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GenAI Can Electrify Your Tech Cost Optimization. Here's How
Wondering what to prioritize in your generative AI strategy? Here are 90+ reasons to focus on tech cost optimization.
5 Min Read
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Business leaders are getting to grips with how generative AI can transform their business -- and it has raised wider questions of how companies can grow through constant change.
Our latest research with 1,500 companies shows that investments in digital core technology can pave the way for businesses to thrive amid change and capture the value of generative AI. The survey revealed that organizations with advanced digital core capabilities, concerted investments in strategic innovation, and a balanced approach achieved 60% higher revenue growth rate and 40% higher profits.
Set Your Priorities
We have more data to share, in our recent Pulse survey, 85% of organizations said they were confident that they would reach their expected return on investment (ROI) from generative AI by the end of 2024. And 50% said they expected to fully scale generative AI enterprise-wide in six to 12 months.
Chief information officers and tech leaders may be wondering which area to prioritize for generative AI: their tech cost optimization programs should be near the top of the list. CIOs are, of course, experts at tech cost optimization: They use it to free cash flow from existing technology spend. They deploy it when investing in new modernization programs, including generative AI, product-value stream models, FinOps and other key investments. And it’s a powerful tool for testing the value of new investments.
When these approaches are combined with generative AI, the impact is magnified on your value and efficiency. We are working with clients who are powering self-funded cost-optimization programs. And they are doing this using a shorter, sharper process of discovery, insight generation and automation, thanks to AI.
90+ Reasons to Believe
To get here, we started with a detailed analysis of the problems facing CIOs, across five core IT functions: data and analytics; technology delivery and operations; product delivery and operations; technology business management; and talent and workforce. This identified more than 180 specific cost-saving levers that CIOs could pull to start getting better value for their business. These are problems that we know nearly all organizations need to solve, and areas of opportunity for them to exploit.
We then filtered these to identify which were most likely to be solved or mitigated by generative AI. We found that the value potential in more than half could be boosted with generative AI and automation. This gives tech leaders a menu to choose from, so they can focus on the sprints that are likely to deliver early value for their organization and help continuously fund their reinvention.
Key Areas of Focus
In data and analytics, we’ve found the most promising areas are the following: analytics driven by natural language queries; data generation for testing; generating data pipelines; data harmonization; release and test analytics; and generative AI-assisted data models.
In tech delivery and operations, many CIOs are focusing on these: generating automated workflows and test scripts; real-time root-cause analysis; and identifying automation opportunities.
In product delivery, among other services, we are helping clients use generative AI for these situations: code development; generating and analyzing user stories; generating test cases and scripts to accelerate automated testing; incident prevention; and for testing and accelerating configurations.
In technology business management, generative AI is proving particularly valuable for improving automated service desks, chatbots and incident resolution.
To take one example, we helped a European energy company to unlock productivity gains in project delivery and data management using generative AI. This collaboration could halve the time it takes to produce oil on a new site and improve productivity by 5% annually.
And we worked with a leading beverage company to streamline its applications management processes. This helped the company to cut its incident resolution turnaround time from an average of two days to under five minutes.
A Sprint State of Mind
Our digital core research cites AI as a top contributor to technical debt -- the cost and effort required to keep IT systems up to date, accumulated through choices that prioritize speed over long-term maintainability. It also reveals that leading companies allocate, on average, 15% of their IT budget toward tech debt remediation. This allows them to “pay down the debt” without sacrificing strategic investments. At the same time, these companies are shifting at least 6% of their IT budget annually from maintenance to innovation, including generative AI.
Of course, this represents a shift in thinking. It means moving from mobilizing long-term programs of tech transformation to an era of continuous reinvention. It will clearly benefit CIOs who are overseeing multiple, agile teams working on specific problems that are hyper-focused on their customer base and geared toward helping the organization respond innovatively to their customers' challenges.
One of the key benefits is versatility. A lot of the savings we've mapped are being delivered within months. Each tech investment is directly linked to clear business outcomes. And it's making the most of generative AI's ability to drive return on investment, through revenue uplift, productivity increases and a reduction in capital expenditure.
For a CIO looking to prioritize their next generative AI play, tech cost optimization is a great way to deliver real value in a tight timeframe.
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