How to Make AI a Habit with Behavioral Science

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By Jeremy Sack, President and Amanda Currell, VP, Management & Delivery at Material

This article originally appeared in Fast Company.

Organizations are not short on AI tools. They are short on AI habits. Budgets typically address the first; behavioral science addresses the second. But only one of them has been getting the attention it deserves.

Habit drives approximately 40% of our daily behavior. Not conscious choice, not deliberate decision-making, but automatic patterns — formed when behaviors are consistently paired with specific cues and meaningful reinforcement. But the tools that become most deeply embedded in work are not always the ones most heavily promoted by leadership. They’re the ones that find their way into that automatic 40%.

From Useful to Personal

The transition from a tool being available to it becoming indispensable follows a recognizable arc that many large and mid-sized organizations have never made explicit and therefore haven’t designed for. This arc includes three stages:

1. Being useful.

This is the entry point. A tool must solve a real problem in a way that’s immediately recognizable to its user — not to the person who bought or made it, but to the person doing the work.

2. Being proven.

Trust is built at this stage. The technology behaves consistently and the conditions for early adoption are stable enough to enable reinforcement. A tool introduced under pressure, applied to the wrong tasks first or surrounded by shifting expectations will feel unpredictable, regardless of how well it works.

3. Being personal.

Habits are created here. It happens when a tool stops being something people use and becomes part of how they think. At this point, its absence is felt more than its presence.

Each stage maps to a distinct phase of habit formation: from conscious effort to contextual reinforcement to automatic, cue-triggered behavior. Many organizations jump from “Useful” to “Personal” in a single leap, skipping the work of “Proven” entirely. Habits aren’t pre-installed but can develop quickly under the right conditions.

The instinct in most rollouts is to ask for comprehensive change: everyone forced to use the new tool, across all relevant tasks, as quickly as possible. But behavioral research suggests this doesn’t work.

Why Top-Down Deployment Strategies Don’t Work

In studies on cognitive load, researchers found that under pressure, humans default to addition (more tools, more processes, more initiatives) and rarely ask what should be removed. When more tools are mandated from the top-down, there’s little room to build habits within teams’ already crowded toolsets and heavy cognitive loads.

But the smallest foothold in an existing workflow — what behavioral scientists call minimum viable behavior — is more valuable than an ambitious ask that never becomes automatic. The right question isn’t what can the tool do in its fullest form, but what does the smallest useful version of user behavior look like, and do the conditions exist for it to be repeated.

Most deployment strategies are built around organizational moments: the all-hands meeting, the mandatory training session or the leadership cascade. These are moments of high collective attention but are likely the least fertile for habit formation. In studies tracking thousands of daily behaviors over multiple years, Wendy Wood found that habits don’t form in moments of high intention but in moments of low deliberation, when the brain is running on autopilot rather than making active choices.

These are the moments when contextual cues attach most readily:

The person at 4:00pm on a Thursday with three things due tomorrow, running on fumes.
The person drained from a difficult conversation who needs to write it up before they lose the thread.
The person doing the same cognitive task every week who has stopped believing it deserves their full attention.

These are the entry points where a well-timed tool can become indispensable.

In most change processes, authority and understanding move in the same direction. Leaders acquire the new thing, and the organization follows. With AI, that pattern has inverted, especially in larger, more complex organizations. Because AI tools are accessible, have a low barrier to entry and can be learned through practice rather than formal training, many employees have developed a personal AI practice.

This is where social proof becomes a strategic lever. Robert Cialdini’s research shows that people look to peers to determine what is normal. Organizations should make their early adopters visible because a relatable peer’s successful adoption is generally more persuasive than a mandate. The experiences of people already at the “Personal” stage should be amplified to create a roadmap that top-down programs cannot produce.

How to Build New Tech Habits

Design use cases around low-resistance moments.

Not the most impressive demonstration, but the most reliable one. Ask your team: when did AI last save you from something tedious? This is your first use case.

Make early adopters visible.

Aggregate adoption data doesn’t change behavior, but a relatable person on a team, describing a concrete before-and-after does. Find the people already at “Personal” and give them a platform.

Stabilize before you scale.

Pick one workflow, protect it from shifting expectations long enough for a habit to form and then scale. Speed comes from depth, not breadth.

Questions to Ask

Where are the early habits?

Are you amplifying them? Do you know who has already made AI an effective part of how they work? What made it possible, and can others see them doing it?

Are you arriving at the right moment?

Avoid moments of peak organizational attention when individuals are near peak cognitive load.

What is the minimum viable version of the behavior you’re trying to build?

And have you designed an entry point around it?

Design Adoption Around the Right Moments

The fastest path to effective AI adoption runs through the science of how habits actually form. Technology will change, but the ways we form habit will remain the same: through cues, reinforcement, small starting points and finding the right low-attention moments within the working day. By designing adoption strategies around these moments, rather than the ones that make it onto the calendar, you can close the gap between offering access and building habit.

Build AI Habits with Material

The brands most likely to drive efficiency and grow meaningful ROI for their AI initiatives will be the ones able to build habit around these tools. Material has decades of experience optimizing technology, as well as industry-leading expertise in behavioral science. We can help you identify effective ways to drive adoption and create growth through innovative new technologies. To learn how we can help your organization, reach out.

How to Drive Tech Adoption with Behavioral Science

By Jeremy Sack, President and Amanda Currell, VP, Management & Delivery at Material

This article originally appeared in Fast Company.

Organizations are not short on AI tools. They are short on AI habits. Budgets typically address the first; behavioral science addresses the second. But only one of them has been getting the attention it deserves.

From Useful to Personal

The transition from a tool being available to it becoming indispensable follows a recognizable arc that many large and mid-sized organizations have never made explicit and therefore haven’t designed for. This arc includes three stages:

1. Being useful.

This is the entry point. A tool must solve a real problem in a way that’s immediately recognizable to its user — not to the person who bought or made it, but to the person doing the work.

2. Being proven.

3. Being personal.

Habits are created here. It happens when a tool stops being something people use and becomes part of how they think. At this point, its absence is felt more than its presence.

The instinct in most rollouts is to ask for comprehensive change: everyone forced to use the new tool, across all relevant tasks, as quickly as possible. But behavioral research suggests this doesn’t work.

Why Top-Down Deployment Strategies Don’t Work

These are the moments when contextual cues attach most readily:

These are the entry points where a well-timed tool can become indispensable.

How to Build New Tech Habits

Design use cases around low-resistance moments.

Not the most impressive demonstration, but the most reliable one. Ask your team: when did AI last save you from something tedious? This is your first use case.

Make early adopters visible.

Aggregate adoption data doesn’t change behavior, but a relatable person on a team, describing a concrete before-and-after does. Find the people already at “Personal” and give them a platform.

Stabilize before you scale.

Pick one workflow, protect it from shifting expectations long enough for a habit to form and then scale. Speed comes from depth, not breadth.

Questions to Ask

Where are the early habits?

Are you amplifying them? Do you know who has already made AI an effective part of how they work? What made it possible, and can others see them doing it?

Are you arriving at the right moment?

Avoid moments of peak organizational attention when individuals are near peak cognitive load.

What is the minimum viable version of the behavior you’re trying to build?

And have you designed an entry point around it?

Design Adoption Around the Right Moments

Build AI Habits with Material

Building an Orchestrated Brand Experience

2026 Media & Entertainment Trends: Consumer Behavior and Industry Predictions in a Shifting Landscape

Get in touch with us.

How to Drive Tech Adoption with Behavioral Science

By Jeremy Sack, President and Amanda Currell, VP, Management & Delivery at Material

This article originally appeared in Fast Company.

Organizations are not short on AI tools. They are short on AI habits. Budgets typically address the first; behavioral science addresses the second. But only one of them has been getting the attention it deserves.

From Useful to Personal

The transition from a tool being available to it becoming indispensable follows a recognizable arc that many large and mid-sized organizations have never made explicit and therefore haven’t designed for. This arc includes three stages:

1. Being useful.

This is the entry point. A tool must solve a real problem in a way that’s immediately recognizable to its user — not to the person who bought or made it, but to the person doing the work.

2. Being proven.

3. Being personal.

Habits are created here. It happens when a tool stops being something people use and becomes part of how they think. At this point, its absence is felt more than its presence.

The instinct in most rollouts is to ask for comprehensive change: everyone forced to use the new tool, across all relevant tasks, as quickly as possible. But behavioral research suggests this doesn’t work.

Why Top-Down Deployment Strategies Don’t Work

These are the moments when contextual cues attach most readily:

These are the entry points where a well-timed tool can become indispensable.

How to Build New Tech Habits

Design use cases around low-resistance moments.

Not the most impressive demonstration, but the most reliable one. Ask your team: when did AI last save you from something tedious? This is your first use case.

Make early adopters visible.

Aggregate adoption data doesn’t change behavior, but a relatable person on a team, describing a concrete before-and-after does. Find the people already at “Personal” and give them a platform.

Stabilize before you scale.

Pick one workflow, protect it from shifting expectations long enough for a habit to form and then scale. Speed comes from depth, not breadth.

Questions to Ask

Where are the early habits?

Are you amplifying them? Do you know who has already made AI an effective part of how they work? What made it possible, and can others see them doing it?

Are you arriving at the right moment?

Avoid moments of peak organizational attention when individuals are near peak cognitive load.

What is the minimum viable version of the behavior you’re trying to build?

And have you designed an entry point around it?

Design Adoption Around the Right Moments

Build AI Habits with Material

Related Perspectives

Building an Orchestrated Brand Experience

2026 Media & Entertainment Trends: Consumer Behavior and Industry Predictions in a Shifting Landscape

Get in touch with us.

Building an Orchestrated Brand Experience