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Amazon Web Services on Wednesday introduced Kiro powers, a system that allows software developers to give their AI coding assistants instant, specialized expertise in specific tools and workflows — addressing what the company calls a fundamental bottleneck in how artificial intelligence agents operate today.AWS made the announcement at its annual re:Invent conference in Las Vegas. The capability marks a departure from how most AI coding tools work today. Typically, these tools load every possible capability into memory upfront — a process that burns through computational resources and can overwhelm the AI with irrelevant information. Kiro powers takes the opposite approach, activating specialized knowledge only at the moment a developer actually needs it.”Our goal is to give the agent specialized context so it can reach the right outcome faster — and in a way that also reduces cost,” said Deepak Singh, Vice President of Developer Agents and Experiences at Amazon, in an exclusive interview with VentureBeat.The launch includes partnerships with nine technology companies: Datadog, Dynatrace, Figma, Neon, Netlify, Postman, Stripe, Supabase, and AWS’s own services. Developers can also create and share their own powers with the community.Why AI coding assistants choke when developers connect too many toolsTo understand why Kiro powers matters, it helps to understand a growing tension in the AI development tool market.Modern AI coding assistants rely on something called the Model Context Protocol, or MCP, to connect with external tools and services. When a developer wants their AI assistant to work with Stripe for payments, Figma for design, and Supabase for databases, they connect MCP servers for each service.The problem: each connection loads dozens of tool definitions into the AI’s working memory before it writes a single line of code. According to AWS documentation, connecting just five MCP servers can consume more than 50,000 tokens — roughly 40 percent of an AI model’s context window — before the developer even types their first request.Developers have grown increasingly vocal about this issue. Many complain that they don’t want to burn through their token allocations just to have an AI agent figure out which tools are relevant to a specific task. They want to get to their workflow instantly — not watch an overloaded agent struggle to sort through irrelevant context.This phenomenon, which some in the industry call “context rot,” leads to slower responses, lower-quality outputs, and significantly higher costs — since AI services typically charge by the token.Inside the technology that loads AI expertise on demandKiro powers addresses this by packaging three components into a single, dynamically-loaded bundle.The first component is a steering file called POWER.md, which functions as an onboarding manual for the AI agent. It tells the agent what tools are available and, crucially, when to use them. The second component is the MCP server configuration itself — the actual connection to external services. The third includes optional hooks and automation that trigger specific actions.When a developer mentions “payment” or “checkout” in their conversation with Kiro, the system automatically activates the Stripe power, loading its tools and best practices into context. When the developer shifts to database work, Supabase activates while Stripe deactivates. The baseline context usage when no powers are active approaches zero.”You click a button and it automatically loads,” Singh said. “Once a power has been created, developers just select ‘open in Kiro’ and it launches the IDE with everything ready to go.”How AWS is bringing elite developer techniques to the massesSingh framed Kiro powers as a democratization of advanced development practices. Before this capability, only the most sophisticated developers knew how to properly configure their AI agents with specialized context — writing custom steering files, crafting precise prompts, and manually managing which tools were active at any given time.”We’ve found that our developers were adding in capabilities to make their agents more specialized,” Singh said. “They wanted to give the agent some special powers to do a specific problem. For example, they wanted their front end developer, and they wanted the agent to become an expert at backend as a service.”This observation led to a key insight: if Supabase or Stripe could build the optimal context configuration once, every developer using those services could benefit.”Kiro powers formalizes that — things that people, only the most advanced people were doing — and allows anyone to get those kind of skills,” Singh said.Why dynamic loading beats fine-tuning for most AI coding use casesThe announcement also positions Kiro powers as a more economical alternative to fine-tuning, the process of training an AI model on specialized data to improve its performance in specific domains.”It’s much cheaper,” Singh said, when asked how powers compare to fine-tuning. “Fine-tuning is very expensive, and you can’t fine-tune most frontier models.”This is a significant point. The most capable AI models from Anthropic, OpenAI, and Google are typically “closed source,” meaning developers cannot modify their underlying training. They can only influence the models’ behavior through the prompts and context they provide.”Most people are already using powerful models like Sonnet 4.5 or Opus 4.5,” Singh said. “What those models need is to be pointed in the right direction.”The dynamic loading mechanism also reduces ongoing costs. Because powers only activate when relevant, developers aren’t paying for token usage on tools they’re not currently using.Where Kiro powers fits in Amazon’s bigger bet on autonomous AI agentsKiro powers arrives as part of a broader push by AWS into what the company calls “agentic AI” — artificial intelligence systems that can operate autonomously over extended periods.Earlier at re:Invent, AWS announced three “frontier agents” designed to work for hours or days without human intervention: the Kiro autonomous agent for software development, the AWS security agent, and the AWS DevOps agent. These represent a different approach from Kiro powers — tackling large, ambiguous problems rather than providing specialized expertise for specific tasks.The two approaches are complementary. Frontier agents handle complex, multi-day projects that require autonomous decision-making across multiple codebases. Kiro powers, by contrast, gives developers precise, efficient tools for everyday development tasks where speed and token efficiency matter most.The company is betting that developers need both ends of this spectrum to be productive.What Kiro powers reveals about the future of AI-assisted software developmentThe launch reflects a maturing market for AI development tools. GitHub Copilot, which Microsoft launched in 2021, introduced millions of developers to AI-assisted coding. Since then, a proliferation of tools — including Cursor, Cline, and Claude Code — have competed for developers’ attention.But as these tools have grown more capable, they’ve also grown more complex. The Model Context Protocol, which Anthropic open-sourced last year, created a standard for connecting AI agents to external services. That solved one problem while creating another: the context overload that Kiro powers now addresses.AWS is positioning itself as the company that understands production software development at scale. Singh emphasized that Amazon’s experience running AWS for 20 years, combined with its own massive internal software engineering organization, gives it unique insight into how developers actually work.”It’s not something you would use just for your prototype or your toy application,” Singh said of AWS’s AI development tools. “If you want to build production applications, there’s a lot of knowledge that we bring in as AWS that applies here.”The road ahead for Kiro powers and cross-platform compatibilityAWS indicated that Kiro powers currently works only within the Kiro IDE, but the company is building toward cross-compatibility with other AI development tools, including command-line interfaces, Cursor, Cline, and Claude Code. The company’s documentation describes a future where developers can “build a power once, use it anywhere” — though that vision remains aspirational for now.For the technology partners launching powers today, the appeal is straightforward: rather than maintaining separate integration documentation for every AI tool on the market, they can create a single power that works everywhere Kiro does. As more AI coding assistants crowd into the market, that kind of efficiency becomes increasingly valuable.Kiro powers is available now to developers using Kiro IDE version 0.7 or later at no additional charge beyond the standard Kiro subscription.The underlying bet is a familiar one in the history of computing: that the winners in AI-assisted development won’t be the tools that try to do everything at once, but the ones smart enough to know what to forget.

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 repThe 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-makingThe 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 alternativesOne 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 themPerhaps 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 counterpartsThe 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 MicrosoftThe 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 outThe 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.

Presented by Oracle NetSuiteWhen Evan Goldberg started NetSuite in 1998, his vision was radically simple: give entrepreneurs access to their business data anytime, anywhere. At the time, most enterprise software lived on local servers. As an entrepreneur himself, Goldberg understood the frustration intimately. “I had fragmented systems. They all said something different,” he recalls of his early days. NetSuite was the first company to deliver enterprise applications entirely through web browsers, combining CRM, ERP, and ecommerce into one unified platform. That breakthrough idea pioneered the cloud computing and software-as-a-service (SaaS) era and propelled supersonic growth, a 2007 IPO, and an acquisition by Oracle in 2016. Still innovating at the leading-edge That founding obsession — turning scattered data into accessible, coherent, actionable intelligence — is driving NetSuite as it reshapes the next generation of enterprise software.At SuiteWorld 2025 last month, the Austin-based firm unveiled NetSuite Next. Goldberg calls it “the biggest product evolution in the company’s history.” The reason? While NetSuite has embedded AI capabilities into workflows for years, he explains, Next represents a quantum leap — contextual, conversational, agentic, composable AI becoming an extension of operations, not separate tools.AI woven into everyday business operations Most enterprise AI today gets bolted on through APIs and conversational interfaces. NetSuite Next operates differently. Intelligence runs deep in workflows instead of sitting on the surface. It autonomously reconciles accounts, optimizes payment timing, predicts cash crunches, and surfaces its reasoning at every step. It doesn’t just advise on business processes — it executes them, transparently, within human-defined guardrails.”We built NetSuite for entrepreneurs so that they could get great information about their business,” Goldberg explains. “I think the next step is to be able to get deeper insights and analysis without being an expert in analytics. AI turns out to be a really good data scientist.”This architectural divergence reflects competing philosophies about enterprise technology adoption. Microsoft and SAP have pursued rapid deployment through add-on assistants. NetSuite’s five-year development cycle for Next represents a more fundamental reimagining: making AI an everyday tool woven into business operations, not a separate application requiring constant context-switching.AI echoes and deepens cloud innovation Goldberg sees a clear through line connecting today’s AI adoption and the cloud computing era he pioneered. “There’s sort of an infinite sense of possibility that exists in the technology world,” he says. “Everybody is thinking about how they can leverage this, how they’re going to get involved.”When NetSuite was starting, he continues, “We had to come to customers with the cloud and say, ‘This won’t disrupt your operations. It’s going to make them better.'” Today, evangelizing enterprise leaders on advanced AI requires a similar approach — demonstrating immediate value while minimizing implementation risk. For NetSuite, continuous innovation around maximizing customer data for growth is an undeniable theme that connects both eras.New transformative capabilities NetSuite’s latest AI capabilities span business operations, while blurring (in a good way) the lines between human and machine intervention:Context-aware intelligence. Ask Oracle adapts responses based on user role, current workflow, and business context. A CFO requesting point-of-sale data receives financial analytics. A warehouse manager asking the same question sees inventory insights.Collaborative workflow design. AI Canvas functions as a scenario-planning workspace where business users articulate processes in natural language. A finance director can describe approval hierarchies for capital expenditures —”For amounts over $50,000, I need department head approval, then CFO sign-off” — which the system translates into executable workflows with appropriate controls and audit trails.Governed autonomous operations. Autonomous workflows operate within defined parameters, reconciling accounts, generating payment runs, predicting cash flow. When the system recommends accelerating payment to a supplier, it shows which factors influenced the decision — transparent logic users can accept, modify, or override.Open AI architecture. Built to support Model Context Protocol, NetSuite AI Connector Service enables enterprises to integrate external large language models while supporting governance.Critically, NetSuite adds AI capabilities at no additional cost — embedded directly into workflows employees already use daily.Security and privacy from Oracle infrastructure Built-in AI requires robust infrastructure that bolt-on approaches sidestep. Here, according to NetSuite, tight integration within Oracle technology provides operational and competitive advantages, especially security and compliance peace of mind. Engineers say that’s because NetSuite is supported by Oracle’s complete stack. From database to applications to analytics, the system optimizes decisions using data from multiple sources in real time.”That’s why I started NetSuite. I couldn’t get the data I wanted,” Goldberg reflects. “That’s one of the most differentiated aspects of NetSuite. When you’re doing your financial close, and you’re thinking about what reserves you’re going to take, you can look at your sales data, because that’s also there in NetSuite. With NetSuite Next, AI can also help you make those kinds of decisions.”And performance improves with use. As the platform learns from millions of transactions across thousands of customers, its embedded intelligence improves in ways that bolt-on assistants operating adjacent to core systems cannot match.NetSuite’s customer base demonstrates this scalability advantage — from startups that became global enterprises including Reddit, Shopify, and DoorDash; as well as promising newcomers like BERO, a brewer of non-alcoholic beer founded by actor Tom Holland, Chomps meat snacks, PetLab, and Kieser Australia. The unified platform grows with businesses rather than requiring migration as they scale.Keeping fire in the belly after three decadesHow does a nearly 30-year-old company maintain innovative capacity, particularly as part of a mammoth corporate ecosystem? Goldberg credits the parent company’s culture of continuous reinvention.”I don’t know if you’ve heard about this guy Larry Ellison,” he smiles. “He manages to seemingly reinvent himself whenever one of these technology revolutions comes along. That hunger, that curiosity, that desire to make things constantly better imbues all of Oracle.”For Goldberg, the single biggest challenge facing NetSuite customers centers on integration complexity and trust. NetSuite Next addresses this by embedding AI within existing workflows rather than requiring separate systems.In addition, updates to SuiteCloud Platform — an extensibility and customization environment — help organizations adapt NetSuite to their unique business needs. Built on open standards, it lets enterprises mix and match AI models for different functions. SuiteAgent frameworks enable partners to build specialized automation directly into NetSuite. AI Studios give administrators control over how AI operates within specific industry needs.”This takes NetSuite’s flexibility to a new level,” Goldberg says, enabling customers and partners to “quickly and easily build AI agents, connect external AI assistants, and orchestrate AI processes.”“AI execution fabric” delivers measurable business impact Industry analysts increasingly argue that embedded AI features deliver superior results compared to add-on models. Futurum Group sees NetSuite Next as an “AI execution fabric” rather than a conversational layer — intelligence that runs deep in workflows instead of sitting on the surface.For midmarket enterprises navigating talent shortages, complex compliance frameworks, and competition from digital-native companies, the distinction between advice and execution matters economically.Built-in AI doesn’t just inform better decisions. It makes those decisions, transparently and autonomously, within human-defined guardrails. For enterprises making ERP decisions today, the choice carries long-term implications. Bolt-on AI can deliver immediate value for information access and basic automation. But built-in AI promises to transform operations with intelligence permeating every transaction and workflow.NetSuite Next begins rolling out to North American customers next year.Why 2026 will belong to the AI-first businessThe bet underlying NetSuite Next: Enterprises reimagining ERP operations around embedded intelligence will outperform those just adding bolt-on conversational assistance to existing systems. Early cloud computing adopters, Goldberg notes, gained competitive advantages that compounded over time. The same logic appears likely to apply to AI-first platforms. Simplicity and ease of use are two big advantages. “You don’t have to dig through lots of menus and understand all of the analytics capabilities,” Goldberg says. “It will quickly bring up an analysis for you, and then you can converse in natural language to hone in on what you think is most important.”The tools now think alongside users and take intelligently informed action. For midmarket and entrepreneurial companies, where the gap between having information and acting on it can be the difference between growth and failure, that kind of autonomous execution may determine which enterprises thrive in an AI-first era.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.