Embedded AI: How It Can Change the Way You Work

When your users need an answer, do they have to leave your app to get it? Maybe they export a spreadsheet into a separate dashboard, or maybe a sensor ships raw readings to a distant cloud and waits. Either way, every trip out costs you time, bandwidth, and your user's focus.

Embedded AI puts the intelligence right where the question comes up. Most explanations stop at chips and sensors, but embedded intelligence runs along a spectrum that reaches into the software your customers use daily. Below, you'll learn what embedded AI is, how it works, where it shows up, and what to watch for as you build it.

What Is Embedded AI?

Embedded AI is artificial intelligence built directly into the device, software, or workflow where a decision happens. It runs in place instead of in a separate app or remote service, so people get answers without switching tools. You'll find it on chips inside sensors and in AI agents inside SaaS products.

Most of that work is inference: the step where a trained model applies what it learned to new data and returns an answer. In plain terms, embedded AI adds automation and generative AI to the software, devices, and systems you already use. If you're building software, that usually means an AI agent that operates directly inside your app, and here's how embedded AI agents work.

Embedded AI vs. Cloud AI vs. Edge AI

Why do these three terms get mixed up so often? They overlap, so the fastest fix is to ask two questions: where does the model run, and where does the user experience it?

Cloud AI runs on remote servers, while edge AI runs on or near the device that collects the data. Embedded AI is the subset built into the product itself, whether that's a chip in a sensor or an AI agent inside an app.

Here's the part that trips people up: software-embedded AI can run in the cloud while still living inside your user's workflow. What makes it embedded is where your user meets it, so an in-app agent counts even when the model sits in a data center.

Embedded AI in Devices vs. Embedded AI in Software

On the device side, embedded AI usually means TinyML, which is machine learning small enough to run on microcontrollers (MCUs). MCUs are the low-power chips inside everyday gadgets, and newer designs add neural processing units (NPUs). An NPU is a processor built to run AI math more efficiently than a general-purpose chip.

On the software side, the AI lives inside a SaaS product or internal app. As ThoughtSpot puts it, "You can place a conversational AI agent directly into your SaaS product." That lets your users "ask questions in natural language and get answers without ever leaving your application."

If that sounds niche, consider the Gartner agentic apps prediction from August 2025. Gartner forecast that up to 40% of enterprise applications would include integrated task-specific agents by 2026, up from less than 5% at the time.

Why Does Embedded AI Matter?

Moving intelligence closer to the moment of decision pays off in a few concrete ways, though the benefits look slightly different on a chip than in an app.

Start with lower latency. When inference happens on the device or inside the app, answers skip the round trip to a distant server, and you feel it whether a sensor is catching a fault or a user is mid-task.

Privacy improves too: data can stay on the device or inside governed systems you already control, so less sensitive information crosses networks.

What about energy and bandwidth? Sending a short result instead of a raw data stream uses less bandwidth, and on battery-powered hardware, fewer transmissions also mean less power spent.

Then there's context switching. Because your users get answers inside the app they're already using, nobody has to export data, open another program, and rebuild their train of thought.

How big is the opportunity? Grand View Research's edge AI market forecast values the global edge AI market at $24.9 billion in 2025 and projects $118.7 billion by 2033. That's a compound annual growth rate of 21.7%, according to the same report.

If you build software, the payoff shows up in your product metrics. ThoughtSpot's page on AI-powered in-product experiences frames the win as adoption, retention, and new revenue streams, delivered without overwhelming your development team.

How Does Embedded AI Work?

Whether you're shipping to a chip or an app, the pipeline looks surprisingly similar. You train or choose a model, fit it to the target, integrate it into your product, and then monitor it in production. Since a model that worked at launch can drift as your data changes, monitoring deserves as much attention as the build.

Choosing the Hardware or Runtime

On devices, your options include MCUs, microprocessors (MPUs), field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), graphics processing units (GPUs), and systems-on-chip (SoCs) with NPUs. Each option trades off power draw, memory, and cost, so the right pick depends on your device's job and battery budget.

Energy often decides the whole design, which is why MLCommons publishes MLPerf Tiny benchmark results for ultra-low-power AI systems. The benchmark scores latency, accuracy, and energy across five edge AI tasks. MLCommons notes that MLMark, an earlier benchmark, stopped short of measuring energy, "which is often the limiting factor in embedded deployments."

What about software? Your runtime is your app plus the platform serving the AI, so the trade-offs shift to response time, security, and engineering hours.

Shrinking the Model to Fit

Most models are too big for a microcontroller out of the box, so engineers compress them. Quantization stores a model's numbers at lower precision, such as 8-bit integers (INT8) instead of 32-bit floating point (FP32).

When a model has weights (the learned values inside it) that contribute little to the output, pruning removes them. Knowledge distillation takes a different route: a small "student" model learns to mimic a larger "teacher" model.

How much does quantization actually save? According to INT8 quantization research posted to arXiv by the Degree-Quant researchers, INT8 models offer about 4x model compression with accuracy comparable to FP32 models. Since comparable accuracy can still mean a small drop, test the compressed model on your own data before you ship it.

Integrating AI into Your Product

On devices, frameworks like TensorFlow Lite, ONNX Runtime, and CMSIS-NN help you run compressed models on constrained hardware. In software, ThoughtSpot outlines three common integration paths: API integration, SDKs and low-code frameworks, and full platforms.

MCP, or Model Context Protocol, is an open standard that lets external AI agents connect to data and tools. When you connect agents through ThoughtSpot's Agentic MCP Server, they can query governed data. ThoughtSpot says developers and product teams can "build once and deploy everywhere across the growing MCP ecosystem."

If you'd rather skip the plumbing, ThoughtSpot Embedded puts analytics and Spotter, its AI agent, in your app with a few lines of SDK code.

Where Is Embedded AI Used, and What Gets in the Way?

You've probably used embedded AI today without noticing, which is kind of the point. Where does it show up? MLCommons reports that its MLPerf Tiny benchmark now runs on doorbell cameras, hearing aids, factory sensors, and battery-powered wearables.

The same placement shapes what gets in the way, too. Why does trust hit harder for software teams? When AI lives inside your product, a wrong answer is your product's wrong answer, and that's what your customers will remember.

Devices and Machines

Here's what those devices do with on-device intelligence:

• Predictive maintenance: Factory sensors spot unusual vibration and flag equipment before it fails.

• Hearing aids and wearables: Sound or motion data gets processed right on the device.

• Smart and doorbell cameras: Cameras detect people or packages locally before sending an alert.

• Keyword spotting: Voice devices listen for a single wake word on a tiny always-on chip.

Software Products and Workflows

You can embed Spotter in custom apps or add AI analytics to a product you already sell, and here's what your peers saw after embedding ThoughtSpot:

• FrankieOne: The team rolled out embedded analytics in sprints, work that previously would have taken months. It now frees 232 hours weekly for analytics work and saves six figures a year in productivity.

• MDaudit: The company posted more than 25% business growth in 2023 and a 40%+ increase in user growth from 2021 to 2023. It also reached market 10x faster than building in-house.

• Act-On: Within 30 days of launching its embedded AI search experience, report usage was more than 60% higher than with its old reports.

• Publicis Sport & Entertainment: Client onboarding dropped from six months to just two or three weeks, and the team saved over 1,000 hours in 2024.

What Are the Biggest Challenges with Embedded AI?

Most constraints get sharper the closer the model sits to your users, so plan for these early:

• Power and memory limits: A microcontroller may have only kilobytes of memory, so every model has to earn its space.

• Cross-platform fragmentation: Chips, operating systems, and frameworks vary widely, and a model tuned for one target often needs rework for the next.

• Security and privacy: Edge devices can be physically tampered with, and in-app AI has to respect who can see which rows and columns.

• Access controls: ThoughtSpot's guide to securing AI models and data walks through row-level and column-level security controls.

• Permissions for outside agents: ThoughtSpot's Agentic MCP Server uses OAuth 2.1, a standard authorization protocol, so the permissions you've already set carry over.

• Accuracy and trust: In the McKinsey AI trust survey of about 500 organizations, 74% of respondents identified inaccuracy as a highly relevant AI risk.

• Consistent answers: Governed semantics and deterministic queries return the same result for every user; here's the agentic semantic layer explained in plain terms.

• Answers you can check: Every Spotter answer shows its Spotter query tokens, so you can check how the result was computed and edit them if something's off.

Start Embedding AI in Your Product

The best AI is the kind your users never have to go looking for. Whether it runs on a chip or inside your app, embedded AI earns its keep by answering questions right where they come up.

Ready to put Spotter inside your own product? Explore the ThoughtSpot Embedded analytics platform to see how you can add AI-powered analytics with just a few lines of code. You can start with our free Developer Edition, which lets you scale embedded analytics at no cost for one year.