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Did you know that 90% of commercial leaders expect to increase the utilization of AI solutions over the next two years? Or do small businesses that incorporate AI in their workflows boost efficiency by up to 40%?
Without a doubt, AI has cast a rather favorable shadow on how we function. The technology does so by mimicking human intelligence. It processes information, analyzes data, and makes decisions based on patterns.
Unlike humans, AI operates continuously without distraction or fatigue. Many AI tools and frameworks can be helpful, whether automating customer service, optimizing marketing campaigns, or predicting inventory levels.
This translates to reduced manual effort, lowered overhead expenses, and optimal operations for small to midsize businesses (SMBs). If you want to integrate AI and gain a stronger foothold in the market, this blog post is for you.
We’ll explore the top AI tools and frameworks and how you can select them and make them work for you. Let’s get started.
Why Your SMB Needs to Incorporate AI Tools
Here’s a fact: small to midsize businesses are constantly pressured to do more with limited resources. AI enables them to do just that—minus the complexity:
1. Automation
This is perhaps AI’s #1 USP—it automates repetitive tasks that consume valuable time, enhancing productivity.
For instance, instead of manually spending hours answering common customer queries, a small eCommerce store can use an AI chatbot to handle basic support requests.
2. Cost savings
Hiring personnel for every task is not always possible when working with smaller budgets. AI can help minimize labor costs and optimize resource allocation.
For instance, a local accounting firm can use AI-powered bookkeeping software to categorize expenses, update ledgers, and flag potential errors. There's no need to spend time or money hiring and training a bookkeeper!
3. Custom buyer experiences
Personalization is the norm today, and if you have the tools to tell you what a customer prefers, you can make AI-driven recommendations accordingly.
For example, armed with this info, a small clothing brand can send emails based on the buyers’ purchases and browsing histories, resulting in higher engagement and increased sales.
4. Data-driven decision-making
AI can process massive volumes of data instantly and more accurately, which means faster decision-making.
For example, a midsize retail business can leverage AI analytics to track customer buying patterns and predict demand for specific products. This way, they’d always know what to restock, what not to, and when.
6 Best AI Tools, Frameworks, and Libraries for SMBs
Let’s take a look at AI tools and platforms primarily designed for technical implementation in the SMB space:
1. PyTorch
PyTorch is an open-source deep learning framework with a user-friendly front end. It offers multi-model serving, logging, metrics, and the creation of RESTful endpoints for app integration.
Features
- Provides easy scaling through prebuilt images, large-scale training on GPUs, and the ability to run models in a production-scale environment
- Supports a comprehensive workflow—from Python to deployment on iOS and Android
- Enables research in high-performance, low latency, and bare metal C++ systems
SMB use case
- Develop fraud detection models that identify suspicious transactions and prevent chargebacks in real-time
Pros
- Faster debugging due to Pythonic nature
- Is highly compatible with array-based data structures and enables dynamic computation
Limitation
- Requires significant machine learning expertise
2. TensorFlow
TensorFlow is a platform that helps you build, train, and deploy Machine Learning (ML) models for AI apps—whether on servers, edge devices, or the web. Tensor development services also equip you to experiment with APIs like the Keras Functional API and Model Subclassing API.
Features
- Integrates highly scalable data pipelines for loading data
- Preprocesses data and creates input pipelines for ML models
- Enables you to use pre-trained models and ready-to-use datasets for image, text, audio, and video use cases
SMB use case
- Use TensorFlow with computer vision to detect product defects in real-time, reducing manual inspection costs
Pros
- Open-source and widely adopted
- Large community and extensive documentation
Limitation
- Computationally intensive—may need high-end GPUs
Read: How TensorFlow Helps in Supply Chain Anomaly Detection and Product Integrity
3. IBM Watson
IBM Watson is an AI suite that uses Natural Language Processing (NLP) to learn and reason. It was one of the first platforms to release a new intent detection model, forming the frontline of any conversation interface, such as AI agents for business automation and chatbots.
It offers Watson Assistant (chatbots), Watson Discovery (document search), and Watson Studio (ML model development).
Features
- Trains, validates, tunes, and deploys foundation and ML models with ease
- Empower developers of all experience levels to write code with AI-generated recommendations
- Enable employees to quickly offload time-consuming work to tackle more of the work only they can do
SMB use case
- Create personalized employee training programs, analyzing learning patterns and suggesting tailored courses
Pros
- Focus on enterprise AI with industry-specific solutions
- Can be deployed on-premises or in the cloud
Limitation
- Expensive for SMBs compared to open-source solutions
4. Hugging Face
Hugging Face is a platform for developing and using computer vision, audio, tabular data, and reinforcement learning models to build AI tools with enterprise-grade security, access controls, and dedicated support.
It has a vast community space for sharing datasets (text, image, audio), apps, and frameworks.
Features
- Has pre-trained models for tasks like language translation, sentiment analysis, and text generation
- Configures organization-wide security policies and default repository visibility
- Tracks and analyzes code repository usage data in a single dashboard
SMB use case
- Generate blog posts, ad copy, and social media content quickly—a plus for digital marketing agencies
Pros
- The API-based approach makes AI more accessible
- Open-source and free for basic use
Limitation
- Limited to NLP and text-based AI applications
5. Google Cloud AI
Google Cloud AI is a single platform for data scientists and engineers to create, train, test, monitor, tune, and deploy ML and AI models.
You can test sample prompts, design your own prompts, and customize foundation models and LLMs to handle tasks that meet your app’s needs.
Features
- Installs a preconfigured solution that uses Generative AI technologies to extract text and summarize large documents quickly
- Creates a chat app using retrieval-augmented generation (RAG) (basically, questions and answers based on embeddings stored as vectors)
- Make your content and apps multilingual with fast, dynamic machine translation
SMB use case
- Analyze competitor pricing, demand trends, and historical sales data to adjust pricing for better profitability dynamically
Pros
- The learning curve is shallow
- Real-time, step-by-step assistance for human agents
Limitation
- Vendor lock-in—tied to Google’s cloud ecosystem (If you’re not using Google, it won’t make sense to financially invest in it separately)
6. Microsoft Azure AI
Microsoft Azure AI is a cloud AI platform that helps build GenAI apps and APIs at scale using native integration capabilities like Visual Studio and GitHub. It allows you to leverage robust small language models (SLMs) with groundbreaking performance at low cost and low latency.
Features
- Integrate with 1,700+ foundation models from creators such as Microsoft, OpenAI, Meta, and Cohere
- Finds relevant info whenever needed with its retrieval system that uncovers hidden patterns with unparalleled precision
- Maintains a secure and positive digital presence with sophisticated safeguards that help mitigate harmful content, protect users, and enhance trust
SMB use case
- Law firms and HR teams use Azure AI Search to quickly find relevant documents, contracts, and policies from large data sets
Pros
- It offers both no-code and low-code AI tools for SMBs
- Strong security and compliance for enterprise apps
Limitation
- Vendor lock-in with Microsoft’s ecosystem (If you’re not using Microsoft, it won’t make sense to financially invest in it separately)
How to Choose the Best AI Tools and Frameworks for Your SMB
There are certain factors to keep in mind when you’re creating your own AI toolkit. Here’s how to ensure you pick the best one.
- Identify the problem you want AI to solve. Do you want to detect financial fraud, facilitate scientific research, or assist with drug discovery? Be as straightforward as possible for the best outcome.
- AI’s job is to simplify your workflow, not complicate it. Whatever library, framework, or model you choose must have clear setup instructions, intuitive interfaces, and strong customer support.
- Choose an AI tool that grows with your business. Sure, you’re an SMB right now. But you most probably won’t be in a few years. Can your technology scale with your operations?
- Can the selected AI tool integrate with your existing software—CRM systems, accounting tools, eCommerce platforms, and so on? You don’t want to do a complete overhaul.
- Most AI solutions offer free trials or demo versions. Sign up for them and run real tests with your business data and processes to ensure they deliver the promised results before you financially commit to the framework or model.
- If the AI tool handles customer data, ensure it complies with data protection laws (GDPR, CCPA, or HIPAA) and has strong security features. The last thing you want to deal with as a growing business is a data breach!
How Intuz Can Help Your SMB
If we could make one prediction, it would be this: your SMB’s AI success is as much about vision as adoption.
But that doesn’t mean deploying AI into any part of your workflow has to be overwhelming. Nor do you need to hire expensive experts to work on the frameworks, libraries, or systems to create an AI ecosystem that delivers results.
You need a reliable AI development company with experience working on prebuilt AI services, ML/NLP technologies, and software frameworks so you can build what’s best for your SMB. That’s where Intuz enters the picture.
Regardless of your use case, we do all the heavy lifting—mining, labeling, and structuring your data, fine-tuning LLMs like GPT and BERT, designing Proof of Concepts (PoCs), implementing ML models for robust version control, and more.
Don’t believe us?
We practically wrote the guide on building your LLM from scratch.
We’re also experienced in creating smart chatbots, advanced image recognition systems, and language translators via our ChatGPT development services. So when we say we have the know-how, we mean it.
Our fail-proof AI strategy roadmap helps you capitalize on future success with confidence—and you can get it for free by scheduling a one-hour-long call with our AI experts. AI can deliver value at scale—why don’t you get started?
