We are at the cusp of another transformative era: the mainstreaming of AI. The technological revolution promises to reshape industries, redefine businesses, and touch the lives of every individual. With Generative AI poised to both create and dismantle companies and business models, we find ourselves amidst a profound “reset.”
Witnessing the dawn of the computer age, the rise of the Internet, and the ubiquity of mobile technology have helped me recognize the magnitude of this major industrial revolution. My position puts me in a situation where I can’t help but notice and learn from the unprecedented opportunities and existential threats being faced by incumbents and ambitious startups.
My essay “Resetting ESP and Adtech Industries” discusses the necessity of understanding industry-wide changes amidst such radical shifts. This radical shift is made up of a cascade of smaller shifts, similar to aftershocks following a seismic event.
In this blog and its sequel, I will delve into the industry transformations aided by the very AIs that characterize this new frontier. Join me as we unravel and anticipate the future amidst the waves of technological innovation and industrial metamorphosis.
If we compare our lives today to those of just two or three decades ago, the magnitude of change is staggering—the way we consume news and information (Google Search and social platforms like Twitter/X, Instagram, Facebook), how we entertain ourselves (Netflix and Amazon Prime), how we shop (Amazon and dozens of shopping sites), how we communicate (WhatsApp), and how we network (LinkedIn).
What’s remarkable is that all these transformative changes have occurred within the span of just a single generation. The pace of innovation shows no signs of slowing down.
In the realm of artificial intelligence, this pace is even more accelerated. What once took a generation now transpires in a year, if not months or even weeks, underscoring the transformative era we live in.
Generative AI: The Biggest Platformic Shift We’re Currently Experiencing
Agentic AI: The Next Frontier in Enterprise Automation
In his blog about Agentic AI, Volodymyr Zhukov presents a groundbreaking class of AI systems capable of understanding complex workflows and pursuing goals independently. Unlike traditional AI, agentic AI functions like human employees, interpreting natural language instructions, reasoning through tasks, and adapting to changing conditions.
Here at Netcore, we’re advancing this agentic AI vision through AI Twins—digital personas that bring customer data to life with autonomy, adaptability, and insight. AI Twins integrate AdTech and MarTech data to create precise, real-time representations of customer preferences and behaviors. These twins empower brands to deeply understand each customer’s unique needs, enabling interactions that feel more personal and relevant than ever before. Rather than relying on broad segments, AI Twins allow brands to deliver truly individualized experiences, transforming customer engagement into a dynamic, data-backed dialogue.
With the ability to operate independently, AI Twins let marketers simulate and refine campaigns in a controlled, virtual space, providing valuable insights without the need for real-world trials. Imagine being able to test an offer or a new message strategy directly with a digital replica of your target audience, seeing how each segment responds before a campaign goes live. This capability helps marketers predict which approaches will resonate most, optimize resource allocation, and make adjustments on the fly, bringing unmatched precision to campaign planning.
Here’s what sets Agentic AI apart:
- Autonomy: Operates independently, taking initiative without constant human oversight.
- Reasoning: Makes complex decisions, considering context, trade-offs, and strategic actions.
- Adaptable Planning: Adjusts goals and plans based on changing circumstances, demonstrating flexibility and responsiveness.
- Language Understanding: Comprehends natural language, enabling it to follow complex instructions accurately.
- Workflow Optimization: Executes tasks, moving seamlessly between subtasks and applications to achieve optimal results.
I’m really impressed by Margo Poda’s take on how Agentic AI is driving the next evolution of enterprise AI and what businesses truly need from AI nowadays. She nails it when she talks about the importance of having AI that can handle complex tasks on its own. She emphasizes how AI agents with agentic capabilities—like setting goals, reasoning, and making decisions—can revolutionize how companies operate.
Agentic Workflows enhance AI Agents by adding loops of improvement and feedback. In these workflows, AI Agents create drafts, get advice to improve them, and then refine their work. This helps them produce more precise results because they get detailed feedback and can tweak their work accordingly. This is where good AI outputs become outstanding, like superhuman performance.
These advanced features allow AI to understand language better and seamlessly connect with different systems. This isn’t just about automation; it’s about having AI copilots that navigate through various tasks without needing constant human supervision.
With agentic AI, businesses can expect more accurate results, smoother workflows, and better adaptability to real-time changes. It’s definitely a game-changer!
Next Steps: Exploring The World of Agentic AI
The current practice of expecting LLMs to produce flawless outputs in one go will be challenged with Agentic AI, where workflows enable a more iterative approach. This includes prompting LLMs to reflect on their work, utilize tools like web searches, plan multistep processes, and even collaborate with other agents.
Agentic AI will focus on empowering LLMs to think beyond immediate outputs and engage in a thoughtful process akin to human writing. This will include cycles of drafting, refining, and improving, for more refined and accurate results.
In his presentation about AI Agentic workflows, Andrew Ng talks about a framework that categorizes design patterns for building such agents:
- Reflection: The LLM examines its own work to come up with ways to improve it.
- Tool Use: The LLM is given tools such as web search, code execution, or any other function to help it gather information, take action, or process data.
- Planning: The LLM comes up with, and executes, a multistep plan to achieve a goal (for example, writing an outline for an essay, then doing online research, then writing a draft, and so on).
- Multi-agent collaboration: More than one AI agent work together, splitting up tasks and discussing and debating ideas, to come up with better solutions than a single agent would.
Companies across sectors are exploring exciting possibilities with AI Twins. For example, in the retail industry, an eCommerce brand could leverage AI Twins to enhance its customer loyalty program. By creating digital replicas of high-value customers, the brand could test personalized incentives, such as exclusive offers and product bundles, in a virtual environment before launching them widely. This approach would allow the brand to predict which promotions might drive repeat purchases and increase customer lifetime value.
Exploring the Potential of Agentic AI
AI agents mark a significant evolution from traditional LLM-based chatbots like ChatGPT, which primarily assist with content creation, coding, and pattern-matching tasks. While chatbots utilise large language models to analyse data and generate responses, AI agents enhance this by autonomously making decisions, initiating actions, and adapting in real-time.
Agentic AI represents a significant evolution in AI technology, moving beyond mere language generation to a more sophisticated, interactive, and effective approach in solving complex problems.
This capability transforms them from reactive tools into proactive partners, opening new avenues for business innovation. These agents are set to revolutionize industries by automating complex decision-making processes and boosting operational efficiencies.
Among these advances, AI Twins stand out as transformative tools in optimizing resource use and operational efficiency. By accurately predicting customer journeys and responses, AI Twins allow brands to make informed adjustments to campaigns in real time, minimizing waste and maximizing impact. This adaptability not only reduces unnecessary spend but also boosts customer lifetime value by ensuring that each interaction is timely, relevant, and aligned with individual preferences. In short, AI Twins drive smarter decision-making and enhance long-term brand loyalty, offering brands a practical edge in today’s competitive landscape.
We’re super excited to dive into this exciting frontier and its effects on enterprise AI and modern marketing in our next edition as we explore and build on Co-Marketer and Digital Twin for B2C brands. Stay tuned for it!
