The debate over whether artificial intelligence belongs in the corporate boardroom appears to be over — at least for the people responsible for generating revenue.

Seven in ten enterprise revenue leaders now trust AI to regularly inform their business decisions, according to a sweeping new study released Thursday by Gong, the revenue intelligence company. The finding marks a dramatic shift from just two years ago, when most organizations treated AI as an experimental technology relegated to pilot programs and individual productivity hacks.

The research, based on an analysis of 7.1 million sales opportunities across more than 3,600 companies and a survey of over 3,000 global revenue leaders spanning the United States, United Kingdom, Australia, and Germany, paints a picture of an industry in rapid transformation. Organizations that have embedded AI into their core go-to-market strategies are 65 percent more likely to increase their win rates than competitors still treating the technology as optional.

"I don't think people delegate decisions to AI, but they do rely on AI in the process of making decisions," Amit Bendov, Gong's co-founder and chief executive, said in an exclusive interview with VentureBeat. "Humans are making the decision, but they're largely assisted."

The distinction matters. Rather than replacing human judgment, AI has become what Bendov describes as a "second opinion" — a data-driven check on the intuition and guesswork that has traditionally governed sales forecasting and strategy.

Slowing growth is forcing sales teams to squeeze more from every rep

The timing of AI's ascendance in revenue organizations is no coincidence. The study reveals a sobering reality: after rebounding in 2024, average annual revenue growth among surveyed companies decelerated to 16 percent in 2025, marking a three-percentage-point decline year over year. Sales rep quota attainment fell from 52 percent to 46 percent over the same period.

The culprit, according to Gong's analysis, isn't that salespeople are performing worse on individual deals. Win rates and deal duration remained consistent. The problem is that representatives are working fewer opportunities—a finding that suggests operational inefficiencies are eating into selling time.

This helps explain why productivity has rocketed to the top of executive priorities. For the first time in the study's history, increasing the productivity of existing teams ranked as the number-one growth strategy for 2026, jumping from fourth place the previous year.

"The focus is on increasing sales productivity," Bendov said. "How much dollar-output per dollar-input."

The numbers back up the urgency. Teams where sellers regularly use AI tools generate 77 percent more revenue per representative than those that don't — a gap Gong characterizes as a six-figure difference per salesperson annually.

Companies are moving beyond basic AI automation toward strategic decision-making

The nature of AI adoption in sales has evolved considerably over the past year. In 2024, most revenue teams used AI for basic automation: transcribing calls, drafting emails, updating CRM records. Those use cases continue to grow, but 2025 marked what the report calls a shift "from automation to intelligence."

The number of U.S. companies using AI for forecasting and measuring strategic initiatives jumped 50 percent year over year. These more sophisticated applications — predicting deal outcomes, identifying at-risk accounts, measuring which value propositions resonate with different buyer personas — correlate with dramatically better results.

Organizations in the 95th percentile of commercial impact from AI were two to four times more likely to have deployed these strategic use cases, according to the study.

Bendov offered a concrete example of how this plays out in practice. "Companies have thousands of deals that they roll up into their forecast," he said. "It used to be based solely on human sentiment—believe it or not. That's why a lot of companies miss their numbers: because people say, 'Oh, he told me he'll buy,' or 'I think I can probably get this one.'"

AI changes that calculus by examining evidence rather than optimism. "Companies now get a second opinion from AI on their forecasting, and that improves forecasting accuracy dramatically — 10 [or] 15 percent better accuracy just because it's evidence-based, not just based on human sentiment," Bendov said.

Revenue-specific AI tools are dramatically outperforming general-purpose alternatives

One of the study's more provocative findings concerns the type of AI that delivers results. Teams using revenue-specific AI solutions — tools built explicitly for sales workflows rather than general-purpose platforms like ChatGPT — reported 13 percent higher revenue growth and 85 percent greater commercial impact than those relying on generic tools.

These specialized systems were also twice as likely to be deployed for forecasting and predictive modeling, the report found.

The finding carries obvious implications for Gong, which sells precisely this type of domain-specific platform. But the data suggests a real distinction in outcomes. General-purpose AI, while more prevalent, often creates what the report describes as a "blind spot" for organizations — particularly when employees adopt consumer AI tools without company oversight.

Research from MIT suggests that while only 59 percent of survey respondents said their teams use personal AI tools like ChatGPT at work, the actual figure is likely closer to 90 percent. This shadow AI usage poses security risks and creates fragmented technology stacks that undermine the potential for organization-wide intelligence.

Most sales leaders believe AI will reshape their jobs rather than eliminate them

Perhaps the most closely watched question in any AI study concerns employment. The Gong research offers a more nuanced picture than the apocalyptic predictions that often dominate headlines.

When asked about AI's three-year impact on revenue headcount, 43 percent of respondents said they expect it to transform jobs without reducing headcount — the most common response. Only 28 percent anticipate job eliminations, while 21 percent actually foresee AI creating new roles. Just 8 percent predict minimal impact.

Bendov frames the opportunity in terms of reclaiming lost time. He cited Forrester research indicating that 77 percent of a sales representative's time is spent on activities that don't involve customers — administrative work, meeting preparation, researching accounts, updating forecasts, and internal briefings.

"AI can eliminate, ideally, all 77 percent—all the drudgery work that they're doing," Bendov said. "I don't think it necessarily eliminates jobs. People are half productive right now. Let's make them fully productive, and whatever you're paying them will translate to much higher revenue."

The transformation is already visible in role consolidation. Over the past decade, sales organizations splintered into hyper-specialized functions: one person qualifies leads, another sets appointments, a third closes deals, a fourth handles onboarding. The result was customers interacting with five or six different people across their buying journey.

"Which is not a great buyer experience, because every time I meet a new person that might not have the full context, and it's very inefficient for companies," Bendov said. "Now with AI, you can have one person do all this, or much of this."

At Gong itself, sellers now generate 80 percent of their own appointments because AI handles the prospecting legwork, Bendov said.

American companies are adopting AI 18 months faster than their European counterparts

The study reveals a notable divide in AI adoption between the United States and Europe. While 87 percent of U.S. companies now use AI in their revenue operations, with another 9 percent planning adoption within a year, the United Kingdom trails by 12 to 18 months. Just 70 percent of UK companies currently use AI, with 22 percent planning near-term adoption — figures that mirror U.S. data from 2024.

Bendov said the pattern reflects a broader historical tendency for enterprise technology trends to cross the Atlantic with a delay. "It's always like that," he said. "Even when the internet was taking off in the US, Europe was a step behind."

The gap isn't permanent, he noted, and Europe sometimes leads on technology adoption — mobile payments and messaging apps like WhatsApp gained traction there before the U.S. — but for AI specifically, the American market remains ahead.

Gong says a decade of AI development gives it an edge over Salesforce and Microsoft

The findings arrive as Gong navigates an increasingly crowded market. The company, which recently surpassed $300 million in annual recurring revenue, faces potential competition from enterprise software giants like Salesforce and Microsoft, both of which are embedding AI capabilities into their platforms.

Bendov argues that Gong's decade of AI development creates a substantial barrier to entry. The company's architecture comprises three layers: a "revenue graph" that aggregates customer data from CRM systems, emails, calls, videos, and web signals; an intelligence layer combining large language models with approximately 40 proprietary small language models; and workflow applications built on top.

"Anybody that would want to build something like that—it's not a small feature, it's 10 years in development—would need first to build the revenue graph," Bendov said.

Rather than viewing Salesforce and Microsoft as threats, Bendov characterized them as partners, pointing to both companies' participation in Gong's recent user conference to discuss agent interoperability. The rise of MCP (Model Context Protocol) support and consumption-based pricing models means customers can mix AI agents from multiple vendors rather than committing to a single platform.

The real question is whether AI will expand the sales profession or hollow it out

The report's implications extend beyond sales departments. If AI can transform revenue operations — long considered a relationship-driven, human-centric function — it raises questions about which other business processes might be next.

Bendov sees the potential for expansion rather than contraction. Drawing an analogy to digital photography, he noted that while camera manufacturers suffered, the total number of photos taken exploded once smartphones made photography effortless.

"If AI makes selling simple, I could see a world—I don't know exactly what it looks like yet—but why not?" Bendov said. "Maybe ten times more jobs than we have now. It's expensive and inefficient today, but if it becomes as easy as taking a photo, the industry could actually grow and create opportunities for people of different abilities, from different locations."

For Bendov, who co-founded Gong in 2015 when AI was still a hard sell to non-technical business users, the current moment represents something he waited a decade to see. Back then, mentioning AI to sales executives sounded like science fiction. The company struggled to raise money because the underlying technology barely existed.

"When we started the company, we were born as an AI company, but we had to almost hide AI," Bendov recalled. "It was intimidating."

Now, seven out of ten of those same executives say they trust AI to help run their business. The technology that once had to be disguised has become the one thing nobody can afford to ignore.

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Just a few short weeks ago, Google debuted its Gemini 3 model, claiming it scored a leadership position in multiple AI benchmarks. But the challenge with vendor-provided benchmarks is that they are just that — vendor-provided.

A new vendor-neutral evaluation from Prolific, however, puts Gemini 3 at the top of the leaderboard. This isn't on a set of academic benchmarks; rather, it's on a set of real-world attributes that actual users and organizations care about. 

Prolific was founded by researchers at the University of Oxford. The company delivers high-quality, reliable human data to power rigorous research and ethical AI development. The company's “HUMAINE benchmark” applies this approach by using representative human sampling and blind testing to rigorously compare AI models across a variety of user scenarios, measuring not just technical performance but also user trust, adaptability and communication style.

The latest HUMAINE test evaluated 26,000 users in a blind test of models. In the evaluation, Gemini 3 Pro's trust score surged from 16% to 69%, the highest ever recorded by Prolific. Gemini 3 now ranks number one overall in trust, ethics and safety 69% of the time across demographic subgroups, compared to its predecessor Gemini 2.5 Pro, which held the top spot only 16% of the time.

Overall, Gemini 3 ranked first in three of four evaluation categories: performance and reasoning, interaction and adaptiveness and trust and safety. It lost only on communication style, where DeepSeek V3 topped preferences at 43%. The HUMAINE test also showed that Gemini 3 performed consistently well across 22 different demographic user groups, including variations in age, sex, ethnicity and political orientation. The evaluation also found that users are now five times more likely to choose the model in head-to-head blind comparisons.

But the ranking matters less than why it won.

"It's the consistency across a very wide range of different use cases, and a personality and a style that appeals across a wide range of different user types," Phelim Bradley, co-founder and CEO of Prolific, told VentureBeat. "Although in some specific instances, other models are preferred by either small subgroups or on a particular conversation type, it's the breadth of knowledge and the flexibility of the model across a range of different use cases and audience types that allowed it to win this particular benchmark."

How blinded testing reveals what academic benchmarks miss

HUMAINE's methodology exposes gaps in how the industry evaluates models. Users interact with two models simultaneously in multi-turn conversations. They don't know which vendors power each response. They discuss whatever topics matter to them, not predetermined test questions.

It's the sample itself that matters. HUMAINE uses representative sampling across U.S. and UK populations, controlling for age, sex, ethnicity and political orientation. This reveals something static benchmarks can't capture: Model performance varies by audience.

"If you take an AI leaderboard, the majority of them still could have a fairly static list," Bradley said. "But for us, if you control for the audience, we end up with a slightly different leaderboard, whether you're looking at a left-leaning sample, right-leaning sample, U.S., UK. And I think age was actually the most different stated condition in our experiment."

For enterprises deploying AI across diverse employee populations, this matters. A model that performs well for one demographic may underperform for another.

The methodology also addresses a fundamental question in AI evaluation: Why use human judges at all when AI could evaluate itself? Bradley noted that his firm does use AI judges in certain use cases, although he stressed that human evaluation is still the critical factor.

"We see the biggest benefit coming from smart orchestration of both LLM judge and human data, both have strengths and weaknesses, that, when smartly combined, do better together," said Bradley. "But we still think that human data is where the alpha is. We're still extremely bullish that human data and human intelligence is required to be in the loop."

What trust means in AI evaluation

Trust, ethics and safety measures user confidence in reliability, factual accuracy and responsible behavior. In HUMAINE's methodology, trust isn't a vendor claim or a technical metric — it's what users report after blinded conversations with competing models.

The 69% figure represents probability across demographic groups. This consistency matters more than aggregate scores because organizations can serve diverse populations.

"There was no awareness that they were using Gemini in this scenario," Bradley said. "It was based only on the blinded multi-turn response."

This separates perceived trust from earned trust. Users judged model outputs without knowing which vendor produced them, eliminating Google's brand advantage. For customer-facing deployments where the AI vendor remains invisible to end users, this distinction matters.

What enterprises should do now

One of the critical things that enterprises should do now when considering different models is embrace an evaluation framework that works.

"It is increasingly challenging to evaluate models exclusively based on vibes," Bradley said. "I think increasingly we need more rigorous, scientific approaches to truly understand how these models are performing."

The HUMAINE data provides a framework: Test for consistency across use cases and user demographics, not just peak performance on specific tasks. Blind the testing to separate model quality from brand perception. Use representative samples that match your actual user population. Plan for continuous evaluation as models change.

For enterprises looking to deploy AI at scale, this means moving beyond "which model is best" to "which model is best for our specific use case, user demographics and required attributes."

 The rigor of representative sampling and blind testing provides the data to make that determination — something technical benchmarks and vibes-based evaluation cannot deliver.

Presented by Celonis

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When tariff rates change overnight, companies have 48 hours to model alternatives and act before competitors secure the best options. At Celosphere 2025 in Munich, enterprises demonstrated how they’re turning that chaos into competitive advantage — with quantifiable results that separate winners from losers.

Vinmar International: Theglobal plastics and chemicals distributor created a real-time digital twin of its $3B supply chain, cutting default expedites by more than 20% and improving delivery agility across global operations.

Florida Crystals: One of America's largest cane sugar producers, the company unlocked millions in working capital and strengthened supply chain resilience by eliminating manual rework across Finance, Procurement, and Inbound Supply. AI pilots now extend gains into invoice processing, predictive maintenance, and order management.

ASOS: The ecommerce fashion giant connected its end-to-end supply chain for full transparency, reducing process variation, accelerating speed-to-market, and improving customer experience at scale.

The common thread here: process intelligence that bridges the gap traditional ERP systems can’t close — connecting operational dots across ERP, finance, and logistics systems when seconds matter.

“The question isn’t whether disruptions will hit,” says Peter Budweiser, General Manager of Supply Chain at Celonis. “It’s whether your systems can show you what’s breaking fast enough to fix it.”

That visibility gap costs the average company double-digit millions in working capital and competitive positioning. As 54% of supply chain leaders face disruptions daily, the pressure is shifting to AI agents that execute real actions: triggering purchase orders, rerouting shipments, adjusting inventory. But an autonomous agent acting on stale or siloed data can make million-dollar mistakes when tariff structures shift overnight.

Tariffs, as old as trade itself, have become the ultimate stress test for enterprise AI — revealing whether companies truly understand their supply chains and whether their AI can be trusted to act.

Modern ERP: Data rich, insight poor

Supply chain leaders face a paradox: drowning in data while starving for insight. Traditional enterprise systems — SAP, Oracle, PeopleSoft — capture every transaction meticulously.

SAP logs the purchase order. Oracle tracks the shipment. The warehouse system records inventory movement. Each performs its function, but when tariffs change and companies need to model alternative sourcing scenarios across all three simultaneously, the data sits in silos.

“What’s changed is the speed at which disruptions cascade,” says Manik Sharma, Head of Supply Chain GTM AI at Celonis. “Traditional ERP systems weren’t built for today’s volatility.”

Companies generate thousands of reports showing what happened last quarter. They struggle to answer what happens if tariffs increase 25% tomorrow and need to switch suppliers within days.

Tariffs: The 48-hour scramble

Global trade volatility has transformed tariffs from predictable costs into strategic weapons. When new rates drop with unprecedented frequency, input costs spike across suppliers, finance teams scramble to calculate margin impact, and procurement races to identify alternatives buried in disconnected systems where no one knows if switching suppliers delays shipments or violates contracts.

By hour 48, competitors who already modeled scenarios execute supplier switches while late movers face capacity constraints and premium pricing.

Process intelligence changes that dynamic by allowing businesses to continuously model “what-if” scenarios, showing leaders how tariff changes cascade through suppliers, contracts, production lines, warehouses, and customers. When rates hit, companies can move within hours instead of days.

No AI without PI: Why process intelligence is non-negotiable for supply chains

AI and supply chains are mutually dependent: AI needs operational context, and supply chains need AI to keep pace with volatility. But here's the truth — there is no AI without PI. Without process intelligence, AI agents operate blindly.

The ongoing SAP migration wave illustrates why. An estimated 85–90% of SAP customers are still moving from ECC to S/4HANA. Moving to newer databases doesn’t solve supply chain visibility — it provides faster access to the same fragmented data.

Kerry Brown, a transformation evangelist at Celonis, sees this across industries.

“Organizations are shifting from PeopleSoft to Oracle, or EBS to Fusion. The bulk is in SAP,” she explains. “But what they really need isn’t a new ERP. They need to understand how work actually flows across systems they already have.”

That requires end-to-end operational context. Process intelligence provides this by enabling companies to extract and connect event data across systems, showing how processes execute in real time.

This distinction becomes critical when deploying autonomous agents. When visibility is fragmented, autonomous agents can easily make decisions that appear rational locally but create downstream disruption. With real-time context, AI can operate with clarity and precision, and supply chains can stay ahead of tariff-driven disruption.

Digital Twins: Powering real-time response

The companies highlighted at Celosphere all applied the same principle: understand how processes run across systems in real time. Celonis PI creates a digital twin above existing systems, using its Process Intelligence Graph to link orders, shipments, invoices, and payments end-to-end. Dependencies that traditional integrations miss become visible. A delay in SAP instantly reveals its impact across Oracle, warehouse scheduling, and customer delivery commitments.

“The platform brings together process data spanning systems and departments, enriched with business context that powers AI agents to transform operations effectively,” says Daniel Brown, Chief Product Officer at Celonis.

With this cross-system awareness, Celonis coordinates actions across complex workflows involving AI agents, humans, and automations — especially critical when tariffs force rapid decisions about suppliers, shipments, and customers.

Zero-copy integration enables instant modeling

A key advancement unveiled at Celosphere — zero-copy integration with Databricks — removes another barrier. Traditionally, analyzing supply chain data meant copying from source systems into central warehouses, creating data latency.

Celonis Data Core now integrates directly with platforms like Databricks and Microsoft Fabric, querying billions of records in near real time without duplication. When trade policy shifts, companies model alternatives instantly, not after overnight data refresh cycles.

Enhanced Task Mining extends this by connecting desktop activity — keystrokes, mouse clicks, screen scrolls — to business processes. This exposes manual work invisible to system logs: spreadsheet gymnastics, email negotiations, phone calls that keep supply chains moving during urgent changes.

Competitive advantage in volatile markets

Most companies can’t rip out and replace systems running critical operations — nor should they. Process intelligence offers a different path: compose workflows from existing systems, deploy AI where it creates value, and adapt continuously as conditions change. This “Free the Process” movement liberates companies from rigid architectures without forcing wholesale replacement.

As global trade volatility intensifies, the companies that model will move faster, make smarter decisions, and turn tariff chaos into competitive advantage — all while existing ERPs keep running.

When the next wave of tariffs hits — and it will — companies won’t have days to respond. They’ll have hours. The question isn’t whether your ERP captures the data. It’s whether your systems connect the dots fast enough to matter.

Missed Celosphere 2025? Catch up with all the highlights here.

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Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com.

Presented by Indeed

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As AI continues to reshape how we work, organizations are rethinking what skills they need, how they hire, and how they retain talent. According to Indeed’s 2025 Tech Talent report, tech job postings are still down more than 30% from pre-pandemic highs, yet demand for AI expertise has never been greater. New roles are emerging almost overnight, from prompt engineers to AI operations managers, and leaders are under growing pressure to close skill gaps while supporting their teams through change.

Shibani Ahuja, SVP of enterprise IT strategy at Salesforce; Matt Candy, global managing partner of generative AI strategy and transformation at IBM; and Jessica Hardeman, global head of attraction and engagement at Indeed came together for a recent roundtable conversation about the future of tech talent strategy, from hiring and reskilling to how it's reshaping the workforce.

Strategies for sourcing talent

To find the right candidates, organizations need to be certain their communication is clear from the get-go, and that means beginning with a well-thought-out job description, Hardeman said.

"How clearly are you outlining the skills that are actually required for the role, versus using very high-level or ambiguous language," she said. "Something that I also highly recommend is skill-cluster sourcing. We use that to identify candidates that might be adjacent to these harder-to-find niche skills. That’s something we can upskill people into. For example, skills that are in distributed computing or machine learning frameworks also share other high-value capabilities. Using these clusters can help recruiters identify candidates that may not have that exact skill set you’re looking for, but can quickly upskill into it."

Recruiters should also be upskilled, able to spot that potential in candidates. And once they're hired, companies have to be intentional about how they’re growing talent from the day they step in the door.

"What that means in the near term is focusing on the mentorship, embedding that AI fluency into their onboarding experience, into their growth, into their development," she said. "That means offering upskilling that teaches not just the tools they’ll need, but how to think with those tools and alongside those. The new early career sweet spot is where technical skills meet our human strengths. Curiosity. Communication. Data judgment. Workflow design. Those are the things that AI cannot replicate or replace. We have to create mentorship and sponsorship opportunities. Well-being and culture are critical components to ensuring that we’re creating good places for that early-in-career talent to land."

How work will evolve along AI

As AI becomes embedded into daily technical work, organizations are rethinking what it means to be a developer, designer, or engineer. Instead of automating roles end to end, companies are increasingly building AI agents that act as teammates, supporting workers across the entire software development lifecycle.

Candy explained that IBM is already seeing this shift in action through its Consulting Advantage platform, which serves as a unified AI experience layer for consultants and technical teams.

“This is a platform that every one of our consultants works with,” he said. “It’s supported by every piece of AI technology and model out there. It’s the place where our consultants can access thousands of agents that help them in each job role and activity they’re doing.”

These aren’t just prebuilt tools — teams can create and publish their own agents into an internal marketplace. That has sparked a systematic effort to map every task across traditional tech roles and build agents to enhance them.

“If I think about your traditional designer, DevOps engineer, AI Ops engineer — what are all the different agents that are supporting them in those activities?” Candy said. “It’s far more than just coding. Tools like Cursor, Windsurf, and GitHub Copilot accelerate coding, but that’s only one part of delivering software end to end. We’re building agents to support people at every stage of that journey.”

Candy said this shift leads toward a workplace where AI becomes a collaborative partner rather than a replacement, something that enables tech workers to spend more time on creative, strategic, and human-centered tasks.

"This future where employees have agents working alongside them, taking care of some of these repetitive activities, focusing on higher-value strategic work where human skills are innately important, I think becomes right at the heart of that,” he explained. “You have to unleash the organization to be able to think and rethink in that way."

A lot of that depends on the mindset of company leaders, Ahuja said.

"I can see the difference between leaders that look at AI as cost-cutting, reduction — it’s a bottom-line activity,” she said. “And then there are organizations that are starting to shift their mindset to say, no, the goal is not about replacing people. It’s about reimagining the work to make us humans more human, ironically. For some leaders that’s the story their PR teams have told them to say. But for those that actually believe that AI is about helping us become more human, it’s interesting how they’re bringing that to life and bridging this gap between humanity and digital labor."

Shifting the culture toward AI

The companies that are most successful at navigating the obstacles around successful AI implementation and culture change make employees their first priority, Ahuja added. They prioritize use cases that solve the most boring problems that are burdening their teams, demonstrating how AI will help, as opposed to looking at what the maximum number of jobs automation can replace.

"They’re thinking of it as preserving human accountability, so in high-stakes moments, people will still make that final call," she said. "Looking at where AI is going to excel at scale and speed with pattern recognition, leaving that space for humans to bring their judgement, their ethics, and their emotional intelligence. It seems like a very subtle shift, but it’s pretty big in terms of where it starts at the beginning of an organization and how it trickles down."

It's also important to build a level of comfort in using AI in employees’ day-to-day work. Salesforce created a Slack chat called Bite-Sized AI in which they encourage every colleague, including company leaders, to talk about where they're using AI and why, and what hacks they've found.

"That’s creating a safe space," Ahuja explained. "It’s creating that psychological safety — that this isn’t just a buzzword. We’re trying to encourage it through behavior."

"This is all about how you ignite, especially in big enterprises, the kind of passion and fire inside everyone’s belly," Candy added. "Storytelling, showing examples of what great looks like. The expression is 'demos, not memos'. Stop writing PowerPoint slides explaining what we're going to do and actually getting into the tools to show it in real life.”

AI makes that continuous learning a non-negotiable, Hardeman added, with companies training employees in understanding how to use the AI tools they're provided, and that goes a long way toward building that AI culture.

"We view upskilling as a retention lever and a performance driver," she said. "It creates that confidence, it reduces the fear around AI adoption. It helps people see a future for themselves as the technology evolves. AI didn’t just raise the bar on skills. It raised the bar on how we’re trying to support our people. It’s important that we are also rising to that occasion, and we’re not just raising expectations on the folks that we work with."

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Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com.

The social media giant will partner with one of Brazil’s leading renewable energy providers to develop a data center in the northeastern state of Ceara.

Data center interconnect stands out as the fastest growing part of the network and shows no signs of slowing with the data center boom.

We look at some of the latest data center developments announced over the past month.