4 IT Cost Reduction Strategies to Keep Your Organization Sustainable

4 IT Cost Reduction Strategies to Keep Your Organization Sustainable
  • Homepage
  • Blog
  • 4 IT Cost Reduction Strategies to Keep Your Organization Sustainable

Getting More Done With the Same Size IT Team

In the current environment, cost-cutting is one of the most reliable ways to retain profitability while also preparing for further possible instability ahead. At the same time, as enterprises digitally transform they should be hesitant to reduce the size of the IT staff that helps sustain and grow their operations.

“Let’s try to do more with what we have now,” has been the motto on many a CFO’s lips, and it’s causing them to redefine how they view productivity given the need for business stability rather than, say, growth.

With this in mind, there are a number of IT cost reduction strategies organizations can use to lower overhead without sacrificing operational performance. In fact, many strategies recommended below can improve performance by enhancing the capabilities of the IT team you have now.

1. Continuously Audit Your IT Assets for Hidden Costs

Action item number one is to measure your IT costs as they stand today. After all, you can’t control what you don’t measure.

Review sources of monthly and quarterly costs, and begin to categorize those costs to quantify what your biggest sources of overhead are. Common categories to use include the purchasing of physical assets, digital licenses, and monthly vendor subscription fees for items like SaaS platforms.

Assess the total cost of ownership (TCO) for all software and physical hardware assets. This includes not only asset license and/or purchase costs, but also the operational and support costs as well. E.g., to calculate support costs, you could correlate asset and incident data and identify classes of IT assets that have a higher occurrence of incidents, such as vendor, asset age, location, etc.

Compiling your costs can allow you to discover sources of unexpected recurring overhead. For instance, you can evaluate the TCO of vendor agreements and licensing arrangements to determine if there are ways to optimize your spend. Many businesses maintain licensing or vendor agreements that they don’t really utilize, or they might have bought several services piecemeal that can be replaced wholesale by a more feature-rich platform or service.

2. Automate Enterprise Data Collection to Make Reporting Less Manually Intensive

The use of IT data analytics can facilitate many beneficial insights that can enhance operations, improve service delivery, and lower the overall cost of IT service provision.

However, to make this type of continual service improvement (CSI) happen, insights need to be readily available and presented in an actionable way.

IT analytics platforms can make this goal a reality through the use of data adapters linked directly to all major systems of record. Not only are informational siloes eliminated, but IT teams also reduce the amount of manual effort required to generate reports using the most pertinent data available.

In many enterprise environments, reports on KPIs and priority metrics occur through manual data collection and synthesis within spreadsheet software. Replacing this process with automated analytics facilitated with highly informative dashboards severely reduces the time and effort required to report on the state of IT — and to act on that information.

With the right platform, IT organizations can transition to a self-service culture where each employee can act as their own autonomous business analyst, utilizing self-service reporting to uncover sources of pain, identify opportunities for service improvement, and win buy-in from key stakeholders on needed organizational changes.

3. Use Analytics to Identify Pain Points for IT Service Delivery

Combining analytics with a CSI strategy can enable IT service teams to constantly improve their level of service while maintaining a nearly identical level of resources.

Leveraging data from IT’s key systems of record can reveal actionable insights. For instance, calculating the volume of requests in a specific category compared to their mean wait time reveals what can be described as “service delivery friction”. Identifying these sources of friction allows IT to prioritize opportunities to move customers to automated self-service. 

Another example would be to enable IT assignment groups to reduce Mean Time to Resolution (MTTR) for categories of emerging incident patterns by enabling lower support tiers with knowledge base articles. Such shift-left opportunities can be a big driver of cost savings by reducing dependence on expensive higher support tiers to fix issues that could have been resolved at first call.

With analytics, IT services are better equipped to not only identify barriers to productivity but also prioritize which ones to remove thanks to clear, actionable data.

4. Streamline Cumbersome Processes Using AI/ML and Analytics

Analytics-derived insights can streamline manual processes through workflow changes, but artificial intelligence (AI) and machine learning (ML) can accelerate value pipelines even further. 

In many situations, the information revealed by AI/ML models allows top-level IT groups, such as the change advisory board (CAB), to arrive at conclusions quickly and more confidently. For example, a scoring model that rates the level of risk posed by a requested change can allow a CAB to rapidly determine the appropriate response. They can decide to impose a remedial revision to the change. Or, in the face of changes that carry a high risk, they can freeze changes entirely until the change or the operating environment is made more stable.

High-level use of automation in an AIOps environment can allow IT teams to more efficiently conduct processes with reduced manual touchpoints. Look again at IT operations, changes that are scored as low-level risks can be automatically reviewed and amended through an AI-driven process that is entirely automated. 

Another example of beneficial AI/ML use is how topic clustering based on natural language processing (NLP) can enable IT teams to “shift left” incident resolution responses to a lower-tier assignment group. Reducing the amount of times a high-level assignment group needs to address a problem that could be resolved by a lower tier directly saves organizations resources while freeing up top talent to focus on more value-producing activities.

Derive More Value from the IT Teams You Have Today Through Analytics, AI, and ML

Shifting IT cultures from Agile to DevOps to AIOps shows that we are always seeking the goal of delivering continuous value with minimal overhead or latency. Quantifying the state of your enterprise and visualizing things like pain points and costs can facilitate these goals in concrete ways.

Empower your IT teams to enhance their capabilities, utilize resources more efficiently, and spend less time chasing the same problems/risks through an analytics culture enabled by AI/ML. Upgrading their capabilities allows businesses to achieve more productivity and reduce the risks of disruptions in an environment where every penny counts more than ever before.

Learn more about how AI/ML and analytics can change your IT culture to be more productive and proactive in our recent webinar produced in partnership with Pink Elephant: “Driving resiliency & high velocity in your IT Operations through AI-powered Analytics

Watch the Webinar

Related blog posts