Interactive edition · 5 chapters
The UltimateGuide to Designingfor AI ROI
Why productivity gains from AI don't automatically become returns, and how to design a stack where they do. Every chapter has something to try.
The 12% productivity tax
Your teams feel faster with AI. So why isn't the return showing up? The answer lives in the moments around the work.
AI is supposed to make work easier. And in many ways, it is.
Your teams are producing more, and work is getting done faster. In our research, over 80% of workers say AI helps them feel more accomplished at the end of the day.
So why aren't you seeing the returns you expected? Because isolated productivity gains don't automatically translate into ROI. If your teams are still spending time switching tools, reconstructing context, and coordinating across systems, those gains are quickly offset.
The breakdown isn't in the work itself. It happens in the moments around the work, when spikes: switching between tools, tracking down information, coordinating with others, deciding where work should live. Half of workers say it's unclear which tool to use for certain work, and nearly one in four say that uncertainty impacts their productivity.
Individually, those moments feel small. But across your organization, they add up quickly. Even a single hour per day spent searching for information, switching systems, or repeating context adds up to a 12% on your workforce.
This is where your ROI starts to break down, and what slows the . You're investing in AI to accelerate productivity, but fragmented workflows are quietly limiting the return. There's a persistent gap between your tool capabilities and the value your team actually realizes from them.
In this guide, you'll learn how to close that gap by reducing the hidden effort slowing your teams down, connecting work across your organization, and designing AI to work the way your people do, so productivity gains turn into real ROI.
What share of workers say AI helps them feel more accomplished at the end of the day?
And yet the returns lag. Keep reading for why.
Each square is 1% of your team's working time. Assumes an 8-hour day and 230 working days. The guide's figure: one hour a day ≈ 12%.
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Why tools alone don't deliver ROI
Every tool works on its own. The cost appears in the seams between them, and AI added as one more destination widens those seams.
You invest in tools to make work easier. Your teams would agree the tools work. But the issue isn't capability. It's that those tools require extra effort when used together.
Now AI is being introduced into that same environment. In many cases, it's added as another , another interface, another place where work happens. And in already fragmented workflows, that doesn't reduce cognitive load. It increases it.
Added as yet another , AI increases cognitive load in workflows that are already fragmented.
Instead of simplifying work, AI introduces more decisions, more output, and more coordination across already disconnected systems. This is why AI ROI falls short. Not because the technology isn't capable, but because it's being applied within workflows that weren't designed for it.
Designing for AI ROI
Lower cognitive load, fewer decisions, context that carries. Here's what high-ROI environments do differently, and what to design for.
In high-ROI environments, work feels different. Cognitive load is lower, and there are fewer decisions about where work should happen. Context carries across tools, and AI supports how work moves instead of adding effort around it.
This is what a well-designed makes possible, not by replacing your tools, but by connecting them.
High-ROI environments aren't defined by the tools they use, but by how those tools work together. They tend to share four characteristics.
Deep-dive note: what "ubiquity" means in practice
The guide's test is simple: people shouldn't have to go looking for AI. In an evaluation, watch a real task end to end and note:
- How many times someone leaves the tool they're in to reach the AI.
- How often they paste in context the AI could already have seen.
- Whether help appears without a prompt at the moment it's useful.
Deep-dive note: permission-aware context
"Context moving" only builds trust when it respects access. Ask vendors how the AI decides what it can draw on for a given person, and how existing permissions in each connected system carry over to answers.
Deep-dive note: where the 12% shows up
The guide locates the biggest drops in the seams: coordinating with others, switching between tasks, and gathering information. Those three are where to measure before and after any platform change.
That's where ROI comes from. Not from adding more tools, but from reducing the effort required to move work forward.
When tools work together, your teams get more than productivity. They get the time and space to think more deeply, work more creatively, and focus on the parts of their jobs that require human taste and judgment.
How to evaluate AI platforms through an ROI lens
To evaluate AI platforms effectively, start by evaluating how work actually happens today, and where it breaks down.
1. Audit your current AI stack
Before evaluating a new platform, take a clear look at what you already have in place. You probably aren't starting from zero. AI is already embedded across the tools your teams use every day (email, messaging, documents, project management, CRM), each with its own assistant or set of features. But when they operate independently, the productivity tax sets in: Work slows down, context is lost, and AI starts to require more effort than it saves.
Use this template to map how AI is actually being used across your organization and where it's helping or hurting the flow of work. This step gives you a baseline for ROI and reframes the goal: Improving AI ROI isn't about replacing your existing tools. It's about making them work better together.
AI Stack Audit Template
Example rows2. Ask the right questions
A platform needs to be evaluated based on how it changes the way work happens, not just what it can do. That means asking different questions than you would for a point solution.
These questions shift the focus from features to outcomes and from isolated performance to real ROI.
Tap a card to flip from the old question to the better one.
3. Use a structured evaluation checklist
Once you've identified your priorities and reframed how you evaluate, the next step is to assess platforms consistently.
A structured evaluation framework ensures you're measuring what matters: how well a platform reduces effort, connects workflows, and fits into your environment.
Use The AI Platform Checklist to assess vendors across these dimensions and compare them consistently. Inside this practical evaluation template, you'll find:
How platforms multiply AI ROI
A platform doesn't replace your stack. It makes each tool worth more by ending its isolation.
You've already invested in tools designed to make your teams more productive. You've added AI to accelerate that work. But when those tools operate independently, their value is limited, and your teams carry the burden of making them work together.
That's where ROI gets lost. It's also what slows the last mile of AI, the gap between what your tools are capable of and the value your teams actually realize from them.
A platform like Superhuman closes that gap.
It doesn't replace your existing stack. It makes it work better. By connecting your tools, carrying context across workflows, and embedding AI into how work actually happens, a platform turns isolated capabilities into a coordinated system. And that shift has a multiplier effect.
When AI is accessible across tools, adoption increases because your teams don't have to go out of their way to use it. When context moves with the work, time isn't lost reconstructing information. When workflows are connected, effort isn't spent switching, coordinating, and piecing things together.
The value of each individual tool increases because it no longer operates in isolation.
That's how ROI scales. Not by adding more tools, but by closing the gap between what your tools can do and how easily your teams can use them, so your investments translate into real, measurable value.
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Superhuman (formerly Grammarly) is the AI productivity platform on a mission to unlock the superhuman potential in everyone.
The Superhuman suite of apps and agents brings AI wherever people work, integrating with over 1 million applications and websites. The company's products include Grammarly's writing assistance, Coda's collaborative workspaces, Mail's inbox management, and Go, the proactive AI assistant that understands context and delivers help automatically.
Founded in 2009, Superhuman empowers over 40 million people, 50,000 organizations, and 3,000 educational institutions worldwide to eliminate busywork and focus on what matters.
Learn more at superhuman.com/solutions/enterprise.