Faced with inflationary pressures, climbing interest rates, and continuing geopolitical tensions, IT leaders are in the position of having to navigate through a year of cost constraints and economic uncertainty. They will approach 2023 with caution and look for ways to do more with less to improve efficiency and reduce costs.
Here are six cost-saving strategies IT leaders should consider:
1. Rationalizing application portfolios
Many enterprises keep obsolete applications alive way beyond their usefulness. Often this is because they need to retain access to important historical data, or they’re unsure of the interdependencies with other systems and are worried about switching the application off.
Or it could be simple inertia. Finding a way to retire these systems is well worth the effort as it frees up maintenance and support spend which can be reallocated to more productive uses.
If key data is required for compliance or customer service from decommissioned systems, it can be rehoused as static data searchable archive, for example, which can often turn out to be cheaper and more accessible. From here, it can be accessed by users and be retained long-term in compliance with data regulations.
One of the keys to successful application decommissioning is involving business users from the outset. They can best explain how an aging or legacy application is used and what information must be extracted and kept accessible.
[ Also read IT leadership: Top 5 challenges to expect in 2023. ]
Prioritizing applications where decommissioning will deliver the greatest return is essential. In particular, IT leaders should look out for systems requiring an imminent (and costly) hardware or software upgrade or where support is being killed off – allowing decommissioning to deliver instant savings and reducing risk.
2. Choosing augmentation over 'rip and replace'
Investment in digital initiatives will not stop in 2023, but getting approval for significant new build projects is likely more complex. Businesses will favor projects that deliver quick wins (with less cost and risk) by adding new digital functionality to systems already in use instead of ditching them in favor of a shiny new technology platform.
Organizations aiming for a more joined-up customer experience can redirect and reformat existing output from several independent departmental systems – say, handling marketing, sales, fulfillment, finance, and customer service – to create a consistent set of text messages throughout the customer journey.
Or, rather than replacing an existing customer billing application, it’s quite feasible to add AI and analytics to give customers personalized spending forecasts.
3. Expanding digital self-service
In customer service, there will be a push to extend self-service to more parts of the organization (since self-service costs $0.09 per customer interaction compared with $14 each time a live agent provides assistance, according to Gartner research). Self-service investments will include technologies such as online account portals, enhanced mobile apps, AI bots, and analytics to help understand and predict customer behaviors to improve customer experience on digital channels.
It’s important not to focus only on short-term cost reduction when measuring self-service success. While a customer service chatbot might successfully reduce costs by reducing the number of queries handled by agents, you also need to be aware of the impact on customer experience and customer retention over the longer term. Are customers happy to interact with a bot, or will they go away feeling disgruntled because their queries were not adequately resolved?
It’s important to incorporate a feedback loop to measure satisfaction levels and apply continuous improvements.
4. Consider your hardware refresh cycle
Analysts, including IDC, expect price rises for IT hardware and software to persist for the next two years, so CIOs will be looking for ways to rein in spending on upgrades and replacements.
One option is to save capital expenditure by extending the refresh cycles for end-user and data center hardware. The average replacement cycle for hardware is 3 to 5 years, and IT departments can make savings by putting this off for another year or more, as long as the hardware remains fit for purpose and meets all security requirements.
Striking the right balance will be key, however, as hanging on to old hardware for too long could also mean more frequent downtime due to reliability issues – and end up costing the IT department more in support.
Another consideration is aligning the hardware refresh cycle with your cloud strategy, as some hardware that’s due for replacement might be a candidate for cloud migration.
5. Chasing data center energy efficiency
With energy costs expected to be high for the foreseeable future, any technology or process that helps the IT team conserve power will get more than a second look. Energy-saving servers such as IBM LinuxONE will see increasing interest, as will eco-friendly liquid cooling tech for server hardware, for example.
Current air cooling systems used in the data center account for a significant proportion of operational expenditure, and rising energy bills are likely to make this even more painful. Water and other liquids used in liquid cooling are significantly more efficient than air at transferring heat, which is why interest in them is proliferating.
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That said, before switching to liquid cooling, it’s important to consider the total cost of ownership and ease of implementation and maintenance. Liquid cooling is relatively new and requires the IT team to acquire a new set of specialist skills to keep all systems running efficiently.
6. Expanding IT automation
The growing trend for AIOps – which combines big data and machine learning to automate IT operations – has the potential to reduce costs while increasing operational efficiency. Greater automation is also a logical solution to the growing IT skills gap, freeing employees to focus on higher-value work rather than lower-value, repetitive (and error-prone) tasks.
Typically, AIOps involves using machine learning to sift through vast amounts of data collected while performing IT operations to find unusual activity and to send automated alerts to human operators or even other systems. An example would be automated monitoring of IT systems to predict system outages or identify the source of performance problems.
Any initiative using the power of AI should go hand in hand with a firm governance policy to ensure that automated decision-making is consistent with business policy.
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