It’s no secret that Application and Infrastructure Management and Support (AMS and IMS) service providers have latched onto artificial intelligence (AI). Most have attempted to address commitments to productivity and continuous improvements touting AI tools like IBM’s WatsonTM, Cognizant’s Evolutionary AITM, and TCS’s ignioTM, but have lagged in making commitments to specific and tangible improvements.
Are AI tools really working? The answer is, “Yes.” Are the cost savings garnered from the use of these AI tools being shared with clients? The answer is, “Definitely not.”
AI tools offered by AMS and IMS providers are often promoted as differentiators. TCS, for example, has monetized ignioTM as an add-on with an annual fee in excess of $3,000. While TCS’s Digitate unit boasts that “ignioTM is a cognitive automation solution that helps IT rapidly identify and remediate outages in minutes,” it does not actually assign a dollar value that it guarantees it will save or quantifies its value. It merely touts that it will drive costs down, but it does not necessarily specify where AI will be leveraged.
Fixed Fee Contracts Benefit the Provider
Most AMS and IMS customers have fixed fee contracts. As time goes on, AI tools help make the business run more efficiently, but customers are still paying fixed fees throughout the contract. This means that the revenue stream remains for the provider but the cost of delivering the services to their customers will be reduced over time. While the providers will argue that their cost reductions over the term have been “baked-in” to their fixed fee proposals, they retain all the upside for exceeding their own efficiency targets. As a result, it is very hard for a customer to get a lower fee based on their contract, but in contrast, providers are well positioned to leverage their AI tools to increase their margins over time.
AMS and IMS providers are tasked with keeping applications and infrastructures free of downtime, highly available, and performing at their best. In the past few years, the addition of AI and other efficiency tools like Robotic Process Automation (RPA) provide the means to accelerate these results providing real-time benefits to providers in increased margins and profitability.
Consider the beginning of your engagement with an AMS or IMS provider. You give them the responsibility to support your current state environment of applications or infrastructure, which probably has limited or non-standard documentation, limited tracking of issues, and limited records on how issues were resolved in the past. Supporting your infrastructure or applications are the only areas your provider is focused on, so they tend to be very good at it. They identify savings they can get in the first year along with savings year over year.
The first project an AMS or IMS provider undertakes is to start documenting. They begin to create a “runbook” or a compilation of routine procedures and operations that are carried out every time a call comes in. They also begin to create an “issues log” so subsequent similar requests can benefit from work that has already been done. The agent that takes the call can refer to the issues log to see if the problem with the application has come in before and then build on the runbook once it is resolved. These efforts are, by and large, manual and to every client deemed table stakes for consideration. Clients expect efficiencies, not just in lower hourly cost of service but in efficiencies these providers bring based on their experience and maturity of processes.
As time goes by within the term, the visibility to provider efficiencies becomes increasingly difficult to obtain and quantify when fees are fixed. Though you cannot readily see the effects of service optimizations, you can rest assured that your AMS or IMS provider is leveraging their experiences across their client base along with their tools (which include AI engines and RPAs) to drive down their cost of services.
To illustrate this point, we only have to read the transcript from Infosys’ last fiscal year earnings call. On that call, Infosys remarked that it exceeded its $150M cost optimization targets and went on to reinforce that automation is a key component of Infosys’ ongoing strategic cost optimization efforts. In fact, all AMS and IMS providers are leveraging every available resource at their disposal to exceed cost optimization targets, but the improvements/efficiencies of support are not translating back to monetary savings to their clients.
Transparency Into Savings
AI identifies patterns to build stronger runbooks or automations that allow providers to further reduce the effort required to support a client. The more you work with your AMS provider, the faster they achieve increased margins. The efficiencies in time to resolve an issue added to the reduction in effort needed to resolve an issue equals increased customer satisfaction for the client and increased profit for the provider.
Since the incoming dollars are fixed, the margins continue to increase. Providers can dedicate less resources as time goes on, while the cost to the supplier does not decrease based on the velocity of these improvements. Given that there is little-to-no transparency into these increased margins and the efficiencies of the support, it is almost impossible for the client to take advantage of them. If there’s a high velocity of effectiveness generated by the use of AI, that velocity doesn’t share back monetarily to the client. Of course, it could share back in terms of improvements to service levels from a delivery perspective, but that won’t translate to further reductions in provider fees.
Develop a Negotiation Plan
AI definitely means added income, but AMS and IMS providers are dead wrong if they are selling this tenet to their customers, since the added income is definitely one-sided. Do not underestimate the value of your data, participation in design thinking sessions, and your contribution to the identification of new and practical AI use cases. All of this is hugely beneficial to the provider’s success, so it should certainly be leveraged in your discussions. Develop a sourcing and negotiation plan that focuses on transparency and clearly defined accountability. You should seek to build an agreement that aligns both your company’s and vendor’s roadmaps and addresses cost-sharing of gains achieved through the use of AI. Ensure a willingness to share in the value of efficiencies and learnings gained throughout the work.
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