Integrating and Implementing Deep Tech into Your Field Operations
Enterprise AI, the strategic application of AI to enhance business operations and outcomes, has become a critical driver of innovation and growth. To effectively harness the power of AI, organizations need a well-defined enterprise AI strategy that aligns with their overall business goals.
Why Do We Care About Deep Tech Integration?
For over a decade, our team has worked exclusively with Fortune 1000 companies to integrate drones and other deep tech like AI and machine learning into their field processes. But we quickly grew frustrated with the inefficiencies of the integration process. It was time-consuming, expensive, and the results were often hard coded to a specific requirement or use case.
This made it difficult to scale these technologies strategically. Even today, 10 years later, this challenge persists. For example, drones don't have the capability or context to know what they're collecting, why they're collecting it, or what to do with it to make it actionable.
Deep tech is advancing rapidly, and businesses shouldn't have to spend months and millions of dollars on development and integration, only to be stuck with one hard-coded tool – there had to be a better way. So, that’s what we set out to build with Cloneable.
Getting Started with Enterprise AI
In our experience working with companies across industries and size, getting started with the integration of any deep tech can seem daunting. Here are some of our best tips for getting started:
Identify business goals and objectives
- Clearly define what you want to achieve with AI. Do you want to improve customer satisfaction, reduce costs, increase operational efficiency, or launch new products and services? Aligning AI initiatives with specific business goals will guide your strategy and ensure that AI investments are aligned with overall business objectives.
Assess data readiness
- Evaluate the quality, quantity, and accessibility of your data. AI models are fueled by data, so having high-quality, well-organized data is essential for successful AI implementation. Identify gaps in data collection, storage, and management, and develop a plan to address them.
Prioritize AI use cases
- Identify specific areas where AI can have the greatest impact. Focus on use cases that address high-priority business problems or opportunities. Prioritization will allow you to allocate resources effectively and ensure that AI efforts are focused on areas of maximum potential value.
Select the right tools and technologies
- Choose AI tools and technologies that align with your specific needs and expertise. Consider factors such as ease of use, scalability, and integration with existing systems. Our no-code platform was built specifically to streamline the deployment of AI models without requiring coding expertise so it can leveraged by technical and nontechnical users.
Build and train AI models
- Develop AI models that address your chosen use cases. This may involve working with your internal data scientists, AI consultants, or external partners. While some of our clients are building proprietary models internally, we also encourage users to turn to datasets that can be leveraged and trained quickly on platforms like Ultralytics (YOLOv8) and RoboFlow. Once you have your model you can feed that seamlessly into Cloneable with a few clicks to deploy and test AI models without writing code, saving time and resources.
Choose a path to deployment
- Our background in drones gave us a firsthand look at the challenges of field data collection. We often used drones, tablets, ground robots, and smart cameras to collect the best data. If you want to deploy your models to the edge via tablets, drones, or IoT devices, you need to consider the speed and cost of deployment, as well as the importance of running your models without connectivity. Cloneable was built specifically to address these challenges. It makes it fast, inexpensive, and easy to deploy your models to the field at scale on any edge device.
Overcoming Common Challenges of Enterprise AI Implementation
While the potential benefits of enterprise AI are immense, implementing and scaling AI solutions can present challenges. Here are some common hurdles to overcome:
Data silos and data quality issues
- Data silos, where data is isolated within different departments or systems, can hinder AI model development and performance. Ensure that data is properly integrated and accessible to AI systems. Address data quality issues such as missing values, inconsistencies, and duplicates to improve model accuracy.
Lack of AI expertise
- The demand for skilled AI professionals far exceeds the supply. Consider training existing employees or hiring external experts to bridge the skills gap. Cloneable can empower non-technical users to apply AI models without coding expertise, reducing the reliance on scarce AI specialists.
Model deployment and maintenance
- Deploying AI models into production can be complex, requiring integration with existing systems and infrastructure. Cloneable simplifies model deployment and management, allowing you to easily integrate AI models into your business processes and make changes when the next version of a model comes around instead of having to recode your application.
Tools to build, train and deploy AI Models for Enterprise Processes
YOLO, Tensorflow, PyTorch, OpenCV, and Roboflow for building and training AI models.
Labelbox, Viso.ai, OpenVINO, Landing AI and Scale AI for AI model labeling , deployment & management.
We also have a short list of individual model developers that we work with on enterprise projects. If you don’t have models in house, we will work with you to outsource model development with best in class developers.
Start Your Enterprise AI Journey with Cloneable
Enterprise AI has the potential to revolutionize businesses, but successful implementation requires a well-defined strategy and a clear understanding of the deep tech ecosystem to find the best tools for your applications and business systems.
Cloneable, our no-code platform for applying deep tech models to business logic, can significantly streamline the process of deploying and measuring the success of AI models, enabling organizations to harness the power of AI without the code, cost or wait of traditional methods.