President & CTO at ContractPodAi.
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Generative artificial intelligence (GenAI), the latest form of machine learning (ML), is rapidly gaining traction and creating new opportunities in the corporate world. It involves a series of algorithms that help professionals perform routine tasks, like reorganizing and classifying data and creating content, like text, audio, images, and videos. And with each breakthrough in the field, GenAI is having a significant impact across various sectors—from banking to life sciences to legal.
This advanced technology is changing the anatomy of corporate work as we know it, “augmenting the capabilities of individual workers by automating some of their individual activities,” McKinsey and Company says. In fact, GenAI has the “potential to automate work activities that absorb 60 to 70 percent of employees’ time today,” adding millions of dollars to organizations’ bottom line and billions to the global economy. In the banking industry, for example, GenAI has the potential to deliver more than $300 billion in additional value each year, according to McKinsey.
The benefits and return on investment (ROI) of this technology are only as strong as the implementation process that’s put into place. If companies do not know how to adopt and implement GenAI successfully in the first place, the results will fall well short of expectations. So with that, here are three key areas to focus on to get the most ROI from a GenAI deployment:
Using Different LLMs for Different Use Cases
A large language model (LLM) is a type of AI algorithm that leverages deep ML techniques to carry out natural language processing (NLP) tasks. Trained on large datasets, LLMs can recognize, translate, summarize and generate whole new content and LLMS are the building blocks to generative AI offerings. It’s important to choose and implement a GenAI offering that leverages multiple LLMs—each trained for a specific use case—instead of relying on a GenAI offering that only leverages one core LLM to achieve desired outcomes. That’s because one size does not fit all: Some LLMs excel at providing advanced analysis and helping to draw conclusions, while others are simply better at generating content.
Combining Human And Artificial Intelligence
When it comes to GenAI technology, it is also important to keep humans in the proverbial loop. Combining people’s sheer ability to apply their instincts, experience and good judgment to determine the best action for their business with AI’s ability to sift through gigabytes of information in fractions of a second to recommend possible courses of action enables AI to deliver its best results Think of this as building on each other’s respective strengths: imaginative and social, and analytical and quantitative. This so-called "collaborative intelligence"’ will increase human efficiency and effectiveness while maximizing AI value.
Outlining Objectives And Setting Measurable Parameters
For GenAI deployment to be successful, it is best to take a step-by-step approach to adoption and implementation, starting with aligning a process across the enterprise. After thinking about what's central to the company’s digital transformation and putting AI technology firmly in place, carefully outline key objectives and come up with measurable outcomes—what can be reasonably expected in the next six, 12 and even 24 months. Then, use success and failure metrics against a benchmark set and report on them to the company. Finally, learn from the technology assessment and make all the necessary adjustments at each development stage.
Indeed, the age of generative artificial intelligence is only beginning. So, as it evolves at a fast clip—transforming roles and strategies is required by senior leadership. This is especially true as domain- and customer-specific use cases continue to grow and become more and more complex. Take analyzing legal or other documents containing sensitive information, extracting key information from them and producing obligation reports, for example. All of this calls for using a mix of best-of-breed LLMs and collaborative intelligence, and setting clear goals and measuring outcomes, as mentioned above.
But remember, leaders also need to think about the possible challenges around GenAI and manage the inherent risks. And they need to rethink core business processes altogether and determine new workforce skills and capabilities.
It is all par for the course in this new wave of corporate productivity.
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