As generative AI continues to evolve rapidly, businesses are rethinking their investment strategies, with 92% planning to increase spending in the next three years. However, only 1% report their AI investments are mature. The shift towards agentic AI emphasizes the need for robust data governance, as these systems rely on complex data interactions rather than just algorithms. This raises important questions about oversight, accountability, and how organizations can adapt to the fast-paced AI landscape. Without proper data management, businesses risk facing significant challenges as AI systems transition to more intelligent and self-learning models. Ultimately, the success of AI in the future hinges on prioritizing data before risks arise.
Legacy methods started with the process and many AI controls today start with algorithms; but for agentic AI, proper oversight requires a phase shift that must start with the data
The rapid rise of generative AI is reshaping how companies and researchers view innovation and investment across various industries. In just a couple of years, despite not having fully matured production systems, new AI variants are already changing how we deploy and operate technology. A recent report reveals that 92% of companies plan to boost their generative AI investments over the next three years, but only 1% believe their investments have reached a mature stage.
As organizations grapple with the complex terminology of generative AI—like algorithms and large language models—a new thinking phase is beginning. This phase emphasizes the need for robust data oversight. The fundamental question becomes: how do we ensure reliable management of AI systems that learn and adapt based on the data they process?
The term “pre-training” has gained new significance in 2023, especially as platforms like ChatGPT alter perceptions of AI, affecting cost, cloud systems, and the skills required to operate them. As we move into 2025, organizations are facing yet another evolution: agentic AI, which operates in fundamentally different ways compared to its predecessors. To move forward, businesses must understand how accountability for data is crucial for their AI systems, which rely on extensive data inputs to function effectively.
Legacy data creates complexity for next-generation AI systems. As AI technologies proliferate, the demand for high-quality, usable data to enhance accuracy becomes even more pressing. Data management challenges—like how data is sourced, stored, and shared—often remain hidden, complicating governance and compliance efforts. As AI continues to evolve, companies must critically evaluate their data controls to avoid potential risks.
The shift toward agentic AI requires innovative governance models that prioritize data. Traditional methods will likely falter in addressing the complexities of interconnected AI functionalities. Ensuring ethical practices around data security and privacy will also pose new challenges, particularly with the likelihood of generating synthetic data for improvement.
In conclusion, navigating the complexities of agentic AI will not merely be about adopting new technologies but also about embracing a continuous journey of oversight and governance. As organizations adapt to these transformations, the focus must shift toward making data a primary concern, ensuring a more stable and effective AI future.
Keywords: generative AI, agentic AI, data governance
Secondary keywords: AI systems, investment in AI, data management
What is autonomous AI?
Autonomous AI is a type of artificial intelligence that can make decisions and take actions on its own, without human input. It learns from data and experiences to improve over time.
How is autonomous AI changing strategy?
Autonomous AI helps companies analyze data faster and more accurately. This means businesses can make better decisions and adjust their strategies quickly based on real-time information.
What risks come with using autonomous AI?
Using autonomous AI can bring risks like data privacy issues, errors in decision-making, and potential job losses. It’s important for companies to manage these risks carefully.
How does autonomous AI affect compliance?
Autonomous AI can help organizations stay compliant with regulations by automatically monitoring processes and flagging any issues. This reduces the chance of human error and helps businesses follow the rules.
Are there benefits to using autonomous AI for businesses?
Yes, autonomous AI can save time, reduce costs, and improve accuracy. It allows businesses to focus on bigger picture goals while the AI handles routine tasks and analyses.