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CEO expectations for AI-driven growth remain high in 2026—at the same time their workforces are grappling with the more sober reality of current AI performance. Gartner research finds that only one in 50 AI investments deliver transformational value, and only one in five delivers any measurable return on investment.

In 2026, executive teams will have to navigate this difficult tension: delivering on today’s growth targets while nurturing a workforce capable of driving future value amid AI transformation. To do that, they must prioritize:  navigate new realities of the AI era, mitigate emerging threats to organizational performance, and seize the opportunities for a blended human-machine workforce.

To help leaders prepare for the unexpected—or underappreciated—risks that organizations may encounter in 2026, Gartner offers the following nine predictions.  

1. AI layoffs outpace AI productivity gains.  In 2025, we witnessed large, high-profile layoffs attributed—directly or indirectly—to AI. But in reality, people aren’t actually losing their jobs to better-performing AI, at least not yet.  In 2026, these organizations must navigate the painful choices that come with reducing headcount, which will be even more difficult without proven AI-driven productivity gains. If the pace of AI productivity lags behind the work organizations need to get done now, they will have to rehire talent they prematurely laid off, often at greater cost, to meet their targets.
2. Culture dissonance holds organizations back from performance goals.  While it’s true that CEOs are seeking a culture that “performs,” in an AI-driven world, these large shifts are the exception, not the rule. Instead, most organizations are more subtly but consistently expecting more from employees without offering more in return. And employees are noticing.  This top-down performance pressure is leading to profound cultural dissonance in which many organizations’ stated culture no longer reflects the realities of employees’ everyday experience.  This cultural dissonance has real consequences.  In 2026, the most successful organizations will be forthright, both internally and externally, about the reality of their culture and what they expect from employees (e.g. hours, output, and location). Culture can, and should, evolve to meet the realities of the current work environment, but employees must know what they’re signing up for.
3. AI takes a toll on employees’ mental fitness.  Organizations are dedicating enormous effort into determining how work will change because of AI, but they’re devoting far less energy into understanding how people themselves will change. Gen AI adoption in organizations has reached near ubiquity. At the same time, the evidence is mounting of emotional and cognitive damage that can result from prolonged gen AI use, from cognitive atrophy to AI psychosis.  In 2026, AI’s impact on employees’ mental fitness—their emotional, psychological, and cognitive well-being will become an urgent problem in the workplace. This is something organizations are currently largely ignoring.  Companies will also be vulnerable to legal actions that are not currently included in most risk assessments.  As AI becomes even more deeply embedded in daily workflows, the most successful organizations will work to mitigate the psychological and cognitive risks of sustained AI use before these costs undermine both employee well-being and organizational performance.

The other trends are:  AI workslop becomes a top productivity drain.  Forward-thinking employers restore humanity to the hiring process.  Insider corporate espionage risks increase.  Tech-to-trades career paths blossom.  Process pros—not tech prodigies—unlock AI value.  And employees demand compensation for training their digital doppelgangers.

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Anton Cherepanov is always on the lookout for something interesting. And in late August last year, he spotted just that. It was a file uploaded to VirusTotal, a site cybersecurity researchers like him use to analyze submissions for potential viruses and other types of malicious software, often known as malware. On the surface it seemed innocuous, but it triggered Cherepanov’s custom malware-detecting measures. Over the next few hours, he and his colleague Peter Strýček inspected the sample and realized they’d never come across anything like it before.

The file contained ransomware, a nasty strain of malware that encrypts the files it comes across on a victim’s system, rendering them unusable until a ransom is paid to the attackers behind it. But what set this example apart was that it employed large language models (LLMs). Not just incidentally, but across every stage of an attack. Once it was installed, it could tap into an LLM to generate customized code in real time, rapidly map a computer to identify sensitive data to copy or encrypt, and write personalized ransom notes based on the files’ content. The software could do this autonomously, without any human intervention. And every time it ran, it would act differently, making it harder to detect.

Cherepanov and Strýček were confident that their discovery, which they dubbed PromptLock, marked a turning point in generative AI, showing how the technology could be exploited to create highly flexible malware attacks. They published a blog post declaring that they’d uncovered the first example of AI-powered ransomware, which quickly became the object of widespread global media attention.

But the threat wasn’t quite as dramatic as it first appeared. The day after the blog post went live, a team of researchers from New York University claimed responsibility, explaining that the malware was not, in fact, a full attack let loose in the wild but a research project, merely designed to prove it was possible to automate each step of a ransomware campaign—which, they said, they had. 

PromptLock may have turned out to be an academic project, but the real bad guys are using the latest AI tools. Just as software engineers are using artificial intelligence to help write code and check for bugs, hackers are using these tools to reduce the time and effort required to orchestrate an attack, lowering the barriers for less experienced attackers to try something out. 

The likelihood that cyberattacks will now become more common and more effective over time is not a remote possibility but “a sheer reality,” says Lorenzo Cavallaro, a professor of computer science at University College London. 

Some in Silicon Valley warn that AI is on the brink of being able to carry out fully automated attacks. But most security researchers say this claim is overblown. “For some reason, everyone is just focused on this malware idea of, like, AI superhackers, which is just absurd,” says Marcus Hutchins, who is principal threat researcher at the security company Expel and famous in the security world for ending a giant global ransomware attack called WannaCry in 2017. 

Instead, experts argue, we should be paying closer attention to the much more immediate risks posed by AI, which is already speeding up and increasing the volume of scams. Criminals are increasingly exploiting the latest deepfake technologies to impersonate people and swindle victims out of vast sums of money. These AI-enhanced cyberattacks are only set to get more frequent and more destructive, and we need to be ready.

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On Saturday, the US and Israel began a joint attack on Iran, killing several key figures, including Supreme Leader Ali Khamenei. Tehran retaliated fast and wide. Waves of drones and missiles rained down at US bases, Israel and the Gulf Arab states.

Iran then went after the region’s vital energy infrastructure. Saudi Arabia’s largest oil refinery halted operations after a drone strike. Qatar’s Ras Laffan energy complex halted operations after being hit. And shipping through the Strait of Hormuz slowed sharply as some vessels faced attacks.

In this unprecedented moment for the Middle East, some consequences for the region and the world are predictable—and some are not.

From Iran through Israel, Iraq, Lebanon, the six Gulf states and even Cyprus, this conflict now spans more than 10 countries. The fighting is no longer symbolic or limited with clear off-ramps for de-escalation; both sides are pursuing maximalist strategies.

Regime change in Tehran is the stated goal for Israel. ‎The US is vaguer about its objective. Yet together they conducted decapitation strikes. The government won’t fall so easily—airstrikes and calls for uprisings are insufficient tools for removing a system so entrenched within the country. Iran views the moment as existential and is striking friends and foes alike to raise the costs. By widening the battlefield, it seeks to force a stop to the attacks.

Three plausible paths now define the endgame. First, Tehran’s government could collapse—that’s unlikely soon but possible in the longer term. Second, both sides might accept a ceasefire to limit mounting costs. Third, failing that, the war drags on, widening geographically, drawing new actors and grinding at lower intensity.

If neither side achieves its objectives, economics and security will decide what comes next. The Middle East remains the world’s critical energy artery. Iran alone supplies about 5% of global oil. With its neighbors, the region provides about one-third of oil and one-fifth of gas. Much sails through the Strait of Hormuz, a chokepoint Iran can disrupt.

Losing these flows would jolt global energy markets. A full closure of Hormuz could lift oil toward $108 per barrel, which would be the highest since Russia invaded Ukraine in 2022. If disruptions persist, inflation pressures would resurface worldwide. Central banks could shelve rate cuts, pause easing cycles—some may even tighten again.

An energy shock would reshape the war’s political calculus. Lower interest rates and cheaper oil anchor President Donald Trump’s economic agenda. A surge in both before midterm elections would cut against these objectives. Facing higher inflation and tighter policy, he may reconsider his war strategy.

Beyond politics and economics, logistics could end this war. Iran’s ability to sustain missile and drone barrages remains uncertain. Israel and Arab Gulf states have finite air defense stockpiles. Intense early salvos may have strained interceptors and readiness. When arsenals run low, Trump, ‎Netanyahu and the new leaders in Tehran may settle on a ceasefire.‎

Until then, what’s predictable is higher volatility in oil, prices generally and politics. The unintended is harder to foresee.

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Do you know what your competitive advantage is? Are you sure?  Competitive advantage is the most critical yet misunderstood facet of strategy. It’s more than a company’s strengths—it’s the reason customers choose its offerings over its peers’ or why it is able to deliver higher returns than its peers through capital efficiency, price premiums, or lower costs. Competitive advantage comprises unique operating models and hard-to-replicate assets such as intellectual property and customer access that enable a company to build superior value over time.

That foundation is shaky for many businesses today, the authors’ analysis shows. To identify shifts in competitive advantage, the authors looked for changes in market position using a metric they call the “shuffle rate”—an industry-level marker that measures the speed of change in the positions of market leaders and laggards. The study found that the shuffle rate has accelerated for more than 60 percent of industries in the past decade. This pattern suggests that the defining elements of competitive advantage are in flux, the degree of differentiation between market players is narrowing (causing more frequent positional changes), or both.

The result is an increasing erosion of competitive advantage for some companies and a critical opportunity to capture greater market share for others. Businesses in a sector with a decelerating shuffle rate may find themselves stuck in lagging positions as the industry’s top performers deepen competitive moats around their leadership. Conversely, those in an industry with an accelerating shuffle rate could find opportunities to attract customers previously locked in by their competitors.

Yet despite the importance of acting on these shifts, recent research shows that most companies aren’t monitoring how their industry positions and competitive advantages may be changing.

With competitive advantage under pressure, business leaders need to actively protect their edge over peers. They can do so by following five rules:  Develop a granular view of competitive advantage.   Tailor the advantage to each market.  Don’t overinvest in areas that won’t improve competitive position.  Boost the return on competitive advantage by embedding it into strategic decision-making.  And track metrics that can signal changes in the competitive landscape.

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As generative AI has transitioned from novelty to necessity for many businesses, both established companies and startups are racing to decipher AI trends and figure out how best to use the technology.  This gold rush spans a wide spectrum of companies — some building AI technologies themselves, others applying them to products or internal operations — which makes clear categorization difficult but increasingly important.

A strategic question remains underexplored: What type of AI company is being built by these startups? There are important implications not only for the leaders of these companies but also for their investors, existing and potential customers, and analysts.  Six types are identified.

1. AI Originators.  AI originators are building the intelligence itself. They develop commercially deployed foundational models for generative AI — large, general-purpose systems. Their models are typically trained on massive data sets, but some, like China’s DeepSeek, are experimenting with smaller, more focused models. 
2. AI Explorers.  AI explorers are startups whose primary mission is not to commercialize today’s AI but to invent tomorrow’s. Rather than optimizing existing tools, they probe scientific frontiers. They have, of course, both the highest potential risk and reward among AI startups. To succeed, they attract PhDs from top AI labs, rely on deep funding (often from mission-driven VCs or large incumbents), and are often secretive by design.
3. AI Infrastructure Builders.  AI infrastructure builders are the toolmakers or “pick and shovel” providers — companies providing the underlying infrastructure that allows others to build and deploy AI.  They don’t compete with model creators; rather, they make the model technology usable at scale.
4. AI Enhancers.  AI enhancers sit at the application layer — taking general-purpose models and applying them to specific vertical or function problems. These players are sometimes disparagingly viewed as putting “wrappers” around foundation generative AI models.  Descript simplifies video and podcast editing with AI. The enhancers segment, sometimes labeled “industry AI” or “vertical AI,” is where the majority of AI startup activity is happening.
5. AI Optimizers.  AI optimizers aren’t building or selling AI. Instead, they’re using it behind the scenes to improve how they operate. In most cases, they use more traditional analytical AI on structured numerical data, although some also use generative AI. These are AI optimizers: companies using AI as a means, not an end.
6. AI Experimenters.  The largest cohort — by far — consists of companies that are experimenting with AI but haven’t yet made it a strategic aspect of their business or products.  They may be using ChatGPT for internal content drafts, testing Midjourney for design work, or piloting AI customer support tools — but there’s no budget, road map, or integration plan. Many companies will remain here indefinitely.

These six categories aren’t mutually exclusive or permanent. A startup may begin as an experimenter, become an optimizer, and eventually evolve into an enhancer.

Strategic Implications for Founders, Investors, and Corporate Leaders.  For founder, clarity about your role helps avoid two classic traps: overbuilding (trying to be an originator without the R&D bench, for example) and underinvesting (being an enhancer without a user experience, data, or niche focus, for example). For investors, in an AI-saturated funding environment, due diligence requires a sharper lens.  As with any inflection point, the investing signal is buried in noise. The startup’s type of AI engagement helps surface it.  Corporate Customers seeking to buy AI capabilities from startups should match their organization’s needs and expectations to company type.

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In an era when anyone can “go live” at any moment, the leaders who rise above the noise aren’t necessarily the loudest—they’re the clearest.  

From both sides of the camera, Paula Rizzo has spent nearly two decades helping leaders understand that distinction. A former senior television producer at Fox News Channel, Rizzo is now a sought-after media trainer and strategist, a Writer’s Digest columnist, a LinkedIn trainer, and the author of Listful Thinking and Listful Living.

Preparation, she insists, is what separates confident leaders from rambling ones. “I always want people to feel comfortable,” she said. “Preparation and practice are the keys to that.” Her process includes rehearsing with common questions, identifying feared questions and refining answers before the interview ever happens.

Rizzo teaches with what she calls the “accordion method.” It’s having a short, a medium, and a long answer to any question that could plausibly be asked, she explained. “That way, in the moment, you can respond comfortably and succinctly in an informative way.”

One of the most common mistakes experts make, she said, is assuming experience equals readiness. “They think, oh, I’ll just talk. I do this all the time. I’m a professional speaker.” But it’s not the same kind of audience, she points out. Media demands immediacy. “You want to catch them in that headline,” she said.

Another misconception holds people back. “A common misconception is that ‘if I give away all my best stuff, they won’t buy the book.’ That’s untrue. In fact, generosity builds credibility—and trust converts.”

Rizzo also emphasizes performance without pretense. “The way we write is not typically the way we speak,” she said. “Speaking is much more conversational.” That requires practice, even for seasoned professionals. “Any media interview I do, I practice beforehand, always.”

Storytelling is central to her approach. “Audience members connect with stories and they remember you for that,” she said. “They may not remember your name, but they’ll remember your stories.”

For highly technical leaders, accessibility matters more than sounding impressive. “It’s not dumbing it down,” Rizzo said. “It’s simply making it accessible. Because if you’re losing people in the message, you’re not helping anybody.”

She also prepares clients for the inevitable curveballs. “It’s like improv,” she said. “That’s why it’s important to have practiced your message so you don’t feel caught off guard.” She encourages clients to anticipate difficult questions and practice pivots that remain authentic and respectful.

In the end, Rizzo believes the most effective communicators share two traits: restraint and attention. “Listening is one trait,” she said. “But I think the most important one is being succinct.”

In a world where leaders are constantly asked to speak, post, pitch and perform, Paula Rizzo’s work is a reminder that influence isn’t about saying more—it’s about saying what matters, clearly, confidently and with intention. The leaders who understand that won’t just survive the media landscape ahead. They’ll shape it.

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The effectiveness of traditional networking methods may be one of the biggest myths still shaping our professional careers. We’re told our networks are built from working the room, going to the steak dinners and staying visible at the right events. But after over 20 years as an innovation executive, the author began to question whether those spaces created real relationships or just polished performances.

So true to her innovation background, she started a multiyear experiment to discover where and how meaningful professional networks are formed today.  The biggest takeaway: networking isn’t evolving, it’s being rewritten. The next era of career growth will be shaped less by who you know and more by who genuinely knows you. Here are five lessons that may change how you build yours.

1. Expand your definition of what it means to “network”.  The first lesson from her experiment was simple and surprisingly disruptive: Networking doesn’t have to look like traditional networking.  She expanded her definition to include experiences, and opportunities to meet new and interesting people, and left no stone unturned.  What she found is that these spaces are creating new ways to meet people and build relationships with people that all crave belonging — not optics. These new waves of communities are born out of necessity, and needed by founders, solopreneurs, and corporate executives alike. Given the long-held belief that networking should be a formal exercise, it’s time to expand your own definition into the opportunity to meet others in unexpected places.
2. Join communities that thrive on real life — not resumes.  Sometimes, the best way to connect is to strip away the formal veneer of elite networking and use real-life contexts in real ways, about real topics. By taking the focus away from corporations, titles and resumes, the conversations shift towards true human connection.
3. Smaller rooms create stronger ties.  In my experiment, I learned it’s not about the big conferences; it’s about more intimate settings where you can skip the small talk and get quickly personal.  According to her I wanted to go where I looked forward to seeing people, and didn’t have to lead with transactions. So I started something else: dinners with my friends and their friends.”  These dinners start with interest and connection — specifically: share something Google doesn’t know about you. Does it get deep? Many times, yes. Does it stay appropriate? Usually. Regardless, she met women that hype her on LinkedIn and others that have become lifelong friends by focusing on the formula of faster, deeper connection.
4. The deepest networks are built around what you care about.  Some of the strongest relationships she built during this experiment didn’t begin as networking at all — they grew from shared purpose. When you invest your time in causes and ideas you genuinely care about, connection stops feeling transactional and starts becoming inevitable.
5. Can’t find what you’re looking for? Build it yourself.  If you’re struggling to find a community you connect with, chances are, you’re not alone. You just need to start one. That’s how most of the networks I joined were born. 

Networking isn’t experiencing a minor upgrade, it’s being fundamentally rewritten. It isn’t transactional, it’s relational and built in smaller, more intentional spaces built on trust, not performance. In the next era of work, the strongest networks won’t be defined by prestige, but by people willing to invest in one another.

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According to the author in his Rich Habits Study, he examined the daily habits of self-made millionaires. What he found was simple but powerful: these individuals didn’t succeed by accident. Their success and wealth were built through consistent habits practiced over time.  The study revealed four distinct “Paths to Wealth,” each with its own set of habits. One of those paths is the Dreamer-Entrepreneur Path — followed by people who turned ideas into businesses and businesses into lasting wealth.

Below are twenty core habits that every successful entrepreneur in the study shared and built into their daily lives.

1. I will forge success habits around my goals.  As a general rule, entrepreneurs are forced to adopt good habits. Their dreams and goals force them to acquire specific habits that enable them to grow, stay healthy, build relationships and acquire knowledge and/or skills that enable them to succeed.
2. I will build strong relationships with influencers.  Entrepreneurs devote more time to building and maintaining relationships with powerful influencers than any other category of self-made millionaire. For entrepreneurs, power relationships are their currency. Forging and maintaining power relationships is, second only to persistence, one of the main drivers of their success.
3. I will dream-set.  Entrepreneurs follow a clear vision that they mapped out long before becoming wealthy. Through that vision, they pursue success — relentlessly. Their vision gives them the passion and persistence to keep going when the going gets tough, which is often, especially in the early stages of their entrepreneurial journey.
4. I will do work that I love.  Most entrepreneurs become entrepreneurs because they are pursuing a dream they are passionate about. Because they like or love their work, they are willing to walk through hardship in achieving their dreams and goals.
5. I will forge daily goal habits.  Entrepreneurs pursue their goals every day. The pursuit of these goals is part of their daily priority list. They continuously tweak their goals as they are forced to navigate around unexpected problems, mistakes, failures and obstacles.
6. I will forge good health habits.  Most entrepreneurs in the author’s study were avid exercisers. They understood that exercise, especially aerobic exercise, helped boost brain performance and their energy levels, allowing them to weather those long work days.

The others are:  I will maintain a positive mental outlook.  I will control my words and emotions.  I will never quit on my dreams.  I will eliminate negative beliefs.  I will seek out mentors.  I will take calculated risks.  I will create multiple streams of income.  I will use the power of leverage.  I will not let fear keep me from my goals and dreams.  I will seek feedback from others.  I will ask for what I want or need.  I will create priority lists.  I will ask many questions.  And I will seek to add value to the lives of others.