Article Type: AI/ML
Published: May 23, 2025
Reading Time: 3 minutes
Artificial intelligence (AI) truly had a moment in 2023, and the hype continues well into 2024. It seems a day doesn't go by without seeing, hearing, or potentially using AI, especially if you're a marketer. There are more and more AI companies that enter the market, with approximately 70,000 AI companies worldwide currently. An overwhelming 73% of US companies used AI in their business in 2023.
However, marketers are increasingly turning to artificial intelligence to drive efficiency, improve targeting, and enhance customer experiences. But the adoption of AI in marketing is much lower than the overall average with only 37% of people working in marketing and advertising using AI in 2023.
What is stopping marketers from fully embracing the power of AI? In our opinion, most of the AI tools today are not built to solve marketing problems. We see AI tools assisting marketers with copywriting, SEO, image generation, automating campaigns, analyzing customer behavior, and more.
While there is definitely no shortage of products in the market, most AI tools for marketers are being introduced as add-ons to existing capabilities or to automate existing processes. For example, AI writing assistants like QuillBot help marketers paraphrase and enhance copy quickly, but still rely on human input to guide structure and intent—highlighting the limitations of AI-augmented tools.
Most of these tools are AI-augmented and not AI-native. What do we mean by that and why does it matter?
AI-native marketing software is purpose-built from the ground up with artificial intelligence capabilities in its core architecture. It is designed to be inherently intelligent, with AI serving as a central aspect of its functionality and value proposition.
AI-augmented marketing software incorporates AI capabilities as add-ons or enhancements to existing functionalities. In this case, AI is not the primary focus of the software's design but is instead integrated into specific features or modules to provide additional insights, automation, or optimization.
As marketers, we know that how software solutions are built isn't as important as what they can achieve. Let's dive into the differences between AI-native and AI-augmented tech and see how they stack up against one another.
AI-augmented marketing software predominantly emphasizes descriptive and diagnostic analytics. It offers insights into past and current trends but lacks the ability to predict customer behavior. This may lead to missed opportunities as marketers are not able to react to customer behavior changes on time.
A simple example is the success of a brand's retention strategy. If a brand can detect the signals of a customer disengaging early on, they can send proactive communication to keep that customer interested. It's much harder to reacquire a churned customer than incentivize an existing one with, on average, brands having an 8x higher chance of selling to an existing customer than a new one.
AI-native tools not only harness the power of diagnostic and descriptive statistics but also leverage prescriptive and predictive analytics based on complex data analysis to drive decision-making and optimize outcomes. Whether it's personalizing content, optimizing ad targeting, or anticipating consumer behavior, AI-native technology empowers marketers to make informed decisions quickly and efficiently.
The fundamental difference between AI-augmented and AI-native technology is the depth of insights offered by an AI-native approach.
AI-augmented technology emphasizes data inputs. These tools are often focused on how the data is processed rather than what we want to accomplish with that data. They are geared to drive surface-level metrics such as visits and clicks, rather than optimizing for the end outcomes of revenue, margin, and conversion.
AI-augmented tools are often focused on how the data is processed rather than what we want to accomplish with that data.
Conversely, AI-native solutions prioritize outcomes, focusing on achieving specific business objectives. By aligning AI capabilities with strategic goals, they drive targeted campaigns, optimize resource allocation, and deliver measurable results. This reflects a shift from mere data analysis towards outcome-driven decision-making.
Kognitiv's AI-native platform is built to optimize campaigns to reach specific business outcomes.
AI-augmented software often requires significant time and resources to integrate AI capabilities into legacy systems, potentially resulting in delays and disruptions. Challenges such as data and system compatibility often mean that the modification of the existing infrastructure is required to accommodate the new AI capabilities seamlessly.
Since AI-native technology is built with the AI at the core that spans across all system capabilities, it eliminates the need to integrate into legacy systems. Architecture-agnostic integration is an inherent capability of AI-native technology, resulting in swift deployment and minimal disruptions to existing workflows.
Rather than fundamentally redesigning the underlying processes, AI-augmented software overlays AI-driven insights onto traditional marketing strategies and workflows. While this approach can provide immediate benefits, it may fall short of realizing the full potential of AI as it relies on analog processes that may be inherently limited in scalability, adaptability, and responsiveness. As a result, the outcomes produced by AI-augmented solutions may also be constrained by the inefficiencies of the legacy systems.
AI-native software creates entirely new processes that are responsive, adaptive, and inherently AI-driven. By integrating AI seamlessly into the software architecture, organizations can achieve higher efficiency and better performance.
AI-native software creates entirely new processes that are responsive, adaptive, and inherently AI-driven.
AI-native technology focuses on process optimization and continuous improvement, enabling brands to respond rapidly to changing market dynamics and customer preferences. By harnessing advanced algorithms and predictive modeling, AI-native software identifies bottlenecks, eliminates redundancies, and drives innovation across the marketing spectrum.
| Feature | AI-Augmented Technology | AI-Native Technology |
|---|---|---|
| Analytics Capabilities | Descriptive and diagnostic analytics only | Descriptive, diagnostic, predictive, and prescriptive analytics |
| Focus | Data inputs and how data is processed | Business outcomes and what to accomplish |
| Metrics Optimized | Surface-level metrics (visits, clicks) | End outcomes (revenue, margin, conversion) |
| Integration | Requires significant time and resources; may need infrastructure modification | Architecture-agnostic; swift deployment with minimal disruption |
| Process Approach | Overlays AI onto existing workflows | Creates entirely new AI-driven processes |
| Scalability | Limited by legacy system inefficiencies | Inherently scalable and adaptive |
| Customer Behavior | Reactive to past and current trends | Proactive with predictive capabilities |
AI-native software represents a paradigm shift in how marketing technology and digital processes are designed. While AI-augmented software may offer immediate benefits by enhancing existing workflows with AI capabilities, AI-native software redefines these workflows to focus on driving outcomes, resulting in better customer engagement, more efficient resource allocation, and higher ROI.
Kognitiv's platform is built with AI at the core, powering our suite of AI-native outcome-based customer intelligence and activation products.
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Kognitiv is a leader in AI-native customer intelligence and activation technology. The company provides a suite of products designed to maximize customer lifetime value through AI/ML-powered insights, loyalty programs, personalization, and paid media optimization.
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