Bobbi is SVP, Software Engineering at Loopio. She is a technology leader with over 25 years of diverse experience in the industry.
AI and emerging technologies under the AI umbrella—like generative pre-trained transformers (GPT)—are reshaping the business world. These technologies are fostering greater organizational efficiencies and innovations and are quickly becoming crucial for companies of all sizes.
The ability to automate processes and tasks opens up a plethora of new opportunities for organizations. When automation can scale with an organization, this can completely transform day-to-day operations. In this article, I’ll look at three ways that engineering organizations in particular can use AI to transform their organizational efficiencies, organizational structure and software practices and processes.
Organizational Efficiencies
GPT and AI are changing the functions for all roles within software organizations. Software engineers are becoming more reliant on AI tools for mundane code and unit tests, creating opportunities for engineers to focus on the more creative and innovative aspects of their profession.
People are finding ways to yield efficiencies in the way they work by using tools such as Code Whisperer and CoPilot to produce high-quality code while spending their time on tasks that are value creating and challenging for them.
New technology, understandably, drives uncertainty, but a developer’s job is not going to disappear—rather, it will change and evolve. We need to look at these new tools with a dose of optimism. While they help the code base become more consistent and easier to understand, engineers will be able to produce faster and dedicate their time to work that is more intellectually stimulating.
Some proven areas of effective AI-enabled effort gains are in QA automation and unit test development as well as repetitive code generation. Engineers spend valuable time in increasing test coverage and software development engineers in test (SDETs) labor to produce needed user interface (UI) tests. Experience shows that using new AI tools helps increase effectiveness by producing quality code and making developers more productive by focusing their effort on more creative tasks.
Organizational Structure
A couple of questions that we might ask ourselves: How do we optimize our existing processes to gain efficiencies with AI tools available for faster development? How do we educate the rest of the company on new possibilities and tools? This is a time when the engineering team has the knowledge to lead the way for the rest of the company into the new world of AI tools and new productivity technologies.
Centers of AI excellence will emerge and thrive while the companies grapple with the question of how to adopt the best technology to increase productivity while maintaining high security standards. Elevated by the GPT wave, the machine learning (ML) and data dcience (DS) teams can take a more central role in engineering organizations as drivers of innovation that permeate through the entire product and are not isolated to specific functionality.
Gone are the days where the ML/DS teams live in their own isolated environment and provide insights for specific features when requested to do so by the very few that understood well the power of this discipline. These teams used to be aligned with infrastructure or data teams. Now, ML/DS teams can become core enablers for all engineering squads and as such are reorganized into the core engineering functions.
This new trend drives better ML/DS awareness into core engineering functions, increases the daily interactions and creates a more fertile environment for innovation.
Software Practices And Processes
AI has transformed the way we think about software practices and processes, and it pushes us to be even more flexible and agile.
GPT presented an adaptability challenge to product and engineering teams. How quickly could the product road map be adjusted to accommodate this new technology? This was and still is the question that determines the viability of businesses and relevancy of the software products.
The rate of innovation is accelerating, which drives the need for more agile ways of structuring the product road map. Do we still need a one-year solidified product road map view or should we keep this process more agile and flexible?
The answer lies in the balance. While product development needs to still be intentional and driven by an ambitious and thoughtful long-term product strategy, there still needs to be a way to interrupt and reevaluate on a cadence or during major disruptions.
Some organizations have standard quarterly product road map reviews and others keep these reviews on a monthly basis. Companies are now discovering that more frequent program revisits and even monthly review schedules put more scrutiny on the product prioritization process and allow for knowledge exchange and adjustment to major technology disruptions.
The release of ChatGPT sent a shockwave throughout the world, and businesses and products are still adjusting to it. Companies need to build the muscle of change management and fast response, as similar disruptions will continue to occur and only quicken. It is a fine balance between speed and risk control.
This new technology is driving changes to all functions within the engineering department. Engineering professionals will become even more reliant on AI tools while spending the bulk of their time on more creative and innovative tasks. All of these changes are logical fallout of a revolutionary technology that will only help us become more efficient, productive and adaptable.
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