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. Marketers are also 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. Most of these tools are AI-augmented and not AI-native. What do we mean by that and why does it matter? We’ll tell you!
What is AI-native technology?
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.
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What is AI-augmented technology?
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.
1. Depth of insights
AI-augmented tech
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 tech
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.
2. Emphasis on inputs vs outcomes
AI-augmented tech
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.
AI-native tech
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.
3. Integration complexity
AI-augmented tech
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.
AI-native tech
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.
Want to create a connected and effective martech stack? Learn more about how to navigate the martech maze.
4. Process automation vs optimization
AI-augmented tech
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.
AI-native tech
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 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.
And the winner is: AI-native
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.
Want to build deeper relationships with your customers and grow a loyal customer base? Let’s chat!
- Track, predict, and optimize your customers’ lifecycles with Kognitiv Pulse. Learn more.
- Enable 1:1 personalization at scale with Kognitiv Ignite. Learn more.
- Launch and manage a successful loyalty program with Kognitiv Inspire. Learn more.
- Intelligently acquire and engage customers across paid channels with Kognitiv Amplify. Learn more.