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Forbes
08-07-2025
- Business
- Forbes
From On-Premises To Autonomous Systems: Why AI Agents Are The Next Big Shift In Software
Dr. Son Nguyen is the cofounder & CEO of Neurond AI, a company providing world-class artificial intelligence and data science services. The software-as-a-service market (SaaS), valued at $247.2 billion in 2024, is undergoing its most considerable transformation. For years, tools like Salesforce, Shopify, Asana and many others have significantly revolutionized business workflows. Despite their success, these platforms share a critical gap: They're static. In other words, traditional SaaS operates like a toolbox: It's useful, but only if you manually pick up each tool. Users spend a lot of time on repetitive data entry in SaaS applications, while most operate independently, with limited integration between them. The result? Frustration, inefficiency and missed opportunities. Imagine a software system that autonomously connects your tools, automates repetitive tasks and delivers insights without you lifting a finger. This is possible, as the reality of AI-powered SaaS. The Story Of Software: From On-Premises To SaaS Software has experienced a great transformation over the past few decades. In the early days of computing, software was almost delivered as on-premises solutions where companies purchased, installed and maintained applications on their hardware. Microsoft Exchange for email, SAP ERP for enterprise resource planning and Oracle Database for data management traditionally run on company-owned servers. This model gave you full control over data storage, security configurations, software customization and access management. However, on-premises solutions also came with significant challenges: high upfront costs, complex maintenance and limited scalability. Organizations had to purchase software licenses and necessary hardware, such as servers and storage systems, to run the system, and this ate up significant investments. The cost of hiring or training IT staff to manage installation, configuration and ongoing operations also created a high financial barrier, particularly for SMBs. Additionally, your businesses had to be responsible for maintaining the software and infrastructure, including applying regular updates, security patches, bug fixes and troubleshooting issues. The introduction of SaaS marked a significant shift. It delivers cloud-hosted software accessible via the Internet, eliminating infrastructure burdens. Businesses could now leverage powerful tools without managing backend complexities. Salesforce allows enterprises to manage customer relationships with advanced automation, analytics and personalized marketing campaigns, helping companies like Coca-Cola streamline sales pipelines and boost customer retention. EarthEnable adopted Asana to replace spreadsheets and emails, improving task management and communication. But here is the point: Due to static workflows, many SaaS tools follow fixed processes and rules. This means they can't easily adjust to unique or changing business needs. How can Salesforce, for instance, automatically adjust sales strategies in real time when a competitor launches a disruptive product? Businesses may have to compromise or find workarounds rather than have the software adapt to their requirements. Plus, the heavy reliance on user actions slows processes. SaaS expense management tools require employees to manually enter each expense, upload receipts and fill out forms for every purchase. If transactions from credit cards aren't imported or scanned automatically, users spend extra time on data entry. Noticeably, SaaS applications don't connect well with other software, creating separate 'islands' of data, making sharing information between different tools difficult. Suppose your company uses QuickBooks Online for accounting and Shopify for the online store. If they don't integrate smoothly, sales data from Shopify must be manually exported and then imported into QuickBooks. The finance team will struggle to get a complete, up-to-date view of the company's sales and financials. AI, particularly AI agents, would turn these limitations into opportunities. AI Agents And The Transformation Of SaaS AI agents can autonomously perform complex tasks, make decisions and navigate multistep processes. Unlike traditional SaaS tools that rely on user input or predefined rules, AI agents are proactive and can take action independently. Let's say a user might have to manually export data from Salesforce, import it into Trello, set up notifications and repeat this process for every new deal. This approach is time-consuming and prone to human error and inefficiency, especially as workflows grow in complexity. AI agents are designed to autonomously manage entire processes from start to finish. They can analyze data across multiple platforms, make real-time decisions based on context and historical patterns and execute multistep actions without waiting for user prompts. You could have an AI agent detect a new lead in Salesforce, cross-reference the lead's activity in email and support platforms, prioritize the lead, send personalized emails and update the sales pipeline—all without a human touch. If the lead responds, the agent can further adapt the workflow, notify team members and generate summary reports, seamlessly handling exceptions and changes as they arise. This autonomy is transformative for businesses. AI agents don't just automate individual tasks; they manage the entire process, making decisions and taking actions much like a skilled human would. Their capabilities enable organizations to scale operations, handle thousands of workflows simultaneously and adapt quickly to changing business needs. As a result, businesses can move from static, user-driven processes to dynamic, self-optimizing operations. Understanding the power of AI agents, giant SaaS providers have implemented this technology into their systems, breaking down silos and creating dynamic workflows. Leading customer service SaaS platform Zendesk has integrated generative AI, specifically OpenAI, to handle customer inquiries autonomously. The company has AI agents analyze tickets, suggest responses and solve complex issues, reducing response times and manual effort. Salesforce's traditional CRM required significant manual input for tasks like lead prioritization or follow-ups. With Agentforce (and Agentforce 3, the company's latest installment), AI agents autonomously qualify leads, schedule emails and generate reports, transforming Salesforce into a proactive sales and service hub. GitHub initially required developers to code and debug manually. Introducing GitHub Copilot, the company aims to create a system that can suggest code snippets and complete functions in real time. Conclusion The development from on-premises software to SaaS was a game-changer, but AI agents are writing the next chapter in the story of business solutions. By transforming static, siloed SaaS tools into intelligent, integrated ecosystems, AI agents empower businesses to operate more efficiently and competitively. To stay ahead in a rapidly growing market, businesses should take advantage of AI agents. Integrating these intelligent systems into existing SaaS platforms will unlock new levels of productivity and innovation, ensuring long-term success. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Forbes
15-04-2025
- Business
- Forbes
Five Transformative AI Technology Trends Shaping 2025
Dr. Son Nguyen is the cofounder & CEO of Neurond AI, a company providing world-class artificial intelligence and data science services. Artificial intelligence technologies are changing faster than ever. What started as tools that could answer questions or generate images is now developing into systems that plan, adapt and collaborate like never before. Established giants like OpenAI and Google now share the stage with agile newcomers such as China's DeepSeek—a rising star making waves with fresh approaches to how AI solves problems, proving that groundbreaking ideas can come from anywhere. In 2025, we're witnessing more than just smarter algorithms; we're seeing the arrival of AI systems that learn while they work, autonomous agents that strategize like seasoned professionals and models small enough to fit in your pocket yet powerful enough to compete against supercomputers. These advancements aren't isolated—they're converging to create AI that remembers, modifies and collaborates with human-like continuity. Based on my research experience and running an AI company, let's explore the most important trends poised to reshape AI technologies in 2025. An AI agent is a system or program that performs tasks autonomously on behalf of a user or another system using artificial intelligence techniques. Unlike conventional AI, which waits for instructions and simply responds, agentic AI actively figures out what needs to be done and takes action to achieve specific goals. It uses advanced tools like ML (to learn from data), NLP (to understand and use language) and reasoning (to make decisions). This proactive approach allows it to adapt to new situations, learn from what it does and handle complex tasks. In terms of business operation, an AI agent can autonomously interpret customer requests through NLP, retrieve relevant information and provide personalized responses—all without human intervention. It might even escalate complex issues to human representatives only when necessary, improving efficiency and reducing response times. The increase in interest in AI agents is also reflected in broader technological and market trends in early 2025. Gartner forecasts that agentic AI will be integrated into 33% of enterprise software applications in 2028, compared to less than 1% in 2024. Inference time compute refers to the computational resources and time required to run a machine learning model to make predictions or inferences on new data. This means allowing the model to spend extra milliseconds (or minutes) 'thinking' during real-world use to improve its predictions without requiring retraining. Grok 3's use of chain-of-thought prompting exemplifies how inference time computing can be harnessed effectively. This technique encourages the model to generate intermediate reasoning steps, much like a human would when solving a problem. For instance, when tasked with a complex math problem, it doesn't jump straight to the answer—it breaks the problem into logical chunks, evaluates each step transparently and arrives at a solution that's not only accurate but also explainable. Crucially, inference can be tuned and improved without retraining the underlying model. By prioritizing high-quality training data and enhanced inference-time 'thought training,' we can create significantly smarter AI agents. AI developers don't always publicly disclose precise figures about the parameters of their large language models. However, it's believed that the current generation of LLMs contains one to two trillion parameters (e.g., 1.8 trillion for GPT 4). The next generation is expected to reach even more parameters. This significant jump promises to unlock even more advanced capabilities, including enhanced reasoning, improved contextual understanding and more fluent and nuanced language generation. Still, bigger isn't always better—unless you're training AI to navigate the complexity of human language. While today's trillion-parameter models demonstrate impressive language processing abilities, such as writing emails or summarizing text, their 2025 successors aim to better understand language and context. A very large language model could, for instance, parse a legal document while referencing regional laws, historical court cases and even cultural biases in legal language. Not every AI needs to be a supercomputer. While LLMs have gained much attention, the rise of small language models is equally transformative this year. Smaller models, some with just 3 billion parameters, can punch above their weight. They're able to achieve comparable performance to their larger counterparts while demanding fewer computational resources. This portability enables SLMs to run on personal devices like laptops and smartphones, democratizing access to powerful AI capabilities, reducing inference times and lowering operational costs. Microsoft's Phi-3 represents the most powerful and efficient SLM. This 3.8B smartphone-friendly model handles coding and math problems efficiently. The secret? Better training data. Leveraging high-quality textbooks, code repositories and synthetic exercises, researchers are distilling expertise into compact systems. Forgetful AI is becoming a challenge. Most of the current generative AI and LLMs have the 'memory problem.' They may struggle to recall anything beyond the last few messages and can only effectively parse the most recent prompt, limiting their ability to maintain context in lengthy conversations. The development of near-infinite memory is set to revolutionize this issue. 2025's systems can maintain ongoing conversations and recall all previous interactions over months or years. Google Gemini, leveraging this advanced memory capability, can provide highly personalized and context-aware responses by drawing on a user's entire interaction history. This feature allows it to seamlessly pick up where past conversations left off, adapt to evolving preferences and deliver tailored insights without requiring users to repeat themselves. AI in 2025 isn't just a tool—it's a teammate, a strategist rolled into one. Agentic AI will handle tasks that once required entire teams. Models with tens of trillions of parameters will decode problems we thought were too messy for machines. Yet the most transformative advances might come from the smallest models: SLMs are already putting AI in places it's never been. But the real story of 2025 isn't just about size, speed or even intelligence; it's about how AI fits into our workflows, devices and the mess of our lives. These trends will blur the lines between human and machine intelligence, unlocking productivity and creativity. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
