Artificial intelligence has been growing by leaps and bounds, fueled by excitement around AI’s current capabilities and potential to drive further efficiencies, improvements and innovation.
McKinsey last year reported that 65% of organizations regularly use GenAI, nearly double from 10 months prior, and a new 2025 McKinsey report indicates that three times more employees are using GenAI for a third or more of their work than their leaders imagine. Meanwhile, a new KPMG survey suggests that 68% of business executives expect to spend $50 million to $250 million on GenAI over the next 12 months – that’s up 45% from the first quarter of 2024.
But despite that growth, if AI were a person, it would still just be a child approaching puberty.
In AI’s next stage of life, it will learn more about what to do and not do, making it more understandable and predictable. AI is not yet completely reliable and trustworthy; according to a recent report, only one-third of U.S. businesses said the majority of the outputs of their AI models are accurate. As a result, businesses are still unsure if they should use AI to make key decisions and act independently, which means they must provide the proper care and feeding to ensure their AI is enterprise-ready.
Chief Technology Officer for Artificial Intelligence at Hitachi Vantara.
Companies are also getting more pragmatic about AI. After investing big on AI experimentation, they now expect to drive real business results with artificial intelligence, so ROI is becoming very important. The recent excitement about China’s DeepSeek, which reportedly has capabilities on par with U.S. models but works at a fraction of the cost and requires far less energy, illustrates how important cost and sustainability considerations around AI have recently become.
At the same time, enterprises are keenly aware they must continue to innovate to remain competitive, and 2025 will usher in exciting new technologies to help enable that. All of that means now is the time to force AI to grow up faster so that it is production-ready for the enterprise while balancing that “enterpriseness” with innovation and business value.
Here’s a crib sheet on how to grow your AI into a trusted and enterprising young adult.
Go back to basics to make AI enterprise-ready
Governance, reporting and security are critical in enterprise environments. But these important considerations, and all they entail, are often overlooked or undervalued when it comes to AI.
As businesses make the leap from experimental to more production-level AI deployments, it is crucial for enterprises to address governance, reporting and security to meet compliance requirements, protect their own and their customer data, and built trust.
Given the level of complexity involved, that can be daunting. But it is essential to accelerate the maturity and adoption of enterprise-ready versions of AI. Understand that you don’t have to go it alone. Collaborate with a partner with deep expertise in technology and your sector, turnkey solutions, products with baked-in scalability and sustainability, and a methodical approach. Together, you can advance the “enterpriseness” of your AI efforts and drive real business value.
Create a solid data foundation for innovation
Data is critical to AI success. The more context AI has, the better results it can deliver. To get quality AI outputs, you need high-quality data. Otherwise, it’s garbage-in, garbage-out.
That’s pretty well understood at this point. But data quality is just part of the AI challenge. The fact that data exists in silos across your far-flung enterprise, and that the bulk of that data is now unstructured, can also interfere with data quality and your company’s ability to use data effectively. Inconsistencies in data collection and stewardship create further complications.
Embrace real-time data processing capabilities and enforce data governance frameworks to ensure your systems meet quality expectations. Employ AI-powered data cleaning tools that sift through massive amounts of data because doing that manually is simply impossible.
Use data catalogs and lineage tracking systems to make it faster and easier to access and understand your data. High-quality data will help ensure the explainability of AI outcomes, which is critical to meeting internal and external regulatory compliance requirements and instilling user trust.
“Trust starts with exposure and evolves with use,” as LinkedIn co-founder and venture capitalist Reid Hoffman writes in “Superagency: What Could Possibly Go Right with Our AI Future.” “Once you learn what something is and how it functions, you begin to trust it. Trust equals consistency over time. In the context of AI, we first must develop trust in the technologies themselves – no easy feat when the technologies are somewhat unpredictable and capable of error.”
You don’t necessarily have to resolve all of your data challenges immediately. However, having at least a basic understanding of your data estate and adopting these approaches and capabilities where and when you need goes a long way in building trust and enabling success.
Be ready for what’s next: Agentic AI
To date, businesses have relied on AI primarily to analyze data to uncover trends and make predictions as well as to automate routine tasks (chatbots in customer service, for example). Much of this work has been highly reactive and typically involves some human supervision.
But now we’re starting to hear more and more about this exciting – and potentially disruptive – evolution of artificial intelligence called agentic AI. As you’re probably already aware, agentic AI systems will be able to make decisions and act autonomously with minimal human intervention.
Agentic AI is a big leap forward and represents most people’s vision for AI. It works independently, takes initiative and self-optimizes. Agentic AI will drive increasing adoption of small language models (SLMs) and often involve the collaboration of smaller AI experts that focus on particular tasks based on their specialized training.
Yet, while agentic AI creates great opportunity, it comes with undeniable risks. That makes it even more critical to prioritize accountability, explainability and responsibility by building robust frameworks to govern these systems and reduce the potential for unintended consequences.
Guiding a human from childhood through their teen years to become a responsible adult who works hard to contribute to society requires ample time and attention. The same is true with AI.
With great power comes great responsibility – and the future is bright.
We’ve featured the best Larger Language Models (LLMs).
This article was produced as part of TechRadarPro’s Expert Insights channel where we feature the best and brightest minds in the technology industry today. The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro
https://cdn.mos.cms.futurecdn.net/FVsNoGEW8hur5KqLGncuBe.jpg
Source link
