When addressing new technologies, services and expanding roadmaps technology product management leaders need to seek differentiation. It is common to have abundance in communications-related assets per client that won’t be cost and time effective to integrate and manage using APIs or manual operations.

Potential TEM use cases are listed here:

Usage Management:

When adding cognitive Artificial Intelligence (AI) approaches along with automation tools, the processes around usage management can be enhanced. For example, usage permissions could be automatically granted through the use of the automation (if end-users authorized them), for tracking and analyzing the usage of services and items by user type/persona through the invoice process and accounting process. The cognitive AI element could enhance the tracking of the device and flag any erroneous use or faults.

This, linked with the data analysis obtained via the TEM system and through records or service and records of help desk activity, could pre-empt item breakdowns before they occur. For example, automation processes could use the information they have collected (on the usage patterns, types of calls to help desk/service desk, applications in use and so forth) and predict the maintenance or set in process an auto-replace.

This could then lead onto enterprises adjusting policies around replacement cycles. For example, the automation process could identify a phone with a broken operating system (or symptoms highlighting an operating system is about to break). It could then automatically notify the user and set up a replacement pre-emptively, as opposed to a broken phone/laptop or a three-year replacement cycle.

Sourcing Management:

Automation could be used to auto-analyze all different carrier/vendor rates in one place to reduce the time for consulting staff. It could routinely explore the market to provide updates on pricing or best pricing plans, making adjustments according to their results. On the mobile side, it could even auto-swap employees onto pricing plans associated with their usage profiles.

Automation tools could also auto-populate Request For Proposals from carriers and vendors, then work within rule limits for those that are meeting price/Service Level Agreement rules and centralize T&Cs. When contracts come to an end, various different automation processes linked with AI and cognitive learning could assist in rightsizing, right fitting the new Communications Service Provider or vendor requirements.

The rightsizing could be done according to every piece of data centralized in one place (from mobile individual users from site size, data usage peaks, etc.). This would enable targeted, custom-made sourcing initiatives and reduce the time taken for consultants or project staff to work through several items of data. Instead, they would have it all to hand in one place.

Dispute Management:

Automation together with machine learning and AI could automatically raise dispute tickets and track them (linking to the TEM portal), track SLAs for compliance with carriers/vendors and other billing inaccuracies. It could centralize and highlight dispute data for TEM agents to manage the dispute. It could also highlight it to the service provider (as an agent) in a pre-emptive manner, and not being restricted to time zone/out-of-hours’ limitations.

Customer Service, Reporting Configuration, Predictive Maintenance and Providing Deeper Business Intelligence and Analytics:

Where automated processes could be used here, is in centralizing information for TEM staff or even human help desk agents into one place in seconds. This enables a rapid and targeted response, resolution, and advice, leading to potential upsell/cross-sell opportunities into process outsourcing and detailed strategic advice.

Reporting of data could also be auto-categorized (namely, auto-population of user information, address department and so on) as well as in report schedulers.

By adding in AI capabilities, TEM vendors could perform pre-emptive maintenance. Such a process could understand messages and send out resolution messages of an issue that was fixed prior to its occurrence. They could do so by basing the information on a number of different data sources (such as usage data, help desk call logs [among others]), proactively for the enterprise and predicting any necessary maintenance.

Customized report configurations could also be automated. Automation tools could be used as an interface to the admin to configure and produce a report that is fully customized.

Recommendations for technology Product Management leaders:

  • Define new TEM-related automation test use cases around optimization, sourcing/contract management, usage management, enhanced BI, analysis and reporting for new services and expanded roadmaps, by selecting those that are better aligned with your strategic product priorities.
  • Add capabilities of AI and machine learning throughout your TEM stack by starting with those that can provide more differentiation and cost optimization.
    • Develop AI and machine learning by starting with simple tasks and usage that assist in enhanced support and extended life cycle management capabilities.