AI Industry Predictions for 2026

Welcome to the Radical Data Science (RDS) annual technology predictions round-up! The AI industry has significant inertia moving into 2026. In order to give our valued global audience a pulse on important new trends leading into next year, we here at RDS heard from many of our friends across the vendor ecosystem to get their insights, reflections and predictions for what may be coming. We were very encouraged to hear such exciting perspectives. Even if only half actually come true, AI in the next year is destined to be quite an exciting ride. Enjoy!

[NOTE: The last updates have been made! The commentaries below are in no particular order.]

Daniel D. Gutierrez – Principal AI Industry Analyst, Influencer & Resident Data Scientist

AI and automation will lead to more business disruptions; organizations will have to tackle this challenge head-on — As AI-driven response becomes embedded in security operations center (SOC) workflows, organizations can experience a new class of self-inflicted outages. AI systems will confidently take “correct” actions without grasping business context, such as locking out key authentication pathways or shutting down critical operations to contain perceived anomalies. This will require less tolerance for relying too much on AI and accepting such inconsistencies. In 2026, companies will need to stop accepting “the AI did it” as an excuse and formalize human-in-the-loop governance to prevent AI-triggered business downtime. – Steve Holmes, Gurucul

Prevention is dead in cybersecurity – By 2026, the myth of prevention as a primary strategy will be fully exposed. Attackers are faster, smarter, and more patient than ever, leveraging AI, deepfakes, and malware that can remain undetected for months, bypassing traditional defenses. Many vendors will continue to overemphasize prevention, presenting it as innovation while moving away from detection and response, but this approach is increasingly ineffective. Breach rates are rising 17 percent year over year, with 55 percent of organizations affected in the past 12 months alone, highlighting that relying solely on perimeter defenses is no longer sufficient.

This acceleration makes real-time detection, removal, and complete visibility critical. Organizations that implement continuous risk assessment, monitor third-party ecosystems, and maintain visibility into encrypted traffic where most threats now hide will gain a decisive advantage. Aligning AI initiatives with security priorities further ensures defenses keep pace with adversaries. In this landscape, resilience is not about keeping every threat out; it is about seeing, stopping, and learning from threats in real time. Prevention alone is a pipe dream; the companies that survive and thrive will be those that detect and remove threats before damage is done. – Shane Buckley, president and CEO at Gigamon

As companies move LLM models from pilot projects to fully deployed production systems, they will need AI Observability tools that can address not just model-related performance issues (like model drift, data quality and hallucination identification and prevention) but also can manage the health and performance of the underlying infrastructure systems. Rapid, sub-minute identification of anomalies and root cause analysis across both models and underlying infrastructure systems will be essential to an organization’s ability to capitalize on the promise of enterprise-scale AI. – Helen Gu, founder of InsightFinder AI 

2026 Kubernetes predictions – As AI workloads shift from training to massive-scale inference, SRE (Site Reliability Engineering) teams are about to feel even more pressure. GPU-heavy computing is breaking the assumptions today’s clusters were built on, while enterprises are beginning to trust autonomous operations and cost pressure is pushing consolidation across the cloud-infrastructure stack. Based on these forces, here are my 2026 Kubernetes predictions as well as some best practice recommendations to help platform teams prepare for what reliable operations will mean next year. – Itiel Shwartz, CTO and cofounder of Komodor

• As AI/ML use continues to increase more workloads will move from training to inference. Even the new GKE experiments are showing signs of this, as the huge number of nodes that they scale up with contain a significant amount of inference workloads.
• AI SRE will make a significant adoption impact. As more organizations deploy cloud native infrastructure, and GenAI cutting time to market for their competitors, platform teams will understand that to continue to innovate and lead, they need to scale up their SRE teams. With Kubernetes experts at a premium, AI SRE will prove to be the missing ingredient that allows them to adapt.
• Cloud operations will start to move towards autonomy. As more and more AI powered tooling is adopted, and users trust it more, we will see a movement among traditionally conservative enterprises towards allowing some operations to be autonomously managed by AI.
• Cloud-native job queueing systems, like Kueue will see a major uptick in adoption, as the race for deploying HPC, AI/ML, and even quantum applications heats up. Since previous queue systems are not built for this scale, new tooling will quickly be implemented across the industry.
• With applications and workloads relying on more compute than ever before, Kubernetes scheduling will require a makeover. The current pod-centric approach will not be able to handle this increased scale, so a more workload specific approach for the scheduler will be required. The community is actively working on this through KEP-4671: Gang Scheduling, which will be managed natively in K8s.
• GPU overprovisioning will become a more pressing problem. As the macro economic climate continues to push towards greater efficiency, organizations will have to find ways to optimize their GPU monitoring and usage.
• FinOps tools will start to consolidate with other products in the cloud infrastructure stack. Similar to what is happening in cloud security, products will consolidate different capabilities, including observability, insights, tracing, cost optimization and troubleshooting, into a single platform. This will remove cognitive load from teams struggling to keep up with too many dashboards and products.

The Rise of Decoupled Observability Stacks – In 2026, the era of the all-in-one observability black box will be over. AI is driving massive growth in logs, metrics, and traces, pushing tightly coupled observability platforms past their limits. Organizations are reaching a breaking point: they can no longer scale these monolithic systems without sacrificing data visibility or having to absorb runaway costs.

The cost and complexity of scaling current observability stacks will become unsustainable. Forward-thinking teams are already starting to rethink architecture, pulling apart the data layer from the tools that sit on top of it. We’ve seen this movie play out before – business intelligence went through the same evolution over the last 40 years. It started as tightly coupled stacks in the 80s and exists today as a decoupled architecture that gives teams flexibility, choice, and control. The separation gave rise to the Snowflakes, Databricks, Fivetrans, and Tableaus of the world. Observability is next.

The observability warehouse (i.e., specialized data stores for logs, metrics and traces) will emerge as the new standard, serving as a central data layer that reduces dependence on any one monolithic platform, freeing teams from vendor lock-in and letting them choose the best tools for the job. – Eric Tschetter, Chief Architect at Imply

Investment may increasingly shift toward fueling new human insight and the systems that preserve it. While most industries face data exhaustion, health tech remains uniquely advantaged, sitting on a vast, still-untapped trove of medical records, clinical notes, and real-world evidence. As AI models in other domains begin to plateau from recycled training data, healthcare has the rare opportunity to keep improving by responsibly unlocking, structuring, and digitizing the knowledge already within its walls. – Melvin Lai, Senior Venture Associate, Silicon Foundry

AI agents will move from non-production to production

Today, most AI agents still run outside production environments. That will change as organizations connect agents directly to live systems and workflows. At scale, this shift will force enterprises to actively manage agent permissions, lifecycle controls, and accountability. – Itamar Apelblat, CEO & Co-Founder, Token Security

The Data Backbone Becomes the Compliance Frontline — In 2026, organizations will find that compliance is no longer just about ticking boxes, it will hinge on the quality and structure of their data. Clean, unified data will be the foundation for leveraging AI to detect risks, enforce policies, and anticipate regulatory scrutiny. Companies that treat data architecture as a strategic asset will not only simplify compliance but also gain a competitive edge in using AI to understand complex operational environments. – Chase Doelling, Principal Strategist & Director at JumpCloud 

Hybrid AI Architecture Becomes the Enterprise Default — Enterprises will lead the way by deploying Small Language Models (SLMs) combined with Retrieval-Augmented Generation (RAG) on purpose-built AI hardware at the edge to offset the rising cost and latency of frontier LLMs. There will be an architectural shift toward hybrid AI data pipelines. Frontier models will be used for reasoning and edge SLMs for contextualization to redefine cost-per-insight as the new efficiency metric for AI operations. Enterprises will look to consolidate communication and collaboration tools to a smaller number of vendors. Users will desire to use one application for chat, calling, SMS/MMS messaging, and meetings both for internal collaboration and external communications. – Doug Ford, VP, Solutions Portfolio & Technical Readiness, All Covered

AI Agents Will Become Self-Building — In 2025, agentic AI went mainstream, but most solutions still required significant engineering lift: specialized teams, custom code, and months of hand-holding just to get the first workflow running. Next-generation AI agents will be created through low-code and no-code platforms designed for business users, not technical experts. Instead of relying on engineering teams, organizations will build, deploy, and iterate agents themselves. As companies gain the ability to create and maintain agents internally, deployments will become faster, cheaper, and more aligned with real operational needs. – Aquant co-founder, Assaf Melochna

2026 will be the year of AI’s security refresh — In 2025, we saw organizations take a shotgun approach to AI implementation. Organizations who joined the AI race invested a lot into AI technology in hopes of increasing productivity and ROI for the business. The rapid implementation of AI has introduced more vulnerabilities to the market. For example, we saw the emergence of prompt injection attacks,  where threat actors manipulate a LLM to bypass instructions and force unintended actions. With researchers discovering security vulnerabilities and AI code and implementations such as Google Gemini and Microsoft Copilot, a new category of threats has begun to emerge.. Rushing AI implementation, without the proper safeguards in place, sets a dangerous precedent and is creating security gaps that can lead to a downstream effect that impacts organizations of all sizes.

In 2026, organizations will double down on fixing and re-developing poorly implemented AI with specialized controls, and reinforce proven security frameworks to address vulnerabilities introduced by AI tools that were rushed to market. MSPs and small and mid-market IT teams will treat AI oversight like code review to catch risks before they hit production and protect clients’ trust. – Amanda Berlin, Senior Product Manager of Cybersecurity at Blumira

Why vector search should still be in your toolbag — While vector search isn’t nearly as comprehensive as GraphRAG, it still performs very well for simple retrieval tasks. We’ll see GraphRAG, and possibly other advanced techniques, used to synthesize data across complex organizational systems, providing LLMs with structured context and helping reduce hallucinations.

However, for many problems, vector search remains a perfectly sensible option and should always be considered. In other words, for simpler use cases, vector-only approaches are entirely adequate; they can get you in the ballpark and deliver good results. I expect engineers will continue testing vector search on information retrieval cases where they already know the answers, as a way to evaluate their LLMs. – Memgraph CEO, Dominik Tomicevic

2026 will be the year of Bring Your Own AI — Enterprises will stop looking for one-size-fits-all models and start layering AI into their architecture with flexibility. That shift will require orchestration platforms that can route tasks across GPT, Claude, Gemini, or open-source tools depending on use case, compliance needs, and performance. – Bryan Cheung, CMO of Liferay

CAPEX IT becomes a dinosaur — Next year, organizations clinging to CAPEX-heavy infrastructure models will find themselves in a major jam. As AI and economic volatility create a need for speed, agility, and cost predictability, rigid, asset-heavy IT strategies will collapse. The winners will be those who shift decisively to OPEX-based models, consuming infrastructure like a utility, scaling instantly, and paying only for what they use.Innovation can’t wait for a three-year hardware refresh cycle. Companies that fail to pivot to OPEX approaches will be outmaneuvered by competitors who have adapted themselves for change. – Vadim Vladimirskiy, CEO and co-Founder, Nerdio

Conversational Commerce Becomes the Default Front Door — AI-powered shopping will explode as consumers turn to conversational agents as the first stop for brand discovery and comparison. The next wave will see ‘shopping agents’ handling recommendations, price checks, and even purchasing, completely redefining how people engage with brands online. – Sarah Molloy, Director of Strategic Partnerships, Brij

For years, cybersecurity has focused on locking down devices. We’ve wrapped them in management software, antivirus, and access controls, all in an attempt to contain data that should never have been there in the first place.

Every year, enterprises pay more and more for corporate devices under the assumption they’re keeping corporate data separate from users’ personal devices. However, with apps like Outlook, Excel, and Google sheets all accessible on mobile devices, a breach of a personal device is a breach of enterprise data.

Moving into next year, AI-generated exploits will continue to be created and deployed in minutes. Our mobile device driven society has extended the attack surface beyond the control of IT and Cybersecurity staffs. We must concentrate on reducing the attack surface and protect our proprietary, sensitive and personal data with the same level of care. – CSO of Hypori, Matt Stern

AI Oversharing Trend — An emerging phenomenon called “AI oversharing” is where enterprise AI applications expose sensitive information not through attacks or breaches, but through poorly defined access controls. This is prevalent in popular Retrieval-Augmented Generation (RAG) architectures if the proper roles and permissions of the original data sources aren’t enforced. This continues to be one of the most significant yet underreported data privacy risks organizations face today.  – Oliver Friedrichs, CEO and Co-Founder, Pangea

AI may reduce operational inefficiencies, helping to accelerate growth for private practice owners — By eliminating operational inefficiencies—from documentation and scheduling workflows to claims and billing processes—AI technology can enable practices to scale their efforts, increase patient volumes, and grow the practice. With fewer errors, claims may be approved faster and reimbursements distributed in much shorter time. No show rates can drop, and patient billing may become more streamlined. With AI technology integrated into daily operations, private practices may be better positioned to remain financially resilient and thrive. – Nupura Kolwalkar-Rana, AdvancedMD’s Chief Product & Technology Officer

Impact on the energy sector — Rapid AI adoption is creating unprecedented energy demands that current power grids are not engineered to handle. The strain is colliding with the rising threat of sophisticated cyberattacks targeting critical infrastructure like power grids and pipelines, creating a new class of compounding risk. Service disruptions may become a normalized challenge, forcing many organizations to rethink operational resilience. – Adam Khan, VP, Global Security Operations at Barracuda  

Hackers will breach an AI application in 2025 — and then they will manipulate the AI application to cause problems in the target company. Organizations will need to start treating an AI application like a person, much in the same way as we did for bots not too long ago. – Bruce Esposito, Senior Manager of IGA Strategy and Product Marketing at One Identity.

A major Copilot-driven breach exposes the risks of AI over-permissioning: 2026 will see a headline-grabbing incident where Microsoft Copilot accesses sensitive data or executes privileged actions beyond its intended scope. As organizations rush to deploy AI copilots across productivity, code, and cloud environments, many will grant broad permissions “to keep things working.” This over-permissioning, combined with implicit trust in AI automation, will lead to unauthorized data exposure or lateral movement. The incident will force enterprises to adopt granular permission controls, audit trails, and continuous monitoring for AI assistants — treating them as powerful identities, not productivity add-ons. – Rob Rachwald, Vice President, Veza

Data Mesh Architecture: Decentralization of data ownership will become more prevalent, allowing teams to manage their own data as products. This will be particularly beneficial for large organizations seeking independent, high-quality data exchange. – Arnab Sen, VP of Data Engineering at Tredence

Ditching the cloud, moving data back to data centers — In 2026, enterprises will begin migrating select workloads and sensitive data from the public cloud back into their own data centers. The “trillion-dollar paradox,” as Andreessen Horowitz described it, is forcing business leaders to face a hard truth: the cloud’s convenience often hides long-term cost and control tradeoffs. The agility that once justified the cloud premium has become a drag on profitability. We will see more organizations move back to the data center because of the fear that the data entered into the cloud will be consumed by public LLMs. A number of organizations have private LLMs to do their AI work on-premises.  

Customers want tighter control over sensitive data and less exposure to cloud outages or the risk that public large language models will ingest proprietary information. The next phase of cloud adoption will look more balanced. Companies will keep what makes sense in the cloud and bring home the workloads that do not. Many will take a hard look at what they are paying for and what they gain in return, then move critical systems back into environments they can fully control. This shift will create more hybrid models that help organizations cut waste, tighten security, and make more informed decisions about where to store their most sensitive data based on cost, performance, and regulatory needs. – John Kindervag, Chief Evangelist at Illumio

Companies will hit an AI operations wall as projects scale from pilots to dozens of implementations — Technology and security leaders will face an AI operational bottleneck, struggling to scale from isolated pilots to enterprise-wide implementations. Industries that rely on complex data ecosystems like finance, manufacturing and healthcare will be particularly vulnerable to conflicting data pipelines, inconsistent architectures and uneven security practices. Without AIOps frameworks and strong governance structures, organizations risk losing visibility, control of their tech stacks and long-term operational resilience. – Siroui Mushegian, CIO at Barracuda 

While it is true that the security risks from AI models are continuing to grow both because of their capabilities and the stepped attacks against the guardrails these models have in place, it’s also important to not overhype the threats. We’ve already seen a couple of reports this year that exaggerated the threats that AI models currently pose. While we have reported an uptick in interest and capabilities of both nation state and cyber criminal threat actors when it comes to AI usage, these threats do not exceed the ability of organizations following best security practices. That may change in the future, however, at this moment it is more important than ever to understand the difference between “hype” and reality when it comes to AI and other threats. – Allan Liska, threat intelligence analyst, Recorded Future.  

The New Age of Deception: The Threat of AI Identity — In 2026, identity becomes the main target. Flawless, real-time AI deepfakes (like “CEO doppelgängers”) will make it impossible to tell a fake from a real person. This risk is huge because autonomous agents outnumber humans by an 82:1 ratio. We face a trust crisis where one forged command can start an automated disaster. Identity security must change from just blocking attacks to actively enabling the business by securing every human, machine, and AI agent. – Palo Alto Networks

AI adoption in 2026 will feel familiar — Most enterprises will continue using agentic AI to automate repeatable tasks and augment existing processes, not reinvent them. Only one in 5 organizations report getting meaningful value from their AI tools at the current time with key adoptions challenges being cost and lack of control mechanisms in context of the desired outcomes. Autonomous business intelligence will remain niche because the foundations including infrastructure required are simply not ready: data quality, governance maturity, and organizational skills still lag far behind the ambition.

Modernization efforts will remain the primary focus. Companies will keep working through the practical realities and motions to replace platforms like VMware and Citrix, while using SaaS to accelerate outcomes where it makes sense. At the same time, compliance and regulatory pressure will intensify. Leaders will need a clear understanding of sovereignty requirements, new operating models, and the talent divide between “old way” and “new way” practitioners.

In 2026, CIOs will be planning for what IT must look like in 2030. The problems they solve today will not be the ones they face next and there is a lot of pressure on the IT suite to ensure companies are ready and competitive as the AI  transformation gains momentum. – Niels van Ingen, SVP Business Development and Strategy, Keepit

Foundation Model providers compete fiercely for token share — Major model providers have amassed massive funding rounds and achieved lofty valuations. The demand for their services has grown exponentially so far but 2026 will see providers catch up and begin competing for share rather than growing freely into green-fields as they struggle to meet committed targets.

A token is the unit of AI processing (so called input tokens) or generation (output tokens, roughly 4x the price of inputs). A text token can be as short as a single character or as long as a word, depending on the language model and how it was trained.  Measuring token usage instead of $ revenue provides a clearer picture of AI demand.

As many open models increase in capability, the foundation model providers are locked in a struggle to differentiate their offerings while simultaneously making it easier to build AI into everything, and fend off competitors seeking the same work.  We’re already seeing them invest across the board in consumer applications, enterprise platforms, development tools, partnerships, marketing, commitment-based pricing and more.

One area that remains wide open is non-text modalities like audio and video, specifically when needed in real-time such as for conversational interaction.  This is a massive opportunity that, so far, is owned by OpenAI.  Unlike text, these modalities replace human time second-for-second, making ROI instantly clear when it works. You can skim a 10-page document in a moment but you have to talk to a voice representative in real time. Another way they’ll do it is to make sure the tokens are used in more applications. OpenAI simplified the process of integrating their platform into many software “surfaces”. They provided more of the plumbing needed to deliver their tokens so that adoption will rise. Microsoft is doing this also, as are a number of third parties. – Mike Finley, Co-Founder, StellarIQ

Cost breaks down as barrier for building great AI models — Until recently, it took tons of money to build high-performance AI models, and hefty budgets to run inference on them. But DeepSeek R1 turned that paradigm upside down when it matched the performance of top-tier models at fractions of both training and inference cost. In November, Kimi K2 Thinking topped the benchmarks on Humanity’s Last Exam and BrowserComp, with a reported training cost of only $4.5 million (ChatGPT training runs are estimated at $500 million) and inference cost of $2.50 per million output tokens, one sixth the price of Claude Sonnet 4.5. We’ll see this trend intensify in 2026 as more low-cost, high-performance models take off, many of which have open weights.

This completely changes the economics of who gets to play in this space. Suddenly, you don’t need to be a mega-corporation with unlimited capital to build capable AI systems. That shift matters more than people realize because it opens the door for specialized solutions that solve specific, real-world problems instead of trying to be everything to everyone. As more players enter the market with tailored, cost-effective solutions, the competition will push innovation further, enabling industries that were previously out of reach to access powerful AI technology. Open-weight, state of the art models can be run in private clouds, providing the security that CISOs demand for highly regulated industries. This democratization of AI will accelerate the pace at which businesses can address unique challenges and create impactful solutions. – Shanti Greene, Head of Data Science and AI Innovation, AnswerRocket

Effective AI is going to hinge on the trusted data underneath – In 2026, ‘explainable AI’ is going to mean ‘explainable data’. Regulators won’t just ask what a model did, they’ll ask which data made it behave that way and who changed that data last. And as AI becomes embedded in decision-making, the C-level are going to be demanding more explainability. The ability to trace AI inputs and outputs across data pipelines is going to define trustworthy AI. Lineage will therefore become the new audit trail for AI ethics, accountability, regulatory assurance etc. – Philip Dutton, CEO of Solidatus

Why Identity Intelligence Will Separate Market Leaders from Breach Headlines – In 2026, identity will either be your company’s strongest differentiator, or its weakest link. We’re entering an era where AI is both transforming business and transforming fraud. The cost is not just revenue loss, but long-term reputational damage, regulatory exposure, and a complete erosion of customer trust. Many companies are still relying on outdated verification methods such as static data, passwords, and fragmented KYC checks, while attackers are using tools that didn’t exist two years ago. This asymmetry will define the winners and laggards in the next phase of digital business.

Identity verification must become continuous, adaptive, and anticipatory, predicting and preventing risk before it occurs while remaining nearly invisible to the end user. It represents the evolution from a point-in-time identity check to a continuous, connected understanding of who someone truly is.

Identity intelligence brings together data across identity, historical, behavior, and risk checks to build a dynamic view of a user over time. Instead of verifying once and hoping for the best, organizations can continuously assess trust in the background, adapting to new signals as they emerge. Because when fraud happens, customers don’t blame the criminal, they blame the brand. The leaders who understand that digital trust and identity intelligence form the foundation of a modern business model, not just a security protocol, will be the ones who scale safely, expand globally, and protect their reputation. – Robert Prigge, CEO, Jumio

AI adoption is redefining cybersecurity risk, yet the ultimate opportunity is for defenders. While attackers utilize AI to scale and accelerate threats across a hybrid workforce, where autonomous agents outnumber humans by 82:1, defenders must counter that speed with intelligent defense. This necessitates a fundamental shift from a reactive blocker to a proactive enabler that actively manages AI-driven risk while fueling enterprise innovation. – Wendi Whitmore, Chief Security Intelligence Officer at Palo Alto Networks

AI coding agents will amplify identity misconfigurations – Coding agents will accelerate development, but also generate identity misconfigurations at scale. Hard-coded credentials, mis-scoped tokens, over-privileged service accounts, and flawed entitlement mappings will propagate through IaC and DevOps pipelines, creating systemic identity debt. – Ido Shlomo, CTO & Co-Founder, Token Security