January 16, 2024
If, like me, you’ve been involved in software development for a long time, you’ll know that having shiny new technology to play with is nothing new.
It was true in the early days of mainframes, when C++ and then JavaScript were created, and when the rise of the internet displaced the rigid reign of mainframes and ushered in a dynamic, cloud-enabled era.
Modern programming languages, techniques and practices marked a watershed moment in the evolution of software development. This transition democratized software creation providing unprecedented flexibility and efficiency.
The debut of generative AI (GenAI) just over a year ago marks another watershed moment. Its rapid evolution and extensive applicability across industries set it apart and makes it a transformative force.
The rise of LLMs (large language models) from the likes of OpenAI and others has changed everything. I personally encountered this during a multi-year transformation project, when ChatGPT’s introduction led me to reevaluate the strategy for a business transformation project I was leading.
I immediately went into research mode. I examined what this new technology meant for the company then went about rewriting the plan to account for the productivity boost GenAI offered. Continuous reassessment of technology strategies against multi-year transformation initiatives is essential, emphasizing adaptability and agility in response to constant advancements.
With GenAI, You Need to Revisit Your Plan
GenAI is a true disrupter in the way it democratizes knowledge and commoditizes skills and specializations. It is reducing the barriers to entry to high-value areas of service delivery across the board. I came to the conclusion that use of GenAI in several aspects of software and product development could, in many cases, speed up the time to get a solution out the door by 50%.
Before the current generation of LLMs became widely available, building a machine learning application required a specific skill set. You needed to know how to build and train a model, how to obtain and process the data the model was built on, and how to operate it on an ongoing basis.
Engineers and data scientists who understood machine learning, were a hot commodity. They still are. But GenAI has automated many aspects of their work, and democratized certain skill sets.
The Rise of GenAI Managed Services
Right now you can go to a cloud provider like AWS, Microsoft Azure or Google Cloud, and choose the exact mix of LLMs you need for specific use cases. They will be delivered as managed services with user-friendly workflows and tools to help you navigate them.
I was in discussions with one of those cloud providers about using an AI service to streamline access to knowledge within the business. I was quoted $60,000 for the managed service. Then OpenAI came along and delivered what we needed as a standard part of its offering, with no extra charge.
What used to take six months in machine learning can now take two weeks as a result of GenAI’s application. The implications of this are immense. You can leverage this technology to automate some of the mundane and repetitive tasks your workforce undertakes, improving efficiency in the process. You may be able to help burn through the backlog of tasks and unfinished projects that plague most organizations.
More fundamentally, you should be looking at how you can use GenAI to reinvent your business model, changing your approach to people, technology, and data in the process.
Thanks to the mainstreaming of AI, there are a new set of table-stakes skills that everybody needs to develop regardless of whether you're in a software development world or a product world.
AI-Assisted Legacy Modernization
Nearly 25 years ago we saw traditional bricks and mortar companies attempt to go digital with the rise of the web and the dawn of e-commerce. Digital just took over. We are seeing that kind of movement right now, but at an accelerated pace.
The difference is that GenAI has the ability to assist the laggards, those businesses that have been slow to undertake digital transformation and to move to the cloud. Legacy modernization becomes easier with AI, giving businesses that have struggled to stay abreast of technology, the opportunity to catch up and shift to cloud platforms that make best use of GenAI.
So what happens when up to 50% of certain processes and tasks in your organization can be automated? It means you can free up your workforce to focus on the things that really count, that make you more competitive and open up new lines of business.
The AI revolution has implications for some jobs. That’s where change management comes in. How are you going to upskill and reskill your workers for the new reality? With GenAI, a set of new priorities emerges. You need the right governance, guardrails and policies in place. There are privacy and security implications. A lot of AI is essentially a black box. How will you explain to customers the role AI plays in the services they are using?
What works for one company won’t necessarily work for another. Every organization needs to find its own path. But one thing has become clear in 2023. Simply exploring GenAI use cases is not enough. You need to have a strategy for AI that aligns with your business strategy.
The Strategic Imperative
2023 has crystallized that exploring GenAI use cases is merely the tip of the iceberg.
A strategic imperative beckons for every organization: a technology strategy tightly aligned with broader business objectives in the context of this new and emerging technology. As businesses gear up for 2024, identifying, prioritizing and testing use cases aligned with your business strategy and crafting roadmaps with shorter test and learn cycles, emerge as essential steps in this transformative journey.
How Will You Measure Success?
What is the return on investment? How are you going to measure success? What KPIs will you use? This is where the rubber meets the road over the next couple of years. The product life cycle, and the software life cycle, are speeding up with the advent of GenAI. You have to create that cycle of iterative learning and development. 2024 will be a period of experimentation in AI for many businesses.
They will test use cases that align with their business strategy and build a roadmap for AI-powered product development. When you do your own in-depth research, you will come to the same conclusion I did - GenAI is a gamechanger and requires a holistic approach touching every part of the organization to make the most of it.
Roopa Sudheendra is a Chicago-based executive technology leader, with decades of experience in building digital innovation and enablement functions at leading US organizations. She has worked closely with Accelerance and its software outsourcing partners.
In part 2 of this series, Roopa will look at the opportunities and challenges GenAI poses for software development firms, particularly those based in some of the world’s leading software outsourcing destinations.
