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Generative AI and machine learning perform different functions within the scope of the digital workplace. Yet some believe their machine learning investments of the last few years should be thrown out in favor of the former.
Let’s start here: Artificial intelligence, even generative AI, is not going to replace machine learning.
Even the figures show how organizations are approaching the problem. The machine learning market is expected to be worth over $200 billion by 2029, while AI is expected to reach $1 trillion by 2030.
Rather than being replaced by generative AI, companies with existing machine learning investments will have a leg up in future AI exploration.
AI and ML, Perfect Together
While it might look like machine learning is being swallowed by GenAI, what the real advancement OpenAI introduced was that it simply put a web interface on top of a large language model said Ryan Ries, chief data science strategist at Mission Cloud.
As a result, he recommends organizations take a more realistic approach to ML and generative AI investments. "As an organization, I would continue to invest in ML and then just make sure I understand how people want to use it and what kind of interface you would need to give users the best experience."
Meta director of product design Travis Vocino agrees that generative AI will not replace machine learning and points to four reasons why this is the case:
1. Foundational Relationship
Generative AI is a subset of AI that relies heavily on advanced ML techniques, explains Vocino. ML provides the foundational algorithms and data processing capabilities that enable generative AI systems to learn from vast datasets and generate new content. Without ML, generative AI would not exist.
2. Purposes and Applications
ML encompasses a wide range of applications beyond content generation, including predictive analytics, natural language processing, computer vision and more. These applications serve a critical need for various sectors, such as healthcare, finance, and manufacturing, Vocino said, where ML algorithms optimize operations, enhance decision-making and drive innovation.
3. Generative AI Maturity and Limitations
While generative AI has shown remarkable capabilities, it is not a one-size-fits-all solution. Challenges remain, including ethical concerns, bias, and the need for vast amounts of quality data, Vocino added. Its ability to replace investments in ML depends on the specific use case and the maturity of the technology in addressing those needs effectively and responsibly.
4. Strategic Integration and Investment
Vocino urged organizations with machine learning investments to view generative AI as an opportunity to augment and enhance their existing capabilities, not as a replacement. The key is to strategically integrate generative AI into operations and business strategies, leveraging its strengths to create new value while continuing to capitalize on existing ML investments in instances where ML excels.
“Rather than viewing Generative AI as a threat to ML investments, organizations should see it as a complementary technology,” Vocino said. “Continuous learning, strategic integration, and adaptability are key to navigating the evolving AI landscape successfully." He urged any leader looking at existing or potential new investments to approach any investment with clarity around what the business goals they are trying to solve and then identify what the strongest solution is for the problem.
Related Article: It Could Be 5 Years Before We See Productivity Gains From Generative AI
GenAI Advantage
In fact, machine learning give those companies moving into generative AI a distinct advantage, said Maria Sukhareva, a senior artificial intelligence expert at Siemens and a former leader of BMW machine translation project.
“Generative AI is in no contradiction to machine learning so the companies that invested into machine learning infrastructure e.g. Azure Cloud have an advantage now as they can run and deploy those models," she said.
What GenAI offers is the ability to solve many tasks in a zero-shot or few-shot manner. She uses emails as an example. In the past, to classify emails into spam and non-spam, users would need 10,000 emails already labelled with those two classes, then train a model, evaluate it, put into operation, monitor its performance and regularly retrain it — a process that would take months to put into production.
Now, all you need is to ask generative AI whether an email is spam or not spam and you get the answer. The use case implementation dropped to a week with minimal operation efforts.
This, she said, is hurting data labelling agencies badly as there is no acute need for labelled data for many tasks. However, in areas like time-series analysis or predictive maintenance traditional statistical ML performs well and GenAI is not an alternative.
"ChatGPT is a language model. A language model needs language. It is not there to predict stock market based on the data over the past five years or find an outlier in excel, for this. We still very much need traditional ML," Sukhareva said.
Related Article: Components of an AI Strategy
The ML–GenAI Trade Off
The ideal situation is one of trade-offs, said Bloomreach's VP of engineering for conversational commerce, Vikas Jha.
GenAI models are typically more resource-intensive and expensive to deploy compared to traditional ML models. They also tend to have higher inference latencies, which can impact real-time performance in certain applications.
That said, there is a wide range of ML technologies, such as predictive analytics, recommender systems, performance optimization, and classification tools, which are designed and optimized for specific use cases, offering cost-effective and efficient solutions that have been proven to deliver results, Jha continued. These specialized ML models will continue to play a vital role in driving organizational success.
“Organizations should adopt a strategic approach when incorporating GenAI into their technology stack,” he said. "Rather than viewing it as a one-size-fits-all solution, organizations should carefully evaluate the specific requirements and objectives of each application. By selecting the right tool for the right job, organizations can leverage the strengths of both GenAI and traditional ML technologies to achieve their goals."
Max Vermeir agrees. The senior director of AI strategy at ABBYY said investments in ML shouldn't be cast aside like last year's smartphone model. These tools have earned their keep, he said, delivering solid, tangible results that businesses rely on daily. Think of GenAI not as a replacement, but as an ambitious expansion pack to the ML framework — introducing new features and possibilities.
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Organizations should adopt a dual approach, leveraging the strengths of ML for existing operations while exploring the innovative frontiers that GenAI opens. "It's an approach akin to having a seasoned chess master and a quantum computer on the same team — each bringing unique strategies to the board,” he said.