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Agentic AI is redrawing the boundaries of value creation in corporate America. Gartner projects that by 2028, 33% of enterprise software applications will incorporate agentic AI, and at least 15% of daily business decisions will be made autonomously by AI agents. The AI race isnt about building the most sophisticated algorithms, its about whether employees actually adopt these digital collaborators and use them to expose inefficiencies long hidden in plain sight. Yet many business leaders are still grappling with how to integrate agentic AI seamlessly into existing operations, and deliver meaningful results. A recent MIT Nanda report found that 95% of AI pilots fail. The core barrier? The learning gap, a disconnect between what the tools can do, and how organizations leverage them. The same report noted that buying AI solutions from specialized vendors succeeds around 67% of the time, while in-house builds succeed only a third as often. RETHINK WORK AT THE ATOMIC LEVEL Agentic AI isnt just another automation tool, its a new way of automating. Its true potential lies in reimagining work at its most granular level: breaking down complex processes into smaller, modular components that can be quickly reconfigured for maximum flexibility and impact by LLM-driven systems and reasoning. Consider the music industrys digital transformation. When the world shifted from CDs to digital downloads, record labels no longer had to sell entire albums to move a single hit. Tracks could be released individually, targeting specific audiences, and responding instantly to demand. Agentic AI lets work evolve the same way. Instead of forcing employees through rigid, linear processes, AI agents can identify whats needed in each moment, suggest next steps, and help people move forward while still ensuring all compliance requirements and approvals. Every step becomes a point of value creation, not just a box to check. Now, the energy once lost to bureaucracy gets redirected toward more meaningful progress that drives improved business outcomes. EXPOSE HIDDEN INEFFICIENCIES One of agentic AIs most powerful capabilities is surfacing inefficiencies that go unnoticed under legacy systems. When workflows become more visibleand dramatically fasterflaws built up over years are suddenly impossible to ignore. At one large industrial company, a frontline employee needed to order a $50 backpack from a trusted supplier. On paper, the process looked fine, a simple purchase order flowing through the required workflows. But when an agentic AI was implemented, the reality was clear: The request required seven separate approvals, each one adding delay. The AI didnt just move through the workflows faster; it turned the entire process into a conversation, exposing how much unnecessary friction had crept in over time. That visibility sparked an important discussion: Did they really need so many layers of sign-off for such a routine expense? The technology made the inefficiency undeniable, but it took a cultural and compliance shift within the company to actually eliminate the redundancy. By combining automation with organizational will, the company not only streamlined purchases but also gained insight into how work actually gets done, building momentum for broader changes regarding outdated processes. FROM RIGID TO RESPONSIVE Traditional enterprise software enforces strict compliance: every field filled, every form completed, every step followed in order. But work in the real world is rarely so tidy. Employees operate with partial information and constantly adapt to shifting priorities. Agentic AI changes the equation. It adapts to how people work, not the other way around. AI agents capture whats available, ask follow-up questions later, and complete tasks dynamically as information emerges. The most advanced agentic deployments go further. When a major movie studios engineering team noticed unexplained server spikes, their ambient AI scanned logs, release schedules, and forums, revealing leaked content driving traffic from torrent sites. It flagged the issue and suggested scaling options, while the agentic AI spun up extra servers and alerted the security team, immediately turning insight into action. These breakthroughs only matter if people actually use the tools. Thats where most enterprises stumble. THE REALITY OF RESISTANCE Many organizations are already overwhelmed by digital complexity. Employees face fragmented workflows, siloed teams, and outdated systems. Agentic AI wont erase this complexity overnight, and adoption will be uneven. Thats why successful implementations dont force new processes or best practices. Instead, agentic systems leverage how work already happens, and makes it easier. Agentic AI wont debate how you onboard vendors or process reimbursements, they just help get it done faster and with less friction. This builds trust. When employees see AI agents handling tasks they already do like finding files, filling out forms, or submitting requests theyre more likely to engage. And the best agentic systems dont just wait for instructions; they reach out proactively, helping people stay one step ahead. The more useful and interactive the assistant, the faster adoption spreads. CAREER IMPLICATIONS Working with AI is fast becoming a core career skill. Employees who learn to collaborate with AI agents by asking smart questions, interpreting insights, and applying them to real-world challenges will be better prepared as roles evolve. This isnt about replacing humans, but amplifying their capabilities. Those who master conversational AI, navigate multiple systems, and use AI to manage complexity will accomplish what once took entire teams. That fluency will set top performers apart. WHAT THIS MEANS FOR TOMORROW PwCs Value in motion report predicts AI adoption could boost global GDP by up to 15% by 2035, an impact on par with the industrial revolution. But realizing that future requires responsible deployment, clear governance, and a culture of trust. The workplace of the future will be built on collaboration between people and AI. Companies that get this right will break down silos, eliminate waste, and empower employees to focus on what matters most. The technology is ready; the real challenge is building cultures that value transparency over complexity and see AI agents as essential partners, not threats. Agentic AI wins when people use it because it makes their jobs easier, shines a light on hidden inefficiencies, and unlocks new ways of working that were once out of reach. Bhavin Shah is founder and CEO of Movework.
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E-Commerce
I grew up in the Netherlands, so I know the upsides of living in Europe. I also know how hard it is to build a company here. The rules change across borders, funding is limited, and things move slower than they should. When we started Remote, we knew we had to think globally but also anchor in the U.S. Its the biggest tech market, and succeeding there gives you the best chance to scale everywhere else. That choice wasnt unique to us. More and more European founders are making the same call.Whats changed is the timing of the move. Expanding to the U.S. used to happen once companies were well-established in Europe. Now theyre showing up earlier and moving faster. Index Ventures found that 64% of startups expand to the U.S. at preseed or seed stage now, an increase from the 2015-2019 rate of 33%. WHY IT MATTERS This shift matters for American businesses. European startups are arriving with funding and moving in as both competitors and potential partners. That changes how U.S. companies compete for capital, customers, and talent. Spotify did this early. They started in Sweden in 2006 and quickly expanded into the U.S. They opened offices, built partnerships, and kept much of their engineering base in Europe. U.S. investment anchored them in the American market. It gave them credibility with local customers, visibility with partners, and the resources to scale fast. By the time they raised their $1 billion Series F, led by a U.S. VC, they were ready to take on Apple. Today, they lead the streaming market. So why is this happening now? On paper, Europe is a huge market. In reality, its fragmented. Tax, labor, and compliance rules differ from one country to the next. Expanding from France into Germany can be as complex as expanding from Europe into Asia. Late-stage capital is harder to find, which slows growth, and enterprise customers are slower to move on smaller deals. Thats why European startups are looking to the U.S. earlier. American buyers move faster, spend more, and make decisions quickly. The U.S. is still the market that signals credibility, and winning there carries weight abroad. Enterprise buyers in other regions often want proof a product works there before they commit. These moves benefit more than just the startups. They raise the bar for everyone by pushing U.S. companies to get leaner, scale faster, and think globally. 4 TAKEAWAYS So what should U.S. founders take away from all this? 1. Dont slow down European founders are showing up with clear goals and aggressive timelines. If youre in a crowded market, theyll be chasing the same deals, talent, and capital. Use that pressure to improve your product and move faster. 2. Build with discipline European founders often scale with fewer resources and smaller teams. They build distributed companies early, with strong culture and tight alignment. Instead of debating office models, they figure out how to work across borders and time zones. That discipline can give U.S. companies an edge on speed and cost. 3. Think global from day one European startups dont have a big home market. They build for multiple markets early, which means products that work across languages, currencies, and regulations. U.S. companies that do the same are better positioned to scale fast and win abroad. 4. Work with them, not against them Working with these companies can give you access to new markets, talent, and expertise. Investors who back them get exposure to broader networks and operating models. Treat partnership as a growth strategy. My advice to American founders: Dont ignore this wave. The best European startups are already here. Competing with them or working alongside them will make your company better. Dont see it as a threat. Learn from it. Job van der Voort is CEO and cofounder of Remote.
Category:
E-Commerce
I recently had an unsettling rideshare experience. Let me paint a visual picture. You have a Tesla doing its self-driving thing, a guy just sitting in the drivers seat supervising, and a terrified human (me) in the backseat looking on in horror. Finally, I said, Please keep your hands on the wheel when youre driving me, OK? Teslas autonomous functionality might be safe, but I dont have enough trust yet to allow a Tesla to get me from Point A to Point B without a human steering it. Theres a parallel between self-driving cars and the current perceptions of AI and agents. You might be comfortable letting one of these automobiles make a simple right-hand turn, but turning left to cross through busy traffic? Probably not so much. Were in the early days of agentic transformation, which describes the shift from traditional software to a more autonomous enterprise that relies on software that can act independently. Businesses are eager to embed agents in processes to make operations more efficient. Yet were in a remarkably similar place to autonomous vehicles with our level of trust, or lack thereof. Implementing simple agentic pilot use cases are one thing. But yielding control of critical workflows is another. WHAT RESEARCH TELLS US This trust gap isnt just a hunch on my part. Its backed by respected research. In a recent KPMG International study of more than 48,000 people in 47 countries, 66% said that while they use artificial intelligence, 54% were unwilling to trust it. A McKinsey & Company report cited something similar, calling it the GenAI paradox. It found that almost eight in 10 companies use generative AI, but the same number has not seen any significant bottom-line impact. This is why the biggest AI challenge isnt technical, the report stated. Instead, It will be human: earning trust, driving adoption, and establishing the right governance to manage agent autonomy and prevent uncontrolled sprawl. AI adoption is happening, but its not happening with great confidence. Every business leader should also think about a Pew Research Center study that found a vast chasm between the views of AI experts and the general public. The public is far less optimistic and enthusiastic about the technology. Bridging this skepticism divide will be the difference between success and failure for businesses as they agentify their operations. So, we still dont fully trust AI to make meaningful decisions for us. Weve all heard the stories of AI hallucinations and businesses rolling out initiatives that, in hindsight, werent ready for prime time. Caution is not the same thing as moving slowly. That said, the companies that figure out how to adeptly use agents in their businesses sooner will be the ones that move faster, execute smarter, and operate leaner. Building trust is the key that unlocks it all. WHY TRUST IS THE HARD PART Of course, the trust in technology issue predates AIs arrival. Ive worked in software for three decades. For a significant portion of that time, the focus has been on digital transformation to make businesses more efficient through digitizing processes. One of the biggest obstacles companies have long faced is a deep distrust of their data. Incomplete, inaccessible, or inaccurate data can fundamentally paralyze organizations. Businesses dont know what to trust. When facing critical decisions that can impact their companies trajectory, some of the most intense leadership team discussions are whether they believe what the data tells them. Ive been part of those conversations. Now, as we shift into agentic transformation, if you dont get the data right, AI can make the problem 10 times worse. Thats because AI models and agents use the data available as fuel to make decisions and generate outputs based on probabilities and likelihoods in a black box environment that can lack transparency. Because AI responses and actions are based on those probabilities, it will never be 100% accurate. (Much like humans, by the way.) But AI can be made as trustworthy as possible. Its all predicated on: Accurate data. Access to information. Without setting a strong foundation for managing data and fully connecting systems so that information moves where needed, the conviction required to support your AI initiatives will be lacking. Youll understandably have doubts about the actions that agents are taking within your organization and externally on behalf of your business. WHAT EVERY LEADER SHOULD CONSIDER Change only happens at the speed of trust. If we believe in something, well use it. Building confidence in AI models and agents requires control and governance. It starts with the foundation I mentioned: well-managed data and well-integrated systems. Solving the age-old garbage in, garbage out problem of poor data is a crucial first step. It will give AI what it needs to make more accurate and responsible decisions. Then there are the agents themselves. Weve reached a point where every organization can build agents, and every vendor is making them part of their products. But something else is more essentialmanaging them. You need to know about every agent in your operations. Youll need visibility into what theyre doing and how theyre performing their assigned tasks. If theyre not acting as expected, you must be able to fix the issue quickly. These guardrails are the backbone upon which trust is nurtured as this new agentic world evolves and matures. For leaders, this requires striking the right balance between championing speed and responsible innovation in ways that enable AI to enhance efficiency while amplifying human capability. Thats because human interactionshelping customers, managing employees, and so onwill always be those more challenging left-hand turns where we want people to make decisions requiring empathy and judgment. Just as were moving toward a self-driving car that inspires unequivocal trust, were also on a path to metaphorically creating the self-driving enterprise with agents. For now, though, most of us arent yet willing to take our hands off our businesss steering wheel. Agents have to earn that kind of trust through governance. Steve Lucas is chairman and CEO of Boomi.
Category:
E-Commerce
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