292 AI Native in Lotus365: Why It’s the Key to Future Technological Success

Artificial intelligence is not new anymore. It has been Lotus365, Lotus365 Login integrated into our daily workflow and is still going on. A new phrase has been tossed around in the AI sphere, and it’s called “AI native”. What exactly is it? Today’s blog aims to discuss native AI and its future.

Let’s not dwell in confusion and learn how we can use this term to define AI products and companies.

What is AI native?

AI native, or native AI, refers to a product or products that have artificial intelligence embedded into their very core. That means the product is completely based on AI, and without it, the product wouldn’t exist in the first place.

Ai native products and Ai native companies

In the past few years, lots of AI-native products have come out, and companies are using it to grow. This changed what we mean by “AI native.” Now, it’s not just about products; it’s also about how businesses work.

When we say a company is AI-native, what does native mean? We mean it’s changing to use AI in its plans to improve sales, market things, help customers, or do other jobs where AI can be useful.

According to Ericsson.com, being AI native means having AI that’s trustworthy and built-in. It is as if AI is a natural part of everything the company does, from how it is designed and set up to how it works every day.

Simply put, a company becomes AI native when AI is an integral part of how it operates.

AI-Native vs AI-Based

In contrast to AI native, AI-based tools refer to an already existing solution that has adopted AI into its workflow to implement newer features for its users.

For example, if you think of Notion, a project management software that has been out there for quite some time, it did not have AI natively when it got launched.

However, as AI became more popular and efficient, they started implementing AI into their software to enhance their product’s usability. This means that this product is AI-based, not AI-native.

Characteristics of AI native

Here are some of the basic characteristics found in an AI native project or piece of software.

Data-Driven

AI-native systems heavily rely on data to function. They use both structured and unstructured data to learn, reason, and make smart decisions. These systems excel at processing large amounts of data to uncover important patterns and insights.

Continuous Learning

AI-native systems are always learning. They keep getting better over time by learning from the data they use. This continuous learning allows them to perform better, be more accurate, and make better decisions. It’s like they’re always getting better at solving hard problems.

Adaptive and Autonomous

AI-native systems can change and adjust based on what’s happening around them. They can be really flexible and work well in different situations. Plus, they can do things on their own without needing people to tell them what to do all the time.

Natural Language Processing

AI-native systems are good with human language. They can understand and generate human language, making communication with them simple. This is useful for things like talking to a computer, understanding emotions from text, and translating languages.

How to build AI native products

If you want your products to be a new native AI product, you first need to address where your company stands in terms of AI research and development. One of the best ways to understand the progress is by looking at Gratner’s AI maturity model.

Understanding Gartner’s AI maturity level

Level 1: Awareness

The initial focus is on raising awareness of the potential of AI. This involves educating employees, recognizing industry trends, and exploring how AI can enhance products or services.

The key is to start discussions about AI solutions and identify areas where they could be useful.

Level 2: Active

After identifying potential AI use cases, the business can begin experimenting with the technology. This may entail creating small AI prototypes, conducting pilot tests, or testing various AI models.

The goal is to pinpoint the most promising use cases and understand the business value of implementing them.

Level 3: Operational

With promising AI use cases identified, the business can now deploy AI at scale. This includes incorporating artificial intelligence into a wide range of systems and processes, including sales, marketing, customer service, and supply chain management.

The goal is to demonstrate AI’s effectiveness and produce positive results.

Level 4: Systematic

At this point, AI has been successfully deployed throughout the organization and is an integral part of the business strategy. The emphasis is shifting to scaling up AI initiatives, aligning them with strategic goals, and creating an AI roadmap for future investments.

The goal is to leverage AI for new business opportunities and revenue streams, with noticeable positive impacts on day-to-day operations.

Level 5: Transformational

In the final stage, the company fully integrates AI into its operations and business models, resulting in significant new revenue streams. AI is being used to transform products and services, develop new business models, and open up new revenue streams.

The ultimate goal is to become an AI-native company, with AI embedded in products and decision-making processes.