Building a business today presents many challenges, especially for startups. Choosing the right AI platform can significantly impact success. This is particularly true in data-driven fields like healthcare, finance, and manufacturing, where faster, data-driven results are critical.
This post clearly explains AI platforms and provides actionable insights to help startup founders, investors, and marketing leaders use AI for real business growth. We’ll explore artificial intelligence platforms, machine learning, and the increasing importance of AI in today’s business world.
Table of Contents:
- What is an AI Platform?
- Building versus Buying an AI Platform
- AI Platforms in Action
- The ROI of AI Platforms
- Choosing the Right AI Platform
- The Future of AI Platforms
- Conclusion
What is an AI Platform?
An AI platform is your AI control center. It offers a set of tools and services for building, training, and launching AI solutions without starting from scratch.
Think of it as a pre-built LEGO set—you get the blocks and instructions to create impressive AI structures. Many platforms offer pre-built learning models, making getting started without needing advanced degrees easier.
These resources empower businesses to start building with artificial intelligence, from building AI apps and intelligence platforms to leveraging cloud AI and applied AI.
Key Components of an AI Platform
A robust AI platform includes several key components working together. These include data scientists and access to tools that accelerate building your AI application.
Machine learning frameworks provide resources like libraries and pre-built models. MLOps and LLMOps streamline model deployments for reliability and efficiency. Learning operations enable automation within workflows to increase efficiency and scalability.
Large language model (LLM) support provides access to Generative AI tools for creating dynamic images and personalized experiences. Scalability is crucial as data grows to handle workloads without impacting performance. Choosing between different AI technologies and AI services is an important decision for any business.
| Capability | Description |
|---|---|
| Machine Learning Frameworks | Libraries, algorithms, and tools for building and training AI models. |
| MLOps and LLMOps | Practices for managing the lifecycle and scalability of AI models. |
| Large Language Models | Access and integration of Generative AI tools like image generators and chatbots. |
| Scalability | Ability to adapt performance without compromising quality, is essential for expanding operations. |
| Automation | Automating repetitive tasks to improve model workflows. |
| Tools & Integrations | Developer resources for effective deployment. |
| Security | Data protection through access controls and encryption. |
| Support | Help resources such as onboarding, training, and bug tracking. |
These platforms transform software development by allowing data scientists and developers to deploy AI models more quickly. By integrating AI, businesses can create more intelligent workflows and develop new AI capabilities.
Building versus Buying an AI Platform
This post explores AI platforms from several angles. We’ll compare different offerings, discuss choosing an AI platform, and cover practical applications of AI. These insights guide founders and other business leaders when making strategic choices about deploying AI.
Buy a Pre-Built Platform
If speed is critical, buying an AI platform may be best. Ready-made platforms provide tools and languages for rapid deployment.
Reputable options include Google Cloud AI Platform, Amazon Web Services (AWS) SageMaker, and Microsoft Azure AI Platform. These platforms provide a head start by offering learning models, pre-built AI, and access to artificial intelligence platforms and partners.
They offer several benefits, including easy integration with existing systems, access to machine learning operations tools, and customer support.
Build Your Own Platform
Customization is vital for specific needs and strict privacy requirements. Companies like Uber and Syapse have built proprietary AI platforms.
Uber uses its platform to improve GPS accuracy and crash detection using NLP and data science. Synapse uses its platform to enhance cancer care quality using oncology data and AI. While demanding, building a custom platform gives your team full control over AI resources, web services, and overall data protection.
This empowers them to deliver AI solutions with very specific AI capabilities and customer stories, leveraging cutting-edge machine learning models. Building a custom solution also enables deeper integration of open-source components.
The Open-Source Route
Open-source frameworks such as TensorFlow and PyTorch offer unrestricted development. This freedom allows for rapid iterations without vendor lock-in. You can leverage external resources for customization with open-source solutions.
This enables data scientists to explore various machine learning models, including those focused on generative AI. It also allows businesses to create innovative AI applications and deliver AI through custom models tailored to their needs.
AI Platforms in Action
A recent article highlighted a McKinsey survey on AI adoption. Understanding how businesses currently utilize AI is beneficial.
Telecommunications
The communications industry uses AI-powered services to enhance network performance and service quality. AI helps predict and prevent customer churn, improving the quality of service. Companies like Turkcell use NVIDIA technologies, OpenShift by Red Hat, and other platforms. Data scientists and data science teams play a key role in developing and maintaining these AI platforms.
Healthcare
AI leads to faster diagnoses, efficient study analysis, and personalized medical advice. This results in better patient experiences, expanded access to healthcare, and improved services. It also saves hospitals and doctors significant time, leveraging various machine learning models to address various patient care and data protection needs. These solutions ultimately enable hospitals and other healthcare providers to integrate AI into their daily operations.
Manufacturing
AI and machine learning help resolve supply chain challenges and automate tasks. This is particularly important given recent disruptions. AI-powered robotics optimizes production and generates actionable reports. Machine learning operations tools help businesses manage the deployment and monitoring of machine learning models on these platforms, maximizing their value across different areas, like financial services.
The ROI of AI Platforms
The PwC Global Artificial Intelligence Study demonstrates AI’s potential economic impact. Numerous studies and industry forecasts from leading firms such as McKinsey & Company and Gartner project significant productivity gains by 2030.
McKinsey estimates a $1 trillion potential annual value increase for banking alone. This transformative power is anticipated to revolutionize multiple industries through cloud AI, partner solutions, and other AI solutions.
Improved quality control via AI boosts profitability. Brands like Olay have successfully used AI applications and neural networks to increase conversions significantly since 2016. Data scientists increasingly use AI platforms to compare models and develop cutting-edge solutions for customer stories.
McKinsey projects substantial savings in healthcare – 5% to 10%—potentially reducing annual costs by $200 billion to $360 billion. This would improve treatment affordability. It’s evident that the total economic impact of artificial intelligence is significant and continues to grow across sectors.
Choosing the Right AI Platform
Selecting an AI platform requires careful consideration of several factors. According to a Harvard and McKinsey collaboration, AI adoption is projected to generate 5% to 10% cost savings. Aligning with your team and data architecture is crucial.
Different organizations and platforms offer distinct solutions, from pre-configured to customizable, with some ready-made while others need development from scratch. These applied AI solutions allow businesses to deliver quickly and effectively across various business units.
Scalability and Costs
Can the platform handle increasing data, model training, and usage while optimizing processing time? This consideration is critical because internal and external data feeds impact overall data volume. Understanding Azure AI and similar platforms like Google Cloud AI is vital when considering scalable options.
IDC forecasts a nearly 20% compound annual growth rate (CAGR) by 2026 in the nearly $1 trillion global market. Server hardware alone is approaching $100 billion within two years and could potentially reach $250 billion, necessitating careful cost management when choosing an AI platform.
Integration and Tools
Many services and platforms offer pre-built integration with leading business analytics, database, and CRM technologies. Some also offer DevOps support. Most are built on established code and common programming languages like Python. Data scientists leverage tools such as AI Studio and Azure AI Studio to build, test and compare models within their preferred environment. Learning resources also contribute to a better understanding of machine learning and learning operations concepts.
Security and Compliance
Data is fundamental to AI, requiring strong protection and regulated use. Some platforms offer containerization and secure protocols using GPUs. Compliance with regulations like GDPR is vital for protecting personal information.
With almost half of the US population subject to GDPR-style laws, compliance is essential for avoiding penalties and operational shutdowns. This understanding also includes consideration for Amazon Web Services, Amazon Web, Edge AI, and any web services in your operations. Navigating data protection requirements effectively requires familiarity with these guidelines, especially regarding patient care.
The Future of AI Platforms
Opportunity comes with inherent risk, and in AI, it’s non-compliance if operations falter. Research on AI technologies emphasizes speed improvements through logical “data products.” But, the extent of this acceleration is crucial.
McKinsey’s other study suggests potential tenfold speed improvements with proper implementation. A 90% acceleration impacts workflows and output times across various business use cases, making a compliant architecture paramount for mitigating operational shutdowns. This growth underscores the significance of large language models for the future of AI platform providers.
Conclusion
An AI platform is a centralized intelligence system that empowers startups and businesses with robust data capabilities. Strategic choices regarding deployment, selection, or in-house development are critical for leveraging current opportunities and efficiently scaling operations.
Conversations with business leaders highlight the focus on benefits during AI planning. However, considering potential non-compliance risks and their associated penalties, especially under regulations like GDPR (with potential fines up to 20 million Euros and 4% of global revenue), is essential.
Subscribe to my LEAN 360 newsletter to learn more about startup insights.
