Automation and artificial intelligence: what 24 companies reveal about how these practices are evolving in the real world
Automation and artificial intelligence have become mandatory topics in leadership meetings at virtually every company these days. Regardless of business size or industry, these two forces are reshaping how organizations operate, make decisions, and deliver value to their customers.
But there is an important distinction that many people still confuse: automating a process and applying AI are not the same thing, even though the two are increasingly intertwined in day-to-day operations. Automating means removing repetitive human effort from a task and having a machine do it instead. Artificial intelligence, on the other hand, involves systems that learn, adapt, and make decisions based on data, which is a very different layer of complexity and possibility. Understanding this difference is the first step toward applying either one of them intelligently.
The good news is we don’t have to stay stuck in theory. The Fast Company Impact Council gathered perspectives from 24 real business leaders on how they are using automation in their organizations, what has changed in recent years, and most importantly, where these technologies are generating concrete impact. The result is a pretty honest snapshot of what is happening in practice, far from the polished conference slides. 🎯
There are cases of companies that turned an internal pain point into a commercial product, design teams delivering functional prototypes in a single afternoon, logistics operations that rethought entire workflows, and even leaders who concluded that not everything should be automated, and that is part of the strategy too. Here is what these 24 cases have to say about the present and future of automation in business. 👇
Consistency, fairness, and the human role in final decisions
Several of the leaders surveyed emphasized that automation works best when it frees up time for high-impact work that only humans can do well. Hala Hanna, from MIT Solve, offered a very direct example of this. In mission-driven organizations, functions like relationship building, equity-centered design, and complex judgment simply cannot be delegated to a machine. What they did was apply AI to scale consistency and fairness across processes. A concrete example: funders spend an average of four minutes reviewing a grant application. MIT Solve created an AI-powered review tool that standardizes evaluations and reduces bias, but keeps people responsible for the final decisions.
This balance between automation and human judgment shows up in virtually every account. Chadwin Sandifer, from Fairleigh Dickinson University, reinforced this same perspective by saying that automation now plays a much larger role than it did just a few years ago, especially in reducing repetitive work and creating more space for the kind of reflection that requires judgment. For him, what changed was how they think about automation: less as a synonym for pure efficiency and more as a matter of experience. If something gets faster but less clear, that is not necessarily progress. The best use of automation is when it removes friction and creates room for work that still depends on judgment and human connection.
Along the same lines, Rukiya Kelly, from FICO, described how automation evolved from an efficiency tool into a tool of discipline. Before, the focus was saving time. Now, the greatest value lies in consistency, visibility, and scale. In impact-driven work, automation helps reduce the manual burden so teams can invest more time in strategy, stakeholder alignment, and tangible outcomes. The goal is not to remove judgment, it is to protect the time for better decisions.
When an internal pain point becomes a product
One of the most interesting patterns that emerges from the 24 accounts is companies that developed automation solutions to solve internal problems and, along the way, realized they had something valuable enough to turn into a commercial product or service. This move is no accident. It is a direct consequence of teams that were close to the problem, understood the workflow well, and had the freedom to experiment.
Kalie Moore, from High Vibe PR, shared how the team did not always think this way. After AI emerged with force, they analyzed their biggest pain points and realized that reporting consumed about 25% of the team’s time. They built internal tools to streamline and improve the reporting process. Now they can quickly extract the insights clients value, improve the relationship with ROI, and free the team to focus on strategic and creative work. That system evolved and became a product they now offer to other teams.
What stands out in these cases is that artificial intelligence entered not as a starting point, but as an accelerator. The leaders first identified where they were losing time, money, or quality, and only then went looking for the right technology to solve it. This sequence makes all the difference. Companies that come to AI without knowing what problem they want to solve tend to spend a lot and deliver very little. Those that start from a real problem arrive at more precise solutions that are faster to implement and easier to measure in terms of return.
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Another relevant point in these accounts is the role of internal culture. When the operational team has room to question processes and propose improvements, results emerge organically. Well-executed automation does not start in the IT department. It starts in the conversation between the people who execute the task every day and those who have a view of the business as a whole. This combination is what transforms a spreadsheet full of workarounds into a robust, scalable system, and eventually into something other companies want to use too.
Design and prototyping at the speed of AI
Among the cases reported, design teams stand out as one of the groups that felt the most direct impact of artificial intelligence in their daily work. Peter Smart, from the agency Fantasy, offered a very concrete example: two years ago, automation in the studio meant faster handoffs. Today, designers build functional front-end prototypes in a single afternoon using Claude Code, with no engineering sprint required. The same shift is happening across every creative discipline they touch. The only standard they maintain is: does the output meet the same quality bar as something built by hand? When it does, they adopt immediately. The tools are advancing fast enough that the answer changes from month to month.
Brad Weber, from InspiringApps, complemented this perspective by describing how routine and repetitive tasks in software deploy pipelines were already automated for some time. Publishing product updates with a single command is already a requirement on some projects. What AI brought to the table was the ability to automate parts of the design and digital product development process that were previously difficult or impossible to automate, including creating project plans, drafting design prototypes, and conducting code reviews.
Productivity on these teams grew in a way that goes beyond the number of deliverables. What the leaders describe is a qualitative shift: the designer can test more hypotheses, iterate faster, and arrive at more refined solutions before even presenting to a client or stakeholder. This reduces rework, improves communication between teams, and accelerates decision-making. When a functional prototype is on the table right at the beginning of the conversation, everyone understands better what is being proposed and alignment happens much faster.
But the leaders themselves warn: speed without direction is still waste. AI delivers fast whatever you ask for, but if the brief is vague or the problem poorly defined, you will have many versions of the wrong problem very quickly. That is why the teams that harvested the best results were those that invested first in clarity, in deeply understanding the user and the context, and only then activated the tools. Technology amplifies what already exists, so the better the process behind it, the better the result AI will help produce. 🚀
Logistics and operations: rethinking workflows from scratch
Logistics operations are, historically, one of the most fertile territories for automation. High volume, constant repetition, margins of error that are expensive, and time pressure at virtually every step of the process. It is no surprise that several of the 24 accounts come from leaders working in this space.
Dennis Anderson, from ArcBest, described how automation gains a larger role every year, evolving from efficiency in isolated tasks to decision support that helps teams operate with greater precision. They have embedded automation across the company, from call routing and document processing to predictive analytics for workforce planning, dock operations, and urban routing. The key, according to him, is aligning automation with operational goals, continuously refining, and keeping the teams closest to these tools involved, so that trust builds and adoption happens naturally.
Justina Nixon-Saintil, from IBM, reinforced that the approach has shifted from automating specific tasks to rethinking entire workflows. The biggest transformation is that people are spending less time on manual steps and more time on strategic thinking, problem-solving, and decision-making that requires human judgment. Automation is no longer just about speed. It is about reshaping how work happens across teams and functions.
Cameron Van Der Berg, from Infravision, brought an infrastructure perspective by describing how automation is critical to the company’s business model. Beyond accelerating tasks, they are increasing systemic capacity by transforming a manual, resource-limited process into something coordinated and repeatable. This means the same workforce can deliver more, with better safety and consistency, unlocking productivity for the entire expansion of the electrical grid.
Artificial intelligence entered these contexts primarily at the prediction and decision layer. Systems that analyze demand history, market conditions, supplier behavior, and external variables to suggest or make decisions in real time. This significantly reduced the volume of exceptions that previously required urgent human intervention. With greater predictability, teams operate with less stress, less waste, and more focus on the decisions that truly require human judgment.
One detail that shows up in more than one account is the importance of not automating the wrong process. Before implementing any solution, some of these leaders invested time in mapping and simplifying the manual process. Because automating a broken process only makes errors faster and more frequent. The practical lesson here is clear: technology does not fix what is fundamentally wrong with the business logic. It needs a solid foundation to work on top of, and building that foundation is the responsibility of people, not the machine.
Education, learning, and the danger of excessive automation
Not every sector embraces automation with the same enthusiasm, and education is a great example of where strategic caution becomes essential. Garret Westlake, from Virginia Commonwealth University, pointed out that education is built on curiosity and reflection, so there is a delicate balance between the efficiencies automation creates and the human interaction necessary for learning. Identifying systems and friction points that inhibit or reduce learning opportunities and connection allows a focus on students as learners. He warned about a real danger in higher education: focusing too much on optimization and automation without leaving room for students to be curious, reflect, and engage as part of their learning process.
Alex Galvagni, from Age of Learning, described a transition from automating tasks and supporting individuals to augmenting thinking and empowering teams. AI now touches everything they do, from content and creative to operations. Teams that used to take days on a deliverable now produce first drafts in hours and go deeper on quality. But what has not changed is human judgment at critical moments: the decisions that shape how young children learn remain firmly with educators and researchers.
Automation as an amplifier of human capability
Scott Brighton, from Bonterra, summed up the philosophy that guides many of these companies pretty directly: they aim to automate processes and work that amplify the human capability at the core of the business. They want salespeople spending time in front of clients, so they automate tasks that eat up time. They want the support team focused on complex customer issues, so they automate the ability for customers to find answers to simple problems. Automation should amplify the center of roles and the capacity of people.
Todd James, from Aurora Insights, brought the perspective of someone using automation to scale a company without building overhead too early. Technology accelerates research, synthesis, drafting, and preparation, which provides more leverage and frees up space to focus on judgment, client relationships, and what truly matters in the business.
Nathan Friedman, from Understood.org, added an interesting layer by talking about personalization. For them, automation is most valuable when it creates broad organizational efficiency, ensuring every team works from the same updated source of truth. And equally important, it allows adding layers of personalization so people get the specific insights they need, when they need them. A concrete example is the company’s internal data bot, which lets anyone ask questions about product or content performance and receive real-time, personalized answers.
Not everything should be automated, and that is strategy
One of the most honest perspectives that emerges from this set of accounts is exactly this: some leaders concluded that certain tasks should not be automated, at least not right now, and maybe never. This might seem contradictory in an article about automation, but it is precisely this kind of careful evaluation that separates companies that use technology with intelligence from those chasing trends without clarity of purpose.
Adam Thatcher, from Grace Farms, was very direct in saying that automation is a new and exciting opportunity every day on the operations side, because it frees up time to focus on the customer service component, something they have no intention of automating. In his words: nobody ever won a client with a chatbot.
James Greenfield, from the creative consultancy Koto, brought a similar reflection by saying that, as a creative consultancy, automation sits a bit at odds with what they do. It is almost the antithesis of the work itself. But, like any business, there is a layer of behind-the-scenes activity that is necessary, and that is where automation has real value. The question has changed: it is no longer whether they can automate, but whether they should. Knowing what to automate and, more importantly, what not to automate, is where judgment lives.
The cases where this decision came up most frequently involved tasks that depend on nuance, emotional context, or situational judgment. Service in sensitive situations, complex negotiations, creative processes that depend on genuine human perspective, these are contexts where automation can create a worse experience, even when it is technically more efficient. Efficiency without perceived quality from the user is not real efficiency. It is just speed. And the leaders who understood this made more balanced decisions about where to invest in technology and where to preserve the human touch.
This kind of evaluation also reveals an important organizational maturity. Companies that can say no to a technological solution because it does not serve their specific context, even if it is trendy, are companies with clarity about their values, their customers, and their strategy. The smartest automation is the kind that serves the purpose of the business, not the kind that exists for its own sake. And understanding where it should not be applied is just as valuable as knowing where it shines. 💡
Continuous maintenance and the circular economy
Another point that came up in the accounts and deserves attention is the matter of continuous maintenance of automation systems. Madeleine Smith, from Civic Roundtable, highlighted that automation has evolved from saving time to shaping how work actually gets done. In the beginning, it was about reducing manual tasks. Now the challenge is continuous maintenance, keeping systems useful, up to date, and actually used, instead of creating more abandoned tools. In government especially, success comes from embedding automation into real workflows so it improves coordination and follow-through over time, not just launching something new.
David Klanecky, from Cirba Solutions, brought the circular economy perspective by describing how automation is a critical part of how they scale operations commercially. It prioritizes consistent capabilities that customers and partners expect. By embedding this foundational tool across the organization, they improved safety and quality standards, generated valuable operational data, and enabled process optimization across facilities. Automation advances competitive differentiators, increases reliability, and sustains growth.
Automate everything repeatable and focus on what matters
Some leaders adopted an even more direct philosophy. Neil Cawse, from Geotab, observed a significant increase in the use of AI to automate as much as possible across the company’s operations. With the cost of software dropping dramatically, the question for him becomes: why not automate like never before?
Effie Carlson, from Watershed Health, shares a similar belief: anything that needs to be done the same way repeatedly is an opportunity for automation. As automations become more accessible, the first question she asks when they are building or scaling something is: how do we make this zero-click?
Logan Mulvey, from GoDigital Music, wrapped up this line of thinking perfectly: automation is no longer just about efficiency. It is about focus. They aggressively offload repetitive work so the team can invest time in judgment, creativity, and relationships. The goal is not to have fewer people. It is to have higher-leverage work per person.
What these cases have in common
Looking at this collection of real-world experiences, a few patterns emerge clearly. The first is that the companies that got the most out of automation and artificial intelligence were the ones that started from real, well-defined problems, not from technologies searching for an application. The order matters: problem first, solution second. This sounds obvious, but in practice it is tempting to go the other way when a new tool shows up surrounded by hype.
The second pattern is that productivity was not measured solely by the volume of completed tasks, but by the quality of decisions, reduction of rework, speed of iteration, and ability to focus on more complex problems. Teams that used automation more maturely did not necessarily deliver more in quantity, but they delivered better, with more consistency and less waste of human energy on low-value tasks. This has a real impact on team motivation, as people get to engage in work that truly matters.
The third pattern is that leadership made a difference. Not technical leadership, but leadership that created the right context for experimentation, tolerated mistakes along the way, and clearly communicated the purpose behind technology adoption. Processes improve when the people who execute them understand the why behind the change and feel like they are part of it. When automation is imposed top-down without explanation or team involvement, resistance is natural and results fall well short of their potential.
And the fourth pattern, perhaps the most important of all: automation works best when it preserves and amplifies what is essentially human. Creativity, empathy, situational judgment, trust-building, these capabilities are not replaced by technology. They are supercharged when technology takes care of the rest. The 24 companies that shared their stories show that the future of automation is not about machines doing everything, but about people doing what truly matters, with the right support to make it happen. 🤝
