Jira on their behalf. The company automated their top use cases, including one that required a custom integration between Barista and their CMDB, enabled by the Barista Control Center. Also, because Barista enables feedback at the end of every transaction, the service desk team can identify content gaps and required updates.

Results

With Barista integrated into Slack, employees save time by receiving immediate, personalized answers. And with fewer employees relying on the public IT channel, agents are able to focus on more strategic initiatives. Now Barista deflects over 3,300 tickets per month, including 500+ that are fully automated (e.g., VPN connection issues, password reset), significantly reducing MTTR.

With Barista integrated into Slack, employees save time by receiving immediate, personalized answers. And with fewer employees relying on the public IT channel, agents are able to focus on more strategic initiatives. Now Barista deflects over 3,300 tickets per month, including 500+ that are fully automated (e.g., VPN connection issues, password reset), significantly reducing MTTR.

NLP 101: What Your Chatbot Needs to Know

In 2019, artificial intelligence (AI) was thrown around as a buzzword related to chatbots for employee self-help. Unfortunately, the specific requirements of AI were not made clear, causing confusion when evaluating options. There was a belief that if you bought a chatbot you automatically acquired strong AI, and so AI became just a "checklist item" in RFPs. In reality, many chatbots have little if any AI.

Now that natural language processing (NLP) is being touted as the next big requirement for chatbots, NLP is on the path to becoming the next "checklist item" . . . unless you have a clear understanding of key NLP requirements.

Join Fran Fernandez, chief product officer at Espressive, as he discusses:

• NLP fundamentals to provide a baseline understanding
• The relationship between NLP and ticket deflection
• Key NLP requirements for an employee self-help chatbot

This webinar will prepare you to build a list of NLP requirements for your RFP that will enable you to confidently select a chatbot that will deliver real business value.

Fran Fernandez, Chief Product Officer, Espressive

As the first ever product manager hired at ServiceNow, Fran is intimately familiar with the ITSM market. Fran has a wealth of experience in both artificial intelligence (AI) and natural language processing (NLP) and is a pioneer for conversational chatbots. He is an expert in blending consumer design principles with the best AI technologies to create exceptional self- help experiences. Prior to being chief product officer at Espressive, Fran held leadership positions at ServiceNow, BMC Software, and CA Technologies. As director of product management at BigPanda, Fran helped organizations process a storm of data into meaningful inputs they could react to.