Introducing the Digital Labor Studio With Amelia AIOps

5 minute read

The Digital Labor Studio (DLS) converges people, process, and technology into a contemporary Digital Workforce.

IPsoft this week announced Amelia AIOps (formerly 1Desk), the fusion of autonomics, cognitive and analytics for users to connect with any service within their enterprise: IT, Finance, HR or Administrative. AIOps is packed with multiple innovations based on our automation experience with IPcenter and our cognitive expertise with our virtual agent Amelia. AIOps brings together the IT front-office (service desk) and back-office (operations) into a cohesive solution where virtual cognitive agents, autonomics and humans work toward a common goal of delivering unified business services of continuous improvement.

What’s more, AIOps is designed to encourage organizations to embrace a future where autonomic and cognitive technologies work alongside their human counterparts solving business and IT issues. This will take a convergence of people, process and technology into a true Digital Workforce, sparking board-level discussions and debates about how to build this workforce and how to best bring this vision into focus.

One of the Digital Workforce innovations within AIOps is the Digital Labor Studio (DLS), a nexus of technology, process, resources and talent which is designed to drive new efficiencies through intelligent use of different AI and automation technologies. The DLS constantly analyzes the processing, remediation and optimization of the work occurring in AIOps, driven by a team of automation and cognitive engineers who are guided and aided by ITIL processes and AI technologies.

With AIOps at the center, building the DLS will be a journey for many organizations. A good first step in that journey is for customers to look at how they’re already using ITIL as a guide in IT support, consider how AIOps can significantly improve and greatly elevate their IT operations, and then expand the use of AIOps to encompass other business services.

ITIL Processes for Managing IT

Think about how we currently manage the IT environment. We use ITIL as a guide to drive incidents, changes, requests, problems etc.  through standardized process flows, and within the processes, tickets can get automatically escalated, dispatched or even resolved automatically. Before tickets get into a process flow we use various methods such as filtering, event correlation, service catalogs and perhaps even machine learning algorithms and semantic analysis (NLU) to read incoming email. The goal of all this is to route and resolve tickets as efficiently and cost-effective as possible.

Then after that? Yes, as people often remind me, Continual Service Improvement (CSI) is part of ITIL, and ITIL considers CSI as a critical method of creating a culture focused on continual improvement, where everyone involved in the service delivery lifecycle takes ownership and responsibility to advance services over time. But how many organizations have actually successfully implemented CSI as part their ITIL program? By successful, I mean an industrialized approach with measurable impact on operations. I would be willing to bet it’s less than a majority.

Building Digital Labor

AIOps is a platform built for continuous improvement by recording and learning from everything humans are doing to resolve tickets and issues affecting various services. Every ticket that gets resolved creates a new ticket with all the relevant info related to how the issue was solved. Did the person consult a SOP (standard operating procedure), look into the CMDB (configuration management database), connect to a remote system, and/or run commands? What was the output of those commands, and how quickly was this done? Was an incident escalated, did an automation run, and did it resolve the issue?

Ideally, some of these tickets can be directly turned into approved automations which will automatically resolve the same situation next time – which is a core mission of AIOps.

Within AIOps, all these tickets now enter a new process for extracting knowledge, insight, trends and building blocks for creating new -- and improving existing -- digital labor solutions. Similar to the operational environment, tickets are correlated, analyzed, and prioritized using algorithms, machine learning and rules to guide into and through new workflows.

Let me give two examples of how AIOps optimizes and improves IT management:

  • Yesterday, somebody in operations solved three issues with Oracle. Everything was recorded, process flows were automatically built, resolution results were documented and now the tickets sit in the queue for the automation engineer responsible for building the Oracle “Virtual Engineer.” This engineer will use AIOps to inspect the resolution and generated content to determine how the improve the existing Oracle automation solution.
  • Over the past weeks, the service desk has been seeing an increase in the number of requests for troubleshooting access and resetting passwords for various mobile applications. This is likely caused by mobile OS upgrades. Currently, these tickets are solved by service desk staff using a Mobile Device Management (MDM) solution as well as going into identity and access management. AIOps semantically analyzes the tickets and identifies the trend. Cognitive engineers are assigned to train Amelia to recognize and resolve these requests by analyzing and using the content generated from the tickets, and how agents executed the troubleshooting and resolutions. The integration specialist and automation engineers collaborate to do their backend work. All of this, it should be noted, occurs without users or those who submitted the tickets even realizing it, until the same tickets or issues crop up again. At that point, users might notice their tickets are resolved even faster, if not almost instantaneously.

The continuous creation and prioritization of opportunities based on analytics is key to the success of any automation project. This is often cited as one of the key failures of such projects, which is why we have built that process into the core operations of AIOps.

The Digital Labor Studio Team

The Digital Labor Studio (DLS) converges people, process, and technology into a contemporary Digital Workforce. The DLS team is one that organizations can potentially create with their adoption of AIOps, as part of a broader digital and human resource model. The DLS team itself is relatively small – compared to the operational team – and work being done here is not about scripting solutions or using decision trees, but rather more modern design thinking that can permeate the approaches.

Let me make a few things very clear:

  • For the core DLS team, this is their role in the organization, it is not a part-time job.
  • It is a multi-disciplinary team working closely with subject matter experts (SMEs) from the rest of the organization.
  • Creating a DLS team does not require an organization to hire dozens of employees; many of the skills may already exist within an organization, and others can be added incrementally over time.

Core DLS members are cognitive engineers and automation engineers, however architects, data scientists, linguists, integration specialists, QA engineers and project coordinators also play critical roles on the team(s). The work environment is similar to an Agile development environment, with backlogs, MVPs, roadmaps and daily stand-ups. Just like CSI in ITIL, this is a very metrics-driven approach to recognizing and quantifying opportunities for improvement.

Creating a different experience for business users

This is not all about efficiencies -- solving things faster and cheaper -- but also about a higher level of service being delivered. Imagine requesting access to a new application via Amelia in common language and instead of receiving the “usual” response – it will take about two hours – Amelia comes back with a calculated estimate of 21 minutes. This is based on similar requests that AIOps has fulfilled previously, the length of the current approval queue, and the level of urgency associated with the request. The cognitive competence that AIOps can provide elevates current service delivery to new levels of quality and competency.

The Digital Labor Studio is a new concept for a collection of collaborative talent, but the concept itself should not be foreign to organizations that already have a great familiarity with ITIL processes. Indeed, ITIL is a beating heart inside AIOps, which extends ITIL’s concepts of continual service improvements and adds new capabilities with intelligent automation and cognitive technologies embedded inside. AIOps can drive organizations to rethink how they approach service delivery, with a new Digital Workforce at the wheel.

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