As we approach 2025, artificial intelligence (AI) continues to revolutionize enterprise solutions, driving efficiency, innovation, and growth. This blog post explores the cutting-edge trends in AI, delving into transformative advancements that will redefine how businesses operate. From AI agents becoming essential for software vendors to the critical role of data integrity and management, we will uncover how these innovations are shaping the future of enterprise solutions. Join us on this journey to discover the exciting possibilities that AI brings to the business world.

Will AI Agents Become Essential for Software Vendors?

AI agents are set to become as essential as robust APIs for software vendors, marking a new rite of passage. By 2025, software vendors will be expected to provide AI agents that are not only capable and trustworthy but also secure and seamlessly integrated with other systems. These AI agents will play a pivotal role in enterprise AI platforms, enabling businesses to leverage AI without requiring extensive technical expertise.

Revolutionizing AI Agents: From Natural Language Processing to Real-Time Decisions

No-code and natural language environments will enable enterprises to easily extract insights and orchestrate automation across platforms. This accessibility will empower businesses to leverage AI without requiring extensive technical expertise. As the year progresses, we can expect significant advancements in AI agent capabilities.

For instance, consider a large retail company that wants to optimize its supply chain management. By integrating AI agents into their enterprise software, they can automate inventory management, predict demand, and streamline logistics. These AI agents can analyze vast amounts of data in real-time, providing actionable insights and recommendations to improve efficiency and reduce costs.

Data Integrity Will Emerge as a Key Enabler for AI in Enterprise Decision-Making

We’ve all heard that data is the fuel for AI, but its role has evolved. Data is no longer just used to train AI; it now serves as the foundation for AI systems to reference, respond, and make decisions in real time. The relevance, accuracy, and security of this data are critical to ensure AI can function effectively. In 2025, we will see a renewed focus on information management, not only for human consumption but also for optimizing AI’s ability to process, analyze, and act on data in a secure and efficient manner.

The Critical Role of Data Integrity for Relevance, Accuracy, and Security

The relevance, accuracy, and security of data are critical to ensure AI can function effectively.

For example, a financial institution that relies on AI for fraud detection must ensure that the data used by the AI system is accurate and up to date. Any discrepancies or inaccuracies in the data could lead to false positives or missed fraud cases. By implementing robust data governance practices, the institution can ensure that its AI system has access to high-quality data, improving its accuracy and effectiveness.

Additionally, a healthcare provider that uses AI to analyze patient data must ensure that the data is accurate, complete, and secure. This involves implementing data governance policies, using data quality tools, and ensuring compliance with data privacy regulations. By doing so, the provider can ensure that its AI system can make accurate and reliable decisions, improving patient outcomes.

Unlocking AI Potential with Curated, Connected, and Confidential Data

In 2025, data management will cement itself as the cornerstone for organizations aiming to unlock the potential of generative AI and large language models.

Without curated, connected, and confidential data, even the most advanced AI tools, including Natural Language Processing (NLP), will fall short. Poor data management limits AI’s effectiveness, while a strong data strategy unlocks higher-value opportunities. As AI adoption grows, data management will evolve from a backend necessity to a critical skill every company must excel in to drive success.

For example, a manufacturing company that wants to implement AI for predictive maintenance must ensure that it has access to high-quality data on equipment performance and maintenance history. This involves collecting data from sensors and other sources, storing it in a secure and accessible manner, and ensuring that it is accurate and up to date. By doing so, the company can use AI to predict equipment failures and schedule maintenance more effectively, reducing downtime and improving productivity.

Additionally, a retail company that uses AI to personalize customer experiences must ensure that it has access to high-quality data on customer preferences and behavior. This involves collecting data from various sources, such as online and offline interactions, and ensuring that it is accurate and up to date. By doing so, the company can use AI to provide personalized recommendations and offers, improving customer satisfaction and loyalty.

The Strategic Evolution of Data Management in AI

As AI adoption grows, data management will evolve from a backend necessity to a critical skill every company must excel in to drive success. This shift highlights the importance of data in the AI ecosystem. Companies will need to invest in data management tools and technologies that can help them maintain high-quality data and ensure their integrity.

For example, a logistics company that uses AI to optimize its supply chain must ensure that it has access to high-quality data on inventory levels, shipping times, and other factors. This involves collecting data from various sources, storing it in a secure and accessible manner, and ensuring that it is accurate and up to date. By doing so, the company can use AI to optimize its supply chain, reducing costs and improving efficiency.

Global AI Strategy: Adapting to EU and US Regulatory Differences

The EU's comprehensive, preemptive regulations will impose significantly higher compliance costs on its companies compared to those operating in less regulated regions like the US. This disparity will impact multinational enterprises, leading to fragmented AI strategies. Subsidiaries in different geographies will face varying levels of access to and the adoption of new technologies.

For example, a multinational technology company that operates in both the EU and the US may face different regulatory requirements for its AI systems. In the EU, the company may need to comply with strict data privacy and security regulations, while in the US, it may have more flexibility in how it uses and manages data. This regulatory disparity can create challenges for the company in developing and implementing a consistent AI strategy across its global operations.

How to Ensure Compliance While Fostering AI Innovation

Balancing compliance with innovation will become a critical challenge for global organizations navigating these divergent frameworks. Businesses must develop strategies that ensure compliance while fostering innovation. This requires a proactive approach to regulatory changes and a flexible AI strategy.

For instance, a financial institution that operates in multiple countries may need to develop different AI strategies for each region to comply with local regulations. This may involve implementing different data management practices, security measures, and AI models to meet the specific requirements of each jurisdiction. By doing so, the institution can ensure compliance while continuing to innovate and leverage AI to improve its operations.

State-Specific AI Regulations Will Complicate US Compliance

Following nearly 700 legislative proposals related to AI in 2024, states will continue to ramp up their efforts next year, addressing a broad range of issues surrounding AI use, ethics, and regulation. In 2025, state-specific AI laws and regulations in the US are expected to be enacted on a larger scale, presenting significant compliance challenges for businesses operating across multiple states. These state-level regulations will vary in scope and requirements, creating complexity for US operations that must adapt to a patchwork of rules. Companies will need to stay agile and proactive to navigate this evolving regulatory landscape, ensuring they meet compliance standards in each jurisdiction while maintaining consistent AI strategies.

Embracing the Future: Navigating the Evolving Landscape of Enterprise AI

The landscape of enterprise AI is rapidly evolving, with transformative trends shaping the future of business operations. AI agents, no-code environments, and natural language processing are making AI more accessible and powerful. Data integrity and management are becoming critical enablers for AI decision-making, while diverging regulations are creating new challenges for global enterprises. By staying informed and agile, businesses can harness the potential of AI to drive innovation, efficiency, and growth. The future of enterprise solutions is promising, and those who embrace these advancements will lead the way in an increasingly competitive market.

By Mika Turunen, Senior Vice President, Product and Engineering at M-Files