Artificial Intelligence (AI) has been around for years but its advancements are moving faster than ever, transforming industries at breakneck speed. The potential of AI to transform business functions is clear but navigating its complexities can feel like walking a tightrope.
For Chief AI Officers, CTOs and AI leaders, the role of shaping AI implementation is crucial—and unenviably challenging. The right decisions can position an enterprise for unmatched success while missteps could lead to costly setbacks. But here’s the key: a truly robust AI strategy doesn’t rely on a single type of AI. It’s the seamless integration of traditional, generative and agentic AI that allows organizations to achieve versatility and impact.
This blog unpacks the three types of AI–traditional AI, Generative AI and Agentic AI—before outlining actionable strategies to begin building a robust AI strategy. By the end, you’ll understand how these diverse AI applications can complement one another and transform your organization’s operations.
Understanding the Three Types of AI
Before developing an AI strategy, it’s important to understand the different forms AI takes and their respective roles in enterprise ecosystems. While some possibilities, such as Artificial General Intelligence (AGI), remain largely in the realm of science fiction, these three types of AI are driving real-world innovation today.
Traditional AI
This is the most established form of AI, focusing on pattern recognition, data analysis, predictive modeling, computer vision and digital twins. Traditional AI excels at automated, task-specific solutions. Key applications include fraud detection, autonomous vehicles, supply chain forecasting and customer segmentation.
Think of it as the backbone of AI-driven organizations—a proven technology that delivers efficiency and accuracy. It doesn’t generate new ideas but applies existing knowledge to solve clearly defined problems.
Generative AI
Generative AI represents the next wave of innovation. It creates entirely new content, such as text, images, music or code, based on patterns and knowledge extracted from training data. Leveraging organizational data with LLMs allows you to unlock valuable insights, transforming your data into a powerful competitive advantage.
Enterprises are using generative AI to create tailored ad campaigns, generate customer support responses and even code software. While powerful, generative AI requires more effort to build and fine-tune compared to traditional AI, as outputs can vary greatly depending on the quality of input data and prompts.
Agentic AI
Agentic AI takes innovation further by enabling systems to operate autonomously and make independent decisions without constant human intervention. Unlike traditional AI, agentic AI is autonomous, operating with varying degrees of independence to perform tasks on its own. It is also goal-directed, designed to pursue defined objectives without explicit instructions on how to achieve them. This doesn’t mean humans are no longer part of the decision-making process. Instead, their role shifts to AI orchestrator.
These AI agents are capable of reasoning and decision-making, analyzing information, drawing conclusions and taking decisive actions. They are perceptive, able to interact with their environment by gathering and processing data through machine learning models and other techniques. Additionally, agentic AI is capable of learning and adaptation, continuously evolving its behavior based on data analysis and interactions with its surroundings. This bridges the gap between automation and adaptation, enabling real-time, context-informed choices.
Applications of agentic AI include workflow automation, autonomous vehicles and security breach remediation. For example, agentic AI can be deployed to monitor for vulnerabilities in critical systems, dynamically respond to breaches and proactively adapt security protocols—all with minimal downtime.
While agentic AI’s potential is revolutionary, its development demands a higher level of confidence and risk mitigation given its autonomy.
Practical Guidance for Building an AI Strategy
Building a comprehensive AI strategy tailored to your organization’s unique needs can feel overwhelming. However, the good news is you don’t need to go all-in on every type of AI from the start. A pragmatic, phased approach is often the best way forward.
1. Continue Investing in Traditional AI
If you’ve already started implementing traditional AI, don’t overlook this valuable workstream. These proven, scalable systems are integral across industries and continue to deliver significant impact. Focus on refining and expanding efforts in areas like optimizing supply chain logistics, detecting fraud or enhancing data-driven decision-making.
Continued investment in traditional AI can yield consistent results, maintain internal buy-in and generate measurable ROI to support broader AI initiatives.
2. Accelerate Your Generative AI Efforts
Once you’ve built a foundational level of AI maturity, it’s time to move fast with generative AI. The opportunities to supercharge creativity and personalization are immense. Don’t wait—start leveraging tools to automate content creation, enhance customer support touchpoints and scale personalized marketing strategies now.
Generative AI is advancing rapidly, so prioritize experimentation and training today. Partnering with experienced AI providers can help you stay ahead of the curve and unlock its full potential before your competitors do.
3. Build Toward Agentic AI
Deploying agentic AI is challenging but rewarding. This advanced form of autonomy requires high-quality data governance, robust security measures and confidence in your system’s ability to make critical decisions independently. Success also depends on effective business process mapping and clearly defining outcomes and preferences to guide the AI’s decision-making.
Consider incremental deployments of agentic AI, such as automating routine IT workflows or implementing dynamic monitoring systems for cybersecurity. Small, low-risk applications can pave the way for larger, enterprise-wide rollouts in the future.
Why All Three AI Types Matter in Your Strategy
A successful enterprise AI strategy doesn’t rely exclusively on one type of AI. Instead, it integrates traditional, generative and agentic AI in a complementary way. For example, traditional AI can analyze historical data, generative AI can create new customer engagement content based on that data and agentic AI can autonomously deploy and adjust marketing campaigns in real time.
When these forms of AI work together, they create a powerful ecosystem of automation, innovation and adaptability.
Partner With the Experts
AI is arguably the most advanced and fast moving technology market there is today. Having the right partner on your AI adoption journey is essential to turning challenges into opportunities and complexity into simplicity, helping you achieve ROI faster.
Dell Technologies, in collaboration with NVIDIA, offers end-to-end enterprise solutions through the AI Factory, designed to help you seamlessly integrate AI across your organization. Whether you’re starting with traditional AI or building towards agentic AI, our expertise ensures you’re supported every step of the way.
We’ve guided countless organizations through transformational technological implementations before—and we’re here to do the same for yours.
