Informed i’s Weekly Business Insights

Watching the automatic hand of the Manus AI agent scroll through a dozen browser windows is unsettling. Give it a task that can be accomplished online, such as building up a promotional network of social-media accounts, researching and writing a strategy document, or booking tickets and hotels for a conference, and Manus will write a detailed plan, spin up a version of itself to browse the web, and give it its best shot.

Manus AI is a system built on top of existing models that can interact with the internet and perform a sequence of tasks without deferring to a human user for permission. Its makers, who are based in China, claim to have built the world’s first general AI agent that “turns your thoughts into actions”. Yet ai labs around the world have already been experimenting with this “agentic” approach in private. What makes Manus notable is not that it exists, but that it has been fully unleashed by its creators. A new age of experimentation is here, and it is happening not within labs, but out in the real world.

Spend more time using Manus and it becomes clear that it still has a lot further to go to become consistently useful. Confusing answers, frustrating delays and never-ending loops make the experience disappointing. In releasing it, its makers have obviously prized a job done first over a job done well.

This is in contrast to the approach of the big American labs. Partly because of concerns about the safety of their innovations, they have kept them under wraps, poking and prodding them until they hit a decent version 1.0.

Big labs have been cautious about agentic AI, too, and for good reason. Granting an agent the freedom to come up with its own ways of solving a problem, rather than relying on prompts from a human at every step, may also increase its potential to do harm.

Fortunately, there is little sign yet that Manus has done anything dangerous. But safety can no longer be just a matter of big labs conducting large-scale testing before release. Instead, regulators and companies will need to monitor what is already used in the wild, rapidly respond to any harms they spot and, if necessary, pull misbehaving systems out of action entirely. Whether you like it or not, Manus shows that the future of ai development will play out in the open.

Organizations are starting to make organizational changes designed to generate future value from gen AI, and large companies are leading the way. The latest McKinsey Global Survey on AI finds that organizations are beginning to take steps that drive bottom-line impact.  Some of its major findings are:

Survey analyses show that a CEO’s oversight of AI governance—that is, the policies, processes, and technology necessary to develop and deploy AI systems responsibly—is one element most correlated with higher self-reported bottom-line impact from an organization’s gen AI use.   That’s particularly true at larger companies, where CEO oversight is the element with the most impact on EBIT attributable to gen AI.

The value of AI comes from rewiring how companies run, and the latest survey shows that, out of 25 attributes tested for organizations of all sizes, the redesign of workflows has the biggest effect on an organization’s ability to see EBIT impact from its use of gen AI. Organizations are beginning to reshape their workflows as they deploy gen AI.

Some essential elements for deploying AI tend to be fully or partially centralized. For risk and compliance, as well as data governance, organizations often use a fully centralized model such as a center of excellence. For tech talent and adoption of AI solutions, on the other hand, respondents most often report using a hybrid or partially centralized model, with some resources handled centrally and others distributed across functions or business units.

Organizations have employees overseeing the quality of gen AI outputs, though the extent of that oversight varies widely.

Respondents are more likely than in early 2024 to say their organizations are actively managing risks related to inaccuracy, cybersecurity, and intellectual property infringement—three of the gen-AI-related risks that respondents most commonly say have caused negative consequences for their organizations.

Most respondents have yet to see organization-wide, bottom-line impact from gen AI use—and most aren’t yet implementing the adoption and scaling practices that we know from earlier research help create value when deploying new technologies.

Respondents at larger companies are more likely than their peers at smaller organizations to report hiring a broad range of AI-related roles, with the largest gaps seen in hiring AI data scientists, machine learning engineers, and data engineers.  Many respondents also say that their organizations have reskilled portions of their workforces as part of their AI deployment over the past year and that they expect to undertake more reskilling in the years ahead.

Respondents most often report that employees are spending the time saved via automation on entirely new activities. They also often say that employees are spending more time on existing responsibilities that have not been automated. Respondents at larger organizations, however, are more likely than others to say their organizations have reduced the number of employees as a result of time saved.  Overall, though, a plurality of respondents (38 percent) whose organizations use AI predict that use of gen AI will have little effect on the size of their organization’s workforce in the next three years.

Reported use of AI increased in 2024.3 In the latest survey, 78 percent of respondents say their organizations use AI in at least one business function, up from 72 percent in early 2024 and 55 percent a year earlier (Exhibit 8). Respondents most often report using the technology in the IT and marketing and sales functions, followed by service operations.

While organizations in all sectors are most likely to use gen AI in marketing and sales, deployment within other functions varies greatly according to industry. Organizations are applying the technology where it can generate the most value—for example, service operations for media and telecommunication companies, software engineering for technology companies, and knowledge management for professional-services organizations.

Overall, respondents are also more likely than in the previous survey to say they are seeing meaningful cost reductions within the business units using gen AI.  Yet gen AI’s reported effects on bottom-line impact are not yet material at the enterprise-wide level. More than 80 percent of respondents say their organizations aren’t seeing a tangible impact on enterprise-level EBIT from their use of gen AI.

According to the author, he has been mulling over something that Will Heaven, MIT senior editor for AI, pointed out not too long ago: that all the big players in AI seem to be moving in the same directions and converging on the same things. Agents. Deep research. Lightweight versions of models. Etc. Some of this makes sense in that they’re seeing similar things and trying to solve similar problems. But it almost feels like a lack of imagination.

According to the author what got him thinking about this, again, was a pair of announcements from Google over the past couple of weeks, both related to the ways search is converging with AI language models, something he has spent a lot of time reporting on over the past year. Google took direct aim at this intersection by adding new AI features from Gemini to search, and also by adding search features to Gemini. In using both, what struck me more than how well they work is that they are really just about catching up with OpenAI’s ChatGPT.  And their belated appearance in March of the year 2025 doesn’t seem like a great sign for Google.

Take AI Mode, which it announced March. It’s cool. It works well. Much of what these new features are doing, especially AI Mode’s ability to ask followup questions and go deep, feels like hitting feature parity with what ChatGPT has been doing for months. It’s also been compared to Perplexity, another generative AI search engine startup.

ChatGPT, as the company was preparing to roll out search, it has more freedom to innovate precisely because it doesn’t have the massive legacy business that Google does. Yes, it’s burning money while Google mints it. But OpenAI has the luxury of being able to experiment (at least until the capital runs out) without worrying about killing a cash cow like Google has with traditional search.

Of course, it’s clear that Google and its parent company Alphabet can innovate in many areas—see Google DeepMind’s Gemini Robotics announcement this week, for example. Or ride in a Waymo! But can it do so around its core products and business? It’s not the only big legacy tech company with this problem. Microsoft’s AI strategy to date has largely been reliant on its partnership with OpenAI. And Apple, meanwhile, seems completely lost in the wilderness.

Google has billions of users and piles of cash. It can leverage its existing base in ways OpenAI or Anthropic (which Google also owns a good chunk of) or Perplexity just aren’t capable of. But according to the author he is also pretty convinced that unless it can be the market leader here, rather than a follower, it points to some painful days ahead. But hey, Astra is coming. Let’s see what happens.

AI is potentially disruptive to many organizations’ business models. In few sectors, however, is the threat as seemingly existential as the news business. News ought to matter to all of us since a functioning free press performs an essential role in democracy—informing the public and helping to hold power to account. And, there are some similarities between how news executives are—and critically, are not—addressing the challenges and opportunities AI presents that business leaders in other sectors can learn from, too.

The following reflection by the author is based on his participation at Aspen Institute conference entitled “AI & News: Charting the Course,” that was hosted at Reuters’ headquarters in London. The conference was attended by top executives from a number of U.K. and European news organizations.

Most of the reflections still evolve and remain so around modest impact of AI—mostly around making existing workflows more efficient.  There was active debate among the newsroom leaders and techies present about whether news organizations should take a bottom-up approach—putting generative AI tools in the hands of every journalist and editor, allowing these folks to run their own data analysis or “vibe code” AI-powered widgets to help them in their jobs, or whether efforts should be top-down, with the management prioritizing projects.  Many called for a balanced approach, though there was no consensus on how to achieve it.

News outfits are also being cautious about building audience-facing AI tools. Many have begun using AI to produce bullet-point summaries of articles that can help busy and increasingly impatient readers. Some have built AI chatbots that can answer questions about a particular, narrow subset of their coverage—like stories about the Olympics or climate change—but they have tended to label these as “experiments” in order to help flag to readers that the answers may not always be accurate. Few have gone further in terms of AI-generated content. They worry that gen AI-produced hallucinations will undercut trust in the accuracy of their journalism. Their brands and their businesses ultimately depend on that trust.

This caution, while understandable, is itself a colossal risk. If news organizations themselves aren’t using AI to summarize the news and make it more interactive, technology companies are. People are increasingly turning to AI search engines and chatbots, including Perplexity, OpenAI’s ChatGPT, and Google’s Gemini and the “AI Overviews” Google now provides in response to many searches, and many others. Several news executives at the conference said “disintermediation”—the loss of a direct connection with their audience—was their biggest fear.  Cloudflare, which is also offering to help protect news publishers from web scraping, found that OpenAI scraped a news site 250 times for every one referral page view it sent that site.

So far, news organizations have responded to this potentially existential threat through a mix of legal pushback—the New York Times has sued OpenAI for copyright violations, while Dow Jones and the New York Post have sued Perplexity—and partnerships. Those partnerships have involved multiyear, seven-figure licensing deals for news content. (Fortune has a partnership with both Perplexity and ProRata.) Many of the execs at the conference said the licensing deals were a way to make revenue from content the tech companies had most likely already “stolen” anyway. They also saw the partnerships as a way to build relationships with the tech companies and tap their expertise to help them build AI products or train their staffs. None saw the relationships as particularly stable. They were all aware of the risk of becoming overly reliant on AI licensing revenue, having been burned previously when the media industry let Facebook become a major driver of traffic and ad revenue. Later, that money vanished practically overnight when Meta CEO Mark Zuckerberg decided, after the 2016 U.S. presidential election, to de-emphasize news in people’s feeds.

Executives acknowledged needing to build direct audience relationships that can’t be disintermediated by AI companies, but few had clear strategies for doing so. One expert at the conference said bluntly that “the news industry is not taking AI seriously,” focusing on “incremental adaptation rather than structural transformation.” He likened current approaches to a three-step process that had “an AI-powered Ferrari” at both ends, but “a horse and cart in the middle.”

He and another media industry advisor urged news organizations to get away from structuring their approach to news around “articles.” Instead, they encouraged the news execs to think about ways in which source material (public data, interview transcripts, documents obtained from sources, raw video footage, audio recordings, and archival news stories) could be turned into a variety of outputs—podcasts, short-form video, bullet-point summaries, or yes, a traditional news article—to suit audience tastes on the fly by generative AI technology. They also urged news organizations to stop thinking of the production of news as a linear process, and begin thinking about it more as a circular loop, perhaps one in which there was no human in the middle.

One person at the conference said that news organizations needed to become less insular and look more closely at insights and lessons from other industries and how they were adapting to AI. Others said that it might require startups—perhaps incubated by the news organizations themselves—to pioneer new business models for the AI age.

The stakes couldn’t be higher. While AI poses existential challenges to traditional journalism, it also offers unprecedented opportunities to expand reach and potentially reconnect with audiences who have “turned off news”—if leaders are bold enough to reimagine what news can be in the AI era.

The key to building connections, according to Claude Grunitzky who is a world-class networker, is to first arrive at a thorough understanding of your own identity—the interrelated elements that you use to define yourself. In Grunitzky’s case those elements include family roles such as father and husband, career roles such as CEO and journalist, an interest in jazz, and his Catholic faith. Only once you’ve identified the many facets of your identity, you can “identify commonality” with people from a wide variety of backgrounds. Commonalities provide a solid foundation on which to build and expand your network.  

Doing that well can help you thrive both at work and in life. The good news is that you have more control over your identity than you may realize: You can curate it in ways that will improve your performance as a leader, the trust you’re able to inspire in others, and even your overall well-being.

Today we are constantly asked in the workplace to answer the question “Who are you?” There’s no avoiding it—especially if you’re in a leadership or a management role. Leaders project authenticity, and become trusted, by communicating their identities.  Leaders also call on their identities when they need confidence and guidance.

If we’re socially connected to many others who have a particular identity element—if they consider themselves creative, say, or an athlete, or somebody who likes to cook—we’re more likely to adopt that element ourselves. Likewise, how we think of ourselves is to an extent a function of how others see us. In the end, our personal identities are partly the result of negotiations with others.

In her now-classic book Working Identity, Herminia Ibarra makes the case that successful career transitions depend on aligning your personal identity with the role you are transitioning to. She offers practical advice for achieving that alignment, such as experimenting with projects and activities—including outside work—to discover identity elements that will help you succeed in a new role.

Your identity will be most useful to you, of course, if you understand it as something you’ve defined for yourself rather than something that others have defined for you.  Research has also shown that our identities serve us better when we understand all their elements (no matter how diverse or even seemingly self-contradictory) as being harmonious.

To understand yourself, create your identity map – Identify potential elements of your identity, put those elements on the map, connect the elements, and Indicate elements that you keep concealed.

In analyzing the identity maps that the authors executives created, made some interesting findings. For example, that those who had more elements than others in the group on their maps—a feature I call multiplicity—had more contacts in their professional networks. Those with, say, 26 elements on their maps had professional networks that were 80% larger than the networks of executives who had only 13 – —a pattern consistent with Claude Grunitzky’s sense that being able to affirm a multifaceted personal identity correlates with being able to build an extensive professional network. Given the considerable evidence that connects professional network ties to outcomes such as salary, bonuses, and promotions, being able to identify many elements of your identity might mean the difference between a thriving career and a floundering one.  The executives who reported concealing more elements of their identities than others did had smaller professional networks, rated their own life satisfaction lower, and were rated less effective at work in 360-degree evaluations.

Another interesting finding we made when studying our executives was that the networks of those who put more identity elements on their maps were more diverse than those of the executives with fewer elements. That’s a huge win, because diverse networks are associated with access to diverse ideas—which in turn lead to better decision-making and more creativity.

Ultimately our identities, purposefully curated and artfully deployed, are the substance behind the elusive quality of authenticity.

Organizational learning and adaptation are vital to business survival and success in the face of disruptive technological advancements, increasing environmental challenges, and rapidly shifting customer demands. Yet many business leaders overlook their critical role in facilitating learning, especially when new initiatives demand it at scale.

During organizational transformations, leaders often delegate the learning component of change management to learning and development specialists inside and outside the company. While this approach can bring teams up to speed with new ways of working in the short term, it occurs outside the context of the work itself in a classroom or workshop setting and doesn’t build a capacity for ongoing organizational learning from the work, including the ability to analyze and solve problems from which learning emerges.

To understand how leaders can become effective learning facilitators, the authors conducted two longitudinal studies at Lego, a leading toy company, and Velux, a global leader in manufacturing skylights and roof windows.  Their research highlights a key insight: To become effective learning facilitators, leaders must embrace the counterintuitive approach of going slow to go fast. This principle underscores the impact of deliberate, thoughtful leadership in driving lasting change by focusing on building employees’ learning and problem-solving skills. At both companies, leaders embraced the role of learning facilitators and actively prioritized the development of employees’ systematic problem-solving abilities. By framing each problem as an opportunity for growth, leaders encouraged employees to approach problems with a focus on strengthening long-term skills rather than just resolving the issue at hand.

At both Lego and Velux, leaders first needed to recognize that they could achieve meaningful transformation through learning processes rooted in active engagement with real-world problems alongside their employees.   This realization required leaders to slow down and create the space to facilitate their teams’ learning processes — a counterintuitive yet crucial approach to solving problems effectively.

The four-stage problem-solving journey begins with finding the problem and actively seeking out areas for improvement. This requires a cultivated sense of curiosity and heightened awareness to identify issues and opportunities. Keen observation and attentive listening to customer and stakeholder feedback are essential at this stage to ensure no critical insights are overlooked.

The next step is facing the problem — that is, confronting challenges directly and embracing their complexities with courage and curiosity. By tackling issues head-on, teams gain a deeper understanding of their root causes, building a foundation for effective, lasting solutions.

The third phase, framing, involves defining and clarifying the problem in a structured way. This stage calls for breaking down the issue, analyzing its components, and identifying root causes. Framing requires thorough research, data collection, and stakeholder engagement to uncover diverse perspectives that enrich the analysis.

Finally, the focus shifts to forming solutions. Based on insights from the previous phases, teams generate and implement solutions that are both creative and evidence-based. This step relies on critical thinking, brainstorming, hypothesis testing, and iterative refinement. By actively engaging in this problem-solving cycle, teams can learn from successes and setbacks, and embed continuous improvement into the organizational culture, all the while demonstrating the value of going slow to go fast in solving complex problems.

Developing employees into adept problem solvers through A3 thinking requires leaders to shift from imparting knowledge to fostering inquiry. Leaders who act as learning facilitators encourage employees to set goals and derive conclusions by asking insightful questions rather than dictating answers.  This approach promotes autonomy, involvement, and engagement by letting employees tackle challenges independently; they’re supported by enabling processes rather than directive control.

By embracing inquiry-driven group coaching, Lego and Velux are nurturing leaders who are skilled in facilitating learning and fostering a culture of reflective problem-solving. This approach enables organizations to harness the collective intelligence of their teams, navigate complexity with resilience, and drive sustainable growth.

AI is showing up in nearly every workplace, affecting the way teams communicate, make decisions, and serve customers. For employees, learning AI skills makes them more valuable to their companies and more competitive in the job market. Companies are already using AI to streamline workflows, and employees who know how to use it will be ahead of those who do not. For employers, AI training is about more than just efficiency. It ensures that teams understand how to work alongside AI instead of resisting it. A workforce that embraces AI will be more innovative and better prepared for change.

ChatGPT and other AI-powered tools are already transforming how people write emails, analyze data, and automate repetitive tasks. But understanding AI goes beyond just knowing how to use a chatbot.  AI affects:

Decision-Making – AI helps professionals analyze patterns, predict trends, and make smarter business choices.

Customer Service – AI-powered chatbots and virtual assistants are becoming more common.

Data Analysis – AI can sift through large amounts of information quickly, helping teams make sense of data.

Marketing And Sales – AI improves personalization, from product recommendations to ad targeting.

Knowing what AI can and can’t do is just as important as knowing how to use a single tool like ChatGPT.

The biggest mistake people make when learning AI is assuming they need to be experts before they even start. That’s not true. The best way to learn AI is by being curious—asking questions, experimenting, and seeing how it can be useful in your job.

Some good questions to start with:  Can AI make any part of my work easier or faster?  How are companies in my industry using AI successfully?  What AI tools are already available that I haven’t tried yet?  By approaching AI with a curious mindset, you remove the fear factor and turn it into something exciting—something that can actually make your job easier instead of replacing you.

What Are The Key AI Skills For Non-Tech Professionals?  You don’t need to code, but you do need a basic understanding of how AI works and how to apply it in a practical way. The most important AI-related skills include:

Understanding AI Basics – You should know the difference between automation and true AI, how AI learns from data, and the role of human oversight. Automation does the same task over and over, while AI learns from experience, but people still need to check its work to make sure it gets things right.

Prompt Engineering – Learning how to ask AI the right questions (or prompts) will help you get better results from tools like ChatGPT.

Data Literacy – AI runs on data. Being able to read, interpret, and make decisions based on AI-generated insights is crucial. This is important for non-IT professionals because AI is being used in hiring, marketing, sales, and many other areas, and knowing how to interpret its results helps avoid mistakes and make smarter choices.

Critical Thinking – AI is powerful, but it’s not perfect. Knowing when to question AI-generated answers and spot potential biases is a key skill. Bias is a problem because AI learns from past data, and if that data has mistakes or unfair patterns, AI can repeat them, leading to wrong or unfair decisions.

Collaboration With AI – The best employees won’t see AI as a replacement but as a collaborative tool that can enhance their productivity. It can help them finish tasks faster, avoid boring, repetitive work, and free up time for more creative or important projects.

The best way to prepare for the AI-powered future is to stay curious and build AI skills. AI can help to make you better at what you do. The professionals who ask questions, experiment, and embrace AI will be the ones who succeed in the long run.

You probably know what you want to do. (It’s easy to think of things you want to do.).  How you’ll get there is the problem, especially if the goal doesn’t seem directly — much less easily — controllable. Like “building a reputation for excellence.” Or “becoming the employer of choice.”  It’s a common problem. 

According to Jeff Bezos (said in 2014), you can flip it around (long, but worth it):  If you focus on the controllable inputs to your business, in the long term, you get better results.  Say somebody came up to me and said, “Jeff, I want your job to be to drive up the Amazon stock price, and just manage that directly.” Many companies actually try to do this. They go out and try to “sell” the stock. That’s a silly approach, that’s not sustainable.  It’s much better to say, “What are the inputs to a higher stock price?” OK, well, free cash flow and return on invested capital are inputs to a higher stock price. Let’s keep working backwards. What are the inputs to free cash flow? And you keep working backwards until you get to something that’s controllable. A controllable input for free cash flow would be something like lower cost structure.  Then you back up from there and say, if we can improve our picking efficiency in our fulfillment centers and reduce defects — reducing defects at the root is one of the best ways to lower cost structure — that starts to be a job you would accept.  If you’re a reasonable person, you would say, “I have no idea how to drive up the stock price. I can’t manage that directly. It’s not a controllable input.” But I can make picking algorithms more efficient, and that will reduce cost structure.  And then you follow that chain all along the way.

The same holds true for your business. Want to increase sales?  Focus on controllable inputs: the number of leads generated, the number of cold calls made, the type and frequency of follow-ups. Turn the goal into a series of controllable, manageable inputs. (“Manageable” not in terms of easily attainable, but a task or process that can be actively and objectively managed.)

Want to get new employees up to speed faster and better? Focus on controllable inputs. The same is true for personal pursuits. 

Manage controllable inputs, and the output almost always takes care of itself, especially over the long term. Success typically results not from a relentless focus on an end goal, but on the daily process required to achieve that goal. 

Set goals that allow you to work backwards to find the controllable inputs that allow you to achieve them. Follow that chain all along the way until you find concrete steps you can take, and then focus on improving those controllable inputs.  Do that, and the output will almost always take care of itself.

As the founder of multiple startups, according to the author he has learned that strategic expansion isn’t necessarily about overhauling your business or pulling yet another rabbit out of a hat. It’s about uncovering every possible way that the products and services you’ve already built can deliver more value to your partners or existing customers.

Building an initial customer base is labor- and cash-intensive. Why invest more energy in this pursuit when your existing relationships have yet to be optimized? Deepening existing relationships should be paramount.

But how do you determine what additional offerings your customers truly need? How do you know which products and services will allow you to deepen those relationships and drive growth? Here are three steps to reveal your next new offering.

Start by (actually) listening.  The development of any new product or service begins with careful listening and genuine curiosity. It is about market research that springs from customer obsession — not just scouring Google Trends for ideas.  According to the author when his business development team heads out to see partners, he always say: “Don’t ask what they need; ask what their problem is.” It’s a crucial distinction. The client is an expert in their own situation. But we should be the experts about the services and products that can solve their problems.

Take stock of what you have.  Discovering the problem is only the first step. Next, you have to evaluate whether you should offer a solution. But remember, this isn’t about doing a 180 or creating a whole new vertical. Instead, ask yourself: Is this additive to our existing business, or just a shiny distraction?  It’s important to model how long it will take to realize your ROI. Building an app, for example, may feel like an organic extension of your core business, but time and resources are lost in rounds of complicated testing before any revenue can be generated. So, even if it fits in every other way, a capital-intensive offering with delayed revenue might not be worth it.  That said, if the bandwidth and expertise are already in place, and the new product or service is an efficient use of time and resources, then you’ve potentially just struck gold. The key is looking for those baked-in connections: the light lift and the smart reimagining of existing products and services so that they can easily serve additional use.

Decide whether to greenlight.  You’ve heard what’s really needed, and you’ve taken stock to confirm that you have the skills/equipment/team that can fulfill that need. Then, it boils down to deciding whether this is actually a great opportunity or a distraction.  After a basic cost analysis (will it generate revenue?), a more complicated question is to ask: What is the risk to your current customer relationships when you expand your list of offerings? If your new product or service isn’t executed well, the trust you’ve built could be damaged.  Brand dilution is a further risk. Any brand can grow weaker when new products or services don’t bolster and complement its original offering.

When done right, the delivery of new products and services should feel like a win-win. The client or partner has their problem solved. And you’ve unlocked a brand-new revenue stream. Most importantly, your existing relationships are deepened, and the ties that bind you together grow stronger.