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Using AI and machine learning to accelerate digital transformation

digital transformation

The importance of IT’s role as a technology/business liaison has never been greater. Business leaders not only expect IT to provide technology leadership, but also to connect technology with business priorities.

Transforming the workplace

Digital transformation is not only about the optimization of externally facing customer interactions, it’s also about improving internal processes. MIT Sloan describes digital transformation as “the use of technology to radically improve performance or reach of enterprises…to change customer relationships, internal processes and value propositions”.

Likewise, recently there has been an uptick in the movement of applying IT service management principles, practices and technology into other lines of business (LOB) such as Human Resources and facilities, allowing them to phase out cumbersome email and spreadsheet-based workflows.

IDC forecasts that enterprises will spend $1.2T on digital transformation technologies in 2017, with spending reaching $2.0T in 2020.

Accelerating digital transformation

AI-driven automation allows support organizations to deliver consistent, reliable services and support 24×7 across geographies and devices. And with smart data analysis, service management leaders can personalize interactions, improve workflows and optimize resource allocations to improve service quality while reducing support costs.

AI-driven service management also provides strategic benefits, allowing managers to quickly discover valuable information such as the health of operations based on the number of inquires, problems and changes related to business services. This information allows managers to proactively assess, design and transition resources, vendor relationships and budgets.

Additionally, security is top-of-mind for all business leaders. By analyzing real-time data about how employees use technology, it’s easier to identify threats and enforce policies before breaches occur.

Setting expectations

Service management leaders should make it clear to executives that AI-driven automation may not always know and/or immediately provide the “right” answer. At times, the system may require feedback in the form of conversational dialog or need to route an employee to a live agent to fulfill a request or remediate an issue.

How AI-based service platforms are designed, deployed, and trained determines their rate of adoption. The accuracy of systems that leverage machine learning and natural language processing should continuously improve. Thus, the sooner an organization starts the process of training and implementing, the faster it will gain the benefits and competitive advantages associated with AI.

Unifying the strategy

It’s recommended that service organizations use an AI system internally before rolling it out to the broader organization. Service administrators can select a set of common issues and allow the AI system to suggest the correct action, recommend experts and/or auto-assign/escalate the request to the appropriate support staff.

An iterative approach to deploying AI platforms allows teams to gain a higher level of familiarity, comfort and trust with the solution. Once the value of AI-driven automation is understood by internal users it can then be extended to external users across all service domains.


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