Stop using Home Assistant automations for everything — here’s when scripts and scenes are better


When you’re using Home Assistant, it’s easy to fall into the trap of assuming that you need to create an automation for everything. Automations aren’t the only option, however. Sometimes using a script or a scene can be a better choice.

The difference between automations, scripts, and scenes

They each have their own strengths

Creating an automation based on Wi-Fi presence as a trigger in Home Assistant.

Automations are built around triggers, conditions, and actions. These are the foundations of a Home Assistant smart home. When something happens in your smart home, and the relevant conditions are met, a set of actions automatically run.

Scripts are very similar to automations. They include a set of actions, but the crucial difference is that scripts don’t have their own triggers. An automation will run when something happens, such as a motion sensor detecting motion, or the humidity level passing a set threshold, while a script will only run when something calls it.

Scenes are a little different. A scene is essentially a list of the states of specific smart home devices. For example, the smart bulb is on, set to full brightness, and a blue color, while the desk lamp is on at 50% brightness, set to bright white.

You can use a scene to set smart home devices to the listed states, such as activating a movie night scene. You can also take a snapshot of the current state of devices to create a scene.

Home Assistant Green

Dimensions (exterior)

4.41″L x 4.41″W x 1.26″H

Weight

12 Ounces

Home Assistant Green is a pre-built hub directly from the Home Assistant team. It’s a plug-and-play solution that comes with everything you need to set up Home Assistant in your home without needing to install the software yourself. 


When scenes are a better choice

You don’t need to set states individually

Knowing when to use scenes is the most obvious of the three different types of logic. When you need to set the states of multiple devices, that’s exactly when it makes sense to use a scene.

You could recreate the same scene using an automation. You could add actions that say, “turn this light on, set it to full brightness, make the color blue,” and add more actions for the other devices you want to use in your scene. This is long-winded and unnecessary; using a scene can be far simpler.

There are two main ways to create scenes. You can build one manually using the Scenes screen in Home Assistant. This lets you add devices and entities and change their states, and see the changes happen in real time in your smart home. When the scene is how you want it, you can give it a name and save it.

The other option is to use the scene.create action. This action takes a snapshot of the current state of the entities you specify and saves that snapshot as a temporary scene. You can make changes to the states of those entities, and then restore them again by calling your snapshot.

For example, if you want to flash your lights to indicate when the doorbell is rung, you’ll want them to return to the state they were in beforehand once the flashing is over. You can use scene.create to take a snapshot of the current light settings, flash your lights, then restore the lights to their previous states using the scene.turn_on action to turn on the snapshot scene.

When using a script makes more sense

Reusable sequences can save you a lot of time

A Home Assistant script action showing fields for the title and message.

Knowing when to use a script is a little less obvious. Scripts are very similar to automations in that they include a set of actions that run in sequence. The key difference is that scripts do not have their own triggers; you need to run them with external actions such as pressing a button, using a voice command, calling them from an automation, or even calling them from another script.

There are a few ways that scripts can be a better choice than automations. For example, if you want a dashboard button that runs a set of actions, you can set one up that calls a script directly. With an automation, you’d need to create a trigger that waits for the button press, but the script can run as soon as the button card is tapped.

Scripts are also a useful way to make complex automations easier to build and debug. A complex automation can be a huge mess of loops, delays, and conditions, but these can also be added to a script. You can move all the complicated logic to the script, and have your automation call the script, so that the automation itself is much cleaner and easier to read.


Enabling an Apple Home scene with Siri.


My favorite smart home scene is the one I use last of all

Give yourself the gift of convenience.

Perhaps the most useful way to use scripts is when you have sets of actions that regularly appear in your automations. For example, I have multiple automations that send a notification to my phone, my wife’s phone, my smart TV, and my Echo Show devices. Instead of having to add the same set of actions to each of these automations, I added them to a script.

My automations can now simply call my “notify” script and pass through the notification message. I only need to add a single action to each automation instead of having to add a whole mess of them.

The benefit of doing this really comes into its own when you need to make a change. If I upgrade my phone, I don’t need to go through every single automation that sends a notification to my old phone and update the action to send it to my new one. Instead, I change one action in one script, and all the automations that use that script are immediately fixed.


Combining scripts, scenes, and automations is the most powerful option

Not everything in Home Assistant needs to be an automation. Often, however, the most powerful option is to combine scripts, scenes, and automations. The automation is the brain, the script contains the actions and logic, and the scene controls the state of your devices. It’s a perfect match.



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ZDNET’s key takeaways

  • Trusted quality data is the backbone of agentic AI.
  • Identifying high-impact workflows to assign to AI agents is key to scaling adoption.
  • Scaling agentic AI starts with rethinking how work gets done. 

Gartner forecasts that worldwide AI spending will total $2.5 trillion in 2026, a 44% year-over-year increase. Spending on AI platforms for data science and machine learning will reach $31 billion, and spending on AI data will reach $3 billion.

The global agentic AI market will reach $8.5 billion by the end of 2026 and nearly $40 billion by 2030, per Deloitte Digital. Organizations are rapidly accelerating their adoption of AI agents, with the current average utilization standing at 12 agents per organization, according to MuleSoft 2026 research. This rate is projected to increase by 67% over the next two years, reaching an average of 20 AI agents. 

Also: How to build better AI agents for your business – without creating trust issues

According to IDC, by 2026, 40% of all Global 2000 job roles will involve working with AI agents, redefining long-held traditional entry, mid, and senior level positions. But the journey will not be smooth. By 2027, companies that do not prioritize high-quality, AI-ready data will struggle to scale generative AI and agentic solutions, resulting in a 15% loss in productivity. While 2025 was the year of pilot experiments and small production deployments of agentic AI, 2026 is shaping up to be the year of scaling agentic AI. And to scale agentic AI, according to IDC’s forecast, companies will need trustworthy, accessible, and quality data. 

Scaling agentic AI adoption in business requires a strong data foundation, according to McKinsey research. Businesses can create high-impact workflows by using agents, but to do so, they must modernize their data architecture, improve data quality, and advance their operating models. 

McKinsey found that nearly two-thirds of enterprises worldwide have experimented with agents, but fewer than 10% have scaled them to deliver measurable value. The biggest obstacle to scaling agent adoption is poor data — eight in ten companies cite data limitations as a roadblock to scaling agentic AI. 

Also: AI agents are fast, loose, and out of control, MIT study finds

McKinsey identified the top data limitations as primary constraints that companies face when scaling AI, including: operating model and talent constraints, data limitations, ineffective change management, and tech platform limitations. 

Data is the backbone of agentic AI

Research shows that agentic AI needs a steady flow of high-quality, trusted data to accurately automate complex business workflows. Successful agentic AI also depends on a data architecture that can support autonomy — executing tasks without human intervention. 

Two agentic usage models are emerging: single-agent workflows (one agent using multiple tools) and multi-agent workflows (specialized agents collaborate). In each case, agents will rely on access to high-quality data. Data silos and fragmented data would lead to errors and poor agentic decision-making. 

Four steps for preparing your data 

McKinsey identified four coordinated steps that connect strategy, technology, and people in order to build strong foundational data capabilities. 

Also: Prolonged AI use can be hazardous to your health and work: 4 ways to stay safe

  1. Identify high-impact workflows to ‘agentify’. Focus on highly deterministic, repetitive tasks that deliver value as strong candidates for AI agents. 

  2. Modernize each layer of the data architecture for agents. The focus on modernization should support interoperability, easy access, and governance across systems. The vast majority of business applications do not share data across platforms. According to MuleSoft research, organizations are rapidly adopting autonomous systems. The average enterprise now manages 957 applications — rising to 1,057 for those furthest along in their agentic AI journey. Only 27% of these applications are currently connected, creating a significant challenge for IT leaders aiming to meet their near-term AI implementation goals. 

  3. Ensure that data quality is in place. Businesses must ensure that both structured and unstructured data, as well as agent-generated data, meet consistent standards for accuracy, lineage, and governance. Access to trusted data is a key obstacle. IT teams now spend an average of 36% of their time designing, building, and testing new custom integrations between systems and data. Custom work will not help scale AI adoption. The most significant obstacle to successful AI or AI agent deployment is data quality, cited as the top concern by 25% of organizations. Furthermore, almost all organizations (96%) struggle to use data from across the business for AI initiatives.  

  4. Build an operating and governance model for agentic AI. This is about rethinking how work gets done. Human roles will shift from execution to supervision and orchestration of agent-led workflows. In a hybrid work environment, governance will dictate how agents can operate autonomously in a trustworthy, transparent, and scaled manner. 

The work assigned to AI agents 

McKinsey highlighted the importance of identifying a few critical workflows that would be candidates for AI agents to own. To begin, an end-to-end workflow mapping would help identify opportunities for agentic use. McKinsey found that AI adoption is led by customer service, marketing, knowledge management, and IT. It is important to identify clear metrics that validate impact. Teams should identify the data that can be reused across tasks and workflows.

Also: These companies are actually upskilling their workers for AI – here’s how they do it

McKinsey concludes that having access to high-quality data is a strategic differentiator in the agentic AI era. Because agents will generate enormous amounts of data, data quality, lineage, and standardization will be even more important in the agentic enterprise. And as agentic systems scale, governance becomes the primary level for control. The data foundation will be the competitive advantage in the agentic era. 





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