With gen AI becoming the face of the future, organizations have transformed their operations from a legacy-driven approach to a generative AI-driven approach in handling data science operations, development, and more. As an aspiring data science professional, you need to understand the latest trends in gen AI and work to learn generative AI and implement your learnings in every project in a simplistic yet engaging way. Your objective should be to simplify operations by implementing gen AI to automate and optimize sophisticated tasks in a simple and profound way. Here are some of the latest trends on how gen AI has transformed the current landscape and workflow.
Increased Demand for Domain Specific Gen AI Models
While general-purpose models excel across a wide range of applications, the demand for Generative AI (GenAI) is increasing in various sectors. Coupled with the growing availability of high-performing and commercially viable open-source large language models, there is a strong interest in models tailored to specific domains. By the year 2027, it is projected that over 50% of the GenAI models utilized by enterprises will be focused on either a particular industry or business function, a significant rise from roughly 1% in 2023.
Domain-specific models can be more compact, require less computational power, and reduce the risks of hallucinations that are often associated with general-purpose models. Prepare for the necessity of deploying and managing multiple domain-specific GenAI models to cater to a diverse array of use cases. However, prior to developing your own models, consider seeking out ready-made, domain-specific models that you can train or fine-tune to meet your enterprise requirements.
Sustainable Solutions with Gen AI
The swift integration of generative AI tools has raised immediate concerns for business leaders regarding the negative environmental effects of GenAI, which are being highlighted by both the public and government entities.
It is essential to reduce the energy and resources needed for AI training and development. Customizing renewable energy and infrastructure through green computing methods for both on-premises and cloud services will be essential for AI applications. By the year 2028, it is anticipated that 30% of GenAI implementations will utilize energy-efficient computational techniques, propelled by sustainability efforts. To manage costs associated with energy-efficient computing resources, consider diversifying your suppliers, adopting composable architecture, and implementing edge operations for GenAI across all operational jurisdictions. Additionally, utilizing high-quality renewable energy during the training phase will help lessen the impact on your sustainability objectives.
Resolving Challenges in Data with Synthetic Data
The creation of synthetic data — that is, data generated artificially — facilitates systems in scenarios where real data is costly, inaccessible, unbalanced, or rendered unusable due to privacy laws. By the year 2026, it is projected that 75% of companies will utilize generative AI to produce synthetic customer data, a significant increase from less than 5% in 2023.
The incorporation of synthetic data into models allows organizations to replicate environments and discover new opportunities for product development, particularly in industries with stringent regulations. Additionally, it supports rapid prototyping of software, as well as digital and hybrid experiences.
Emphasize the application of synthetic data in domains that have a direct impact on business expansion, such as the creation of customer segments, journeys, and experiences, along with the training of machine learning models.
More Tailored Gen AI Apps
Organizations have shifted away from the plug-and-play model that was prevalent at the time of ChatGPT's launch, opting instead for a more tailored deployment of GenAI," stated Sagar Samtani, an associate professor at Indiana University's Kelley School of Business and the director of its Data Science and Artificial Intelligence Lab.
Organizations are utilizing GenAI engines for applications that are specific to their industry and workflows, with large language models (LLMs) being trained on data sourced from these particular domains to provide highly customized and targeted use cases. For instance, GenAI can be trained on a financial firm's data to assist its investors in making investment choices. Additionally, companies can train an LLM on their unique processes to develop an agent that guides employees through their workflows.
Gen AI will Make Everyone a Programmer
Employees will utilize Gen AI to assist in the deployment of the technology. They will progressively be able to communicate with Gen AI tools using everyday language to develop Gen AI programs and other software code aimed at automating and optimizing tasks.
Organizations will begin to train their workforce on the utilization of Gen AI, reaching out to various business lines to demonstrate its potential benefits, guide them in envisioning use cases, and illustrate the appropriate applications of generative AI.
Executives will Start Relying on Gen AI for ROI
Now, the pressure is on to demonstrate solid ROIs. Organizations and clients often raise the question, 'Show me the money, show me the value, show me the return on my generative AI investments. GenAI is becoming more like any other service or infrastructure that's been bought, where it's expected to produce value.
Organizations are well on their way. "The ROI of Gen AI," a report conducted in fall 2024 by the National Research Group and commissioned by Google Cloud, found that 74% of respondent enterprises using GenAI are seeing ROIs, with another 35% or so anticipating ROIs within the next 12 months.
The 2,500 executive-level business leaders surveyed for the report listed increased employee productivity as well as improved user experience, user engagement and user satisfaction as the most common measurable benefits they've seen.
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