Enterprise AI: Deep Tech, Applications, and Integration

Enterprise AI

The strategic application of AI to enhance business operations and outcomes.

What is Enterprise AI?

Enterprise AI is the use of artificial intelligence (AI) to improve business operations and outcomes. AI is a broad field that encompasses many different technologies, including machine learning, natural language processing, and computer vision, which can be used to automate tasks, improve decision-making, and create new products and services.  

How will AI scale your workforce?

Many companies face the problem of being overly reliant on a small handful of experienced people to drive the success of their field service function. This can lead to slow and costly processes, a lack of transparency, and difficulty in scaling expertise.  

How do you train a technician with one year of experience to match the decision-making abilities of someone with four decades in the field? This is the idea behind Cloneable. How can we empower an entire workforce with the collective intelligence that technology can provide.

Just as people can perform a wide range of tasks, AI-powered software robots can automate and streamline processes, identify trends with unmatched speed and consistency. Unlike humans, they never tire or require breaks, ensuring continuous operation and enhanced efficiency.

 AI-powered software robots can automate and streamline enterprise processes, identify trends with unmatched speed and consistency

By investing in AI, companies can 'clone' the expertise and knowledge of their internal experts, leading to faster, more transparent, and more scalable decision-making.

What are the business benefits of Enterprise AI?

Enhanced Decision-Making

  • AI can analyze vast amounts of data and identify patterns and insights that would be difficult or impossible for humans to find.  

Increased Efficiency and Productivity

  • AI can automate many time-consuming tasks, such as data entry and analytics.  
AI applications can automate many time-consuming tasks across the enterprise, such as data entry and analytics.

Improved Customer Experience

  • AI can be used to personalize customer interactions and provide more relevant recommendations.  

Reduced Costs

  • By automating tasks and improving efficiency, AI can help businesses reduce costs.  

Reduced Risk

  • AI can be used to identify and mitigate risks, such as material management and improved employee safety.  

Improved Compliance

  • AI can be used to ensure that businesses are complying with all relevant regulations.
AI can be wrapped into custom applications that enable decision-making at the point of data collection.

Greater Agility

  • AI can help businesses adapt to changing market conditions more quickly.


  • AI can be used to optimize operations and reduce environmental impact.

What is Deep Tech and how does it impact my Enterprise AI strategy?

Deep tech is a type of technology that is based on cutting-edge scientific advances and has the potential to solve complex problems and disrupt existing industries.

Examples of deep tech include artificial intelligence (AI), machine learning, natural language processing (NLP), computer vision, and robotics. These technologies are all highly complex and require significant investment to develop, but they have the potential to revolutionize the way we live and work.

Imagine that you are trying to solve a complex puzzle. You can use trial and error to try to solve the puzzle, or you can use a powerful computer to help you solve it. Deep tech is like using a powerful computer to help you solve a complex puzzle. It can help you to find solutions that you would never be able to find on your own.

There are a variety of deep tech ‘types’ that our partners are working with most closely to integrate today:

Artificial intelligence (AI)

  • AI is about creating machines that can think and learn like humans. AI systems can be used to solve a wide range of problems, from automating tasks to making predictions.

Machine learning (ML)

  • ML is a type of AI that allows computers to learn from data without being explicitly programmed. ML algorithms can identify patterns and trends in data, and then use this information to make predictions or decisions.

Natural language processing (NLP)

  • NLP is a type of AI that allows computers to understand and process human language. NLP algorithms can be used to translate languages, generate text, and understand the meaning of sentences.

Computer vision (CV)

  • CV is a type of AI that allows computers to see and understand the world around them. CV algorithms can be used to identify objects, track movement, and analyze images.
Cloneable AI Application on Drone enables real-time inspection analysis and decision-making for drone operations.


  • Robotics is about building machines that can perform tasks automatically. Robots are used in a wide range of industries, from manufacturing to healthcare to space exploration.


  • LiDAR sensors are used to create a 3D map of the environment in order to measure and identify things in the real-world, accurately.  
Scan and measure trees with Cloneable. Rapid collection of tree measurements using mobile sensor technologies for iOS.

GIS (Geographic Information Systems)

  • Spatial analysis and data visualization to collect, store, analyze, and visualize geospatial data. (While GIS is a mature technology, it can still be considered deep tech because GIS systems often require specialized knowledge and skills to develop, implement, and use effectively.)

In the context of enterprise AI, these deep tech types can be used individually or in collaboration to develop specific applications that can solve complex business problems. For example, deep tech-powered applications can be used to:

Deep Tech Integration

Deep tech integration in enterprise is the process of combining deep tech technologies with enterprise systems and processes. This can be a complex and challenging task that requires buy in across an organization, but the investment now will be key to future-proofing a business faced with rapid advancements in the workforce and technologies they utilize.  

There are some obvious challenges associated with deep tech integration today including,  

High cost

  • Deep tech applications can be expensive to develop and implement, which can be a barrier for entry for small and medium-sized businesses.

Long time to market

  • Deep tech applications often take a long time to develop and commercialize, which can make it difficult for businesses to keep up with the pace of innovation.

Lack of skilled workers

  • There is a shortage of skilled workers with the expertise to develop, implement, and maintain deep tech applications.

Regulatory hurdles

  • Deep tech applications may be subject to complex and evolving regulations, which can make it difficult for businesses to bring them to market.

Cultural resistance to change

  • Deep tech applications can lead to significant changes in the way businesses operate, which can create resistance from employees and other stakeholders

Deep tech is rapidly evolving to make it accessible to technical and non-technical users alike. This opens new opportunities for businesses to experiment with deep tech integration in specific areas or processes, ensuring that the cost/benefit, management, and ownership are aligned with their strategic AI goals.

Case Studies: Unleashing the Power of Deep Tech in Real-World Applications

For example, here are a few case studies of companies that have successfully integrated deep tech into their enterprise systems. These use cases are just the beginning of how deep tech integration will revolutionize business processes.  

Integrating enterprise AI, computer vision and deep learning to automate various tasks across agricultural operations has helped seed and chemical companies improve crop yields while reducing environmental impact.
  • Enel utilizes deep learning to enhance its grid operations, enabling predictive maintenance of its infrastructure, optimizing energy generation and distribution, and reducing downtime. Deep learning algorithms analyze sensor data from millions of grid components, enabling early detection of potential anomalies and faults.  
  • John Deere employs computer vision and deep learning to automate various tasks in its agricultural operations. These advancements have helped farmers improve crop yields while reducing environmental impact.
  • Rio Tinto leverages deep learning to optimize its mining operations, improving efficiency and safety. Deep learning is used to analyze geological data, enable more accurate mineral deposit detection and resource planning.  
  • UPS employs machine learning to optimize its delivery routes and transportation networks, reducing fuel consumption and improving delivery times.
  • Siemens utilizes deep learning to enhance its manufacturing processes, improving product quality and reducing costs.  

Future-Proof Your Business with Enterprise AI

Ready to take the next step? Cloneable Can Help.

Cloneable is a platform that makes it easy to apply deep tech models to business logic, all without code. This makes it possible to create highly specific and customizable AI applications that can be deployed to the edge for better data collection, processing and analysis.  

Cloneable can be used to integrate deep tech models from a variety of frameworks, open and proprietary. It can also be used to connect and combine deep tech models to a variety of business systems and data sources.

Cloneable is a valuable tool for businesses that want to integrate deep tech creating powerful and effective AI applications on any field device.  

Browse our resources and check out this demo of Cloneable as you get started with Edge AI.

Take the next step


Start building intelligent apps with Cloneable.


Start building intelligent apps with Cloneable.

Save engineering time, use Cloneable with your stack.


Share an idea with us and we’ll build you a workflow, no strings attached.