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Generative AI (GenAI) has led the technological charge this year, with businesses impacted - for better or worse - by the daily evolving Large Language Models (LLMs), virtual assistants and AI applications. And IT teams are working fast to keep pace, as advancements in GenAI change how and what we automate, while in parallel, navigating the complexities of its integration. That’s notwithstanding the ethics and legality implications of the technology.
What’s clear is the potential for GenAI within the IT and software engineering space is immense, with McKinsey estimating the productivity lift on software engineering teams utilising AI alone could be up to 31%. This underscores the urgency for teams and individuals to enhance their understanding and capabilities of the technology. After all, staying ahead of the curve will only translate to a more seamless journey forward as the technology - and indeed the world - continues to evolve.
The benefits GenAI and automation offers IT professionals Imagine being at the top of your game all the time. GenAI promises to extend your reach and the time available to you. Those that can stay up-to-date with a growing armoury of AI tools will be able to leverage them across the digital operations stack as part of daily ops. GenAI is already being put to use generating status updates, incident response comms, diagnosing faults, and working through possible root causes. It’s only going to get better at abstracting away the more laborious tasks of engineers, developers, and managers alike.
With an awareness of how to use these tools - ideally reformed as functionalities built into platforms - tech workers will work quicker and to greater effect. Understanding the logic behind the models, and their training data, will be key to understanding their functionality and limitations. That means whatever your specialism, it’s likely that at least an intermediate level of AI background knowledge will support your career ambitions and place you above any less skilled peers, stuck - unable to get the most from their toolsets.
Begin adopting AI and automation into strategies sooner, not later
Success will come in a large measure from research and planning. Awareness and understanding of GenAI capabilities will require trialling and testing products and ensuring that features match the scaling needs of the business over time.
Building up knowledge must become an institutional exercise. It’s no good to have one expert who takes that knowledge with them. Learnings must be documented and shared, along with policy-making that departments stay up-to-date with their field.
Industry experts such as consultants and analysts may be best placed to advise the integration of AI into the tech stack. They will also be well-placed to cut through marketing claims to help businesses of all sizes appreciate the nuances of integration and change with complex cloud tech stacks. In-house experts are enormously knowledgeable about one business, but external advisors have the benefit of having been through processes many times and can see the underlying tracks and trends.
Adapting IT skills for the AI era
Everyone in IT must understand how their unique skills and experiences can be leveraged in a world where drudgery and toil - basic administration and rote tasks - are all abstracted away to automation. This will be a benefit leading to more satisfied time spent on more interesting, valuable, and mentally stretching work.
Picking the right education and training will become more important as professionals will inevitably specialise further. According to McKinsey’s research, software engineers and IT professionals who receive training in GenAI tools, like Microsoft’s GitHub Copilot, are able to rapidly reduce the time needed to generate and refactor code, for instance. What’s more, they also report a better work experience generally, in happiness, flow, and fulfilment. Individuals should be encouraged and empowered to complete online professional credential courses that could not only assist their day-to-day use and knowledge of AI but enhance career prospects in future positions.
Additionally, training in, and utilising skills across domains, like business and research allied to problem-solving and design thinking, may transform technologists into digital business transformational specialists. With AI tools extending their reach, it may empower individuals into one-man consultancies. They can choose between tools that enable them to work up and down tech stacks and throughout a business - problem-solving and evolving organisations with incredible agility.
Simple steps to seamless integration
In just a few steps, businesses can wholly integrate AI as a key resource to accelerate their digital operations and empower fluid IT teams:
1. Policy Determination - through consultation of research and gauging the overall impact of AI integration, leaders can set the parameters for generative AI usage. For instance, setting limitations on the usage of business or customer data can ensure compliance with data privacy regulations while still enhancing automated day-to-day processes.
2. Training and education - business and IT leaders should be encouraging and providing regular training resources for their teams to empower the usage of AI, while being diligent and thorough with its application. There is also the additional bonus of
fostering a more fluid and agile workforce, with workers able to apply themselves positively both horizontally and vertically throughout.
3. Experimentation with appropriate use cases - broad policy and training provide clarity and empowerment to the workforce, but leaders must decide where best to allow generative AI to positively disrupt the traditional workflows. Experimentation with different applications will allow leaders to understand the variety of utilisations, and to appreciate where best generative AI can have the most beneficial impact - it’s not something to be scared of when used in the right ways, so explore and find the sweet spot.
To the future
Yes - GenAI is going to upend established ways of working, with digital operations for developers and engineers just at the start of an era of disruption. Getting the most out of these tools will need constant education to keep up with change, and adding a working knowledge of AI’s capabilities, limits, and features.