In a recent article from McKinsey, out of nine key actions for CIOs and CTOs, several research aspects stood out to us as practitioners in the ServiceNow development space. For most of the ServiceNow community in the business of generating business critical workflows and service presence, the immediate focus for developers are the identification of use cases, how to deal with technical debt and what to do with training to maximize proficiency levels of the existing teams.
Given the estimated potential of generative AI could add the equivalent of $2.6 trillion to $4.4 trillion of value annually indicating an entirely new productivity frontier, CIOs and CTOs will certainly cascade AI oriented goals to the IT organization to capture immediate benefits. The following are highlights of McKinsey’s research to address this moving window of opportunity.
IT leaders should work with business leaders to identify valuable opportunities for generative AI, considering factors like productivity, growth, and new business models. In every domain there are critical points known to operational groups that would provide the most return on investment, the contraction of the software development lifecycle is a prime example.
To offer such guidance, tech leaders need to collaborate with the business to establish a financial analysis capability that accurately gauges the costs and benefits of generative AI projects. This analysis is intricate, involving considerations such as various model and vendor expenses, interactions between models (each potentially incurring its own fee), ongoing usage charges, and the costs associated with human oversight.
Another impact of generative AI is expected to be a reimagining of the technology function. CIOs and CTOs need to take action quickly to build experience and expertise in the growing technology that will fuel further demand for very specific creatives. According to the research there are three areas where they can focus their initial energies:
Software development
McKinsey's findings reveal that generative AI support significantly boosts software engineering productivity. It enables engineers to speed up code development by 35 to 45%, streamline code refactoring by 20 to 30%, and expedite code documentation by 45 to 50%. For companies like Blueprint Solutions this productivity boost is only an initial cut as the BlueprintAI product will potentially rewrite the roles across the entire software development lifecycle and also bring customers much closer to a final review pass, even as requirements are being taken.
Technical debt
Within IT organizations, technical debt can consume a substantial portion, ranging from 20 to 40%, of technology budgets, leading to notable delays in development. CIOs and CTOs must scrutinize tech-debt balance sheets to gauge how generative AI capabilities like code refactoring, translation, and automated test-case generation can expedite the reduction of technical debt. Companies like AutomatePro, integrating generative AI enhancements, could enhance automated testing and edge case simulation, thereby enhancing software resilience before release.
IT operations (ITOps):
CIOs and CTOs should assess their ITOps productivity strategies to leverage generative AI for process acceleration. Generative AI already proves invaluable in automating tasks like password resets, status inquiries, and basic diagnostics via self-service agents. It expedites triage and resolution through enhanced routing, provides contextual insights and suggested responses, enhances observability by analyzing extensive log data to identify critical events, and facilitates documentation creation such as standard operating procedures, incident postmortems, and performance reports. This creates a potential feedback loop for further development of automation in the development area.
McKinsey’s analysis of the total cost of ownership for different strategies in leveraging AI range from immediate applications of generative AI using purpose built apps which fall in the range of one time investments $0.5M or up to $5M annually recurring investments for scenarios where model inference, model maintenance and plug-in layer maintenance are implemented within the organization.
Finally, the research also calls out the knock-on impacts of how IT talent management is to be undertaken in the generative AI era. Their empirical study utilizing the generative AI tool GitHub Copilot demonstrated a notable improvement in coding speed, with software engineers achieving a 35 to 45 percent increase in efficiency. However, the extent of these benefits varied: highly skilled developers experienced gains of up to 50 to 80 percent, while junior developers encountered a 7 to 10 percent decrease in speed. This variance stems from the fact that utilizing generative AI tools necessitates engineers to assess, validate, and refine the code, a task less familiar to inexperienced software engineers. Conversely, in less technical roles like customer service, generative AI significantly enhances productivity, resulting in a 14 percent increase and a reduction in staff turnover, as indicated by a study.
These discrepancies highlight the imperative for technology leaders, in collaboration with the chief human resources officer (CHRO) and IT recruiters to reassess hiring and talent management strategies to align with the demands of the evolving workforce. While hiring top generative AI talent is crucial, retaining such talent requires implementing retention measures such as competitive salaries and involvement in strategic projects. A rehash of the hiring alone will be insufficient. Given that nearly every role will be impacted by generative AI, a key focus should be on upskilling employees based on the specific skills required for their roles, proficiency levels, and business objectives. Training programs for novice software developers for instance should prioritize transitioning them into proficient code reviewers alongside code creators.
In conclusion, the integration of generative AI represents a paradigm shift in IT operations, offering unprecedented opportunities for organizations to enhance productivity, accelerate innovation, and drive competitive advantage. As highlighted by McKinsey's research, CIOs and CTOs must proactively embrace this technology, working in tandem with business leaders to identify strategic use cases, establish robust financial analysis capabilities, and prioritize talent management initiatives. By leveraging generative AI to streamline software development, reduce technical debt, and optimize IT operations, organizations can position themselves at the forefront of digital transformation, unlocking new levels of efficiency, agility, and resilience in today's rapidly evolving business landscape. This is the core effort at Blueprint Solutions and we invite you to reach out and find out more using the contact us page.
Credits to McKinsey authors Aamer Baig, Sven Blumberg, Eva Li, Douglas Merrill, Adi Pradhan, Megha Sinha, Alexander Sukharevsky, and Stephen Xu, representing views from McKinsey Digital.
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