Last year, many of us were introduced to agentic AI. In recent articles, I shared 50+ AI agents from top-tier SaaS and security companies, as well as another 30 from growing startups.
AI is reshaping business, but it isn’t transformational yet. In the 50+ expert predictions I captured for this article, you’ll see how AI impacts CX, DevOps, and C-level leadership, which will help drive the transformation. Other predictions on AI, data governance, security, and IT Ops outline what businesses must prioritize to enable compliant, safe, and ethical AI.
Thanks to all the experts that shared predictions from Algolia, AnswerRocket, Aquant, ASAPP, Automation Anywhere, Boomi, BQP, CallRail, Camunda, Cato, Certinia, Cloudera, CloudBolt, Commvault, Cozmo AI, Cribl, Deskpro, Empromptu, EY, Flowable, iBwave Solutions, Imply, Lasso, Nasuni, New Relic, NMI, Object First, Observe, OneTrust, Percona, PolyAI, Precisely, Redgate, Rocket Software, Schellman, Selector, Sendbird, Sentra, Sisense, Spines, Teradata, Thrive, Tines, UiPath, Vectra AI, and Vena Solutions.
The predictions include AI Agents and Orchestration (9), CX, Customer Support, Content Generation, and Creativity (7), LLMs and AI Models (3), AI and Data Governance (9), AI and Information Security (5), AI Code Generation and DevOps (3), Data Management and Visualization (6) AI IT Operations and Infrastructure (6), and Leadership (5).
Contributions came from 15 CEOs and 10 CIO/CTOs.
My take: Many of these are pragmatic takes on what to expect in 2026. But look out for voice AI, growing adoption of agent-to-agent orchestrations, and geospatial AI models as potential innovation disruptors. Trust, accountability, and intent protection are key themes in AI governance and security.
My prediction: GenAI hasn’t had an Uber / Airbnb moment. Yet. And it likely will in 2026, forcing more companies to look beyond productivity and efficiency improvements to how genAI and AI agents will transform the customer experience.
AI Agents and Orchestration
From NetOps to DevOps to VibeOps – John Capobianco, head of developer relations at Selector, “Operations teams have progressed from NetOps to DevOps to NetDevOps. Today’s AIOps era is starting to shift toward VibeOps, where digital coworkers participate in daily operations. These teammates will reason, act, and operate with agency through toolchains connected by a common protocol. The emerging Model Context Protocol (MCP) is becoming the USB-C of software, enabling autonomous agents to plug into a vast, powerful ecosystem of tools. ”
AI Agent Performance Dashboards – Vidya Shankaran, field CTO at Commvault, “By 2026, I would expect every CIO and CEO to have a dashboard that says, ‘How many agents are working for us today — and how many are working against us?’ That’s when we’ll know who truly built the future. To elaborate, many companies will be running so many autonomous or semi-autonomous AI systems — procurement bots, pricing agents, recommendation engines, threat detectors, RPA scripts, etc.”
AI Becomes a Skill Equalizer – Assaf Melochna, co-founder and president, Aquant, “In 2026, AI will act as a skill equalizer, rapidly narrowing the gap between novice and expert performance. Intelligent copilots, voice-driven guidance, and conversational RAC systems will provide real-time coaching, troubleshooting guidance, and step-by-step task support. Because AI will operationalize institutional knowledge, organizations will begin hiring based on learning agility, communication skills, and digital fluency rather than decades of hands-on experience. This shift represents one of the most consequential and least discussed impacts of AI: redefining what expertise looks like and who has access to it.”
Voice AI Becomes the Norm – Andy Sweet, VP of enterprise AI solutions, AnswerRocket, “While individuals and enterprises primarily engage with AI and AI agents through written text today, we’re going to see voice become dominant in the coming year. Early adopters are already having literal conversations with AI, often to support personal productivity when a written conversation is not feasible. This emerging method of AI communication will also drive innovation in the enterprise.”
Omnichannel Voice AI Experiences – Jin Ku, CTO, Sendbird, “Voice will replace text as the primary interface for AI agents, with native voice-to-voice models eliminating the latency bottleneck that’s held back conversational AI for years. Right now, most voice AI agents use a clunky three-step process: speech-to-text, LLM processing, then text-to-speech. This creates latency issues that break the natural flow of real-life conversation. Soon, voice-to-voice models will become mainstream and be able to process audio input directly to audio output, without the text intermediaries. This will be more than a performance upgrade; it will unlock a truly seamless omnichannel experience where customers can start on the phone, switch to SMS, then get email updates – all while the AI maintains full context.”
Human-Agent Teams Will Standardize Processes – Raju Malhotra, chief product and technology officer, Certinia, “Next year, we will see a massive increase in hybrid human-agent teams, allowing businesses to take on more new business and accelerate their delivery. The biggest impact will be on high-volume, standardized processes that today consume precious time, such as project planning, resource allocation, contract compliance, time and expense capture, and reconciliations.
MCP and Agent-to-Agent Goed Maintream – Kyle Campos, CTPO, CloudBolt, “In 2026, we’ll see a surge in Model Context Protocol (MCP) adoption, cross-agent communication, and effective multi-agent systems. This will be one of several factors pushing enterprises to formalize corporate AI strategies, moving usage out of individual productivity lanes and setting clear organizational requirements. Vendors will be expected to align with these strategies, with RFPs explicitly requiring interoperability and MCP compliance.”
AI Innovations are Driven by Human Judgment – Eoin Hinchy, CEO, Tines, “The next wave of AI innovation will be defined by agents that act before they’re asked, but the real differentiator will be how effectively humans stay in the loop. These systems won’t wait for prompts; they’ll monitor markets, compliance landscapes, and customer signals in real time, surfacing insights and taking action autonomously. Yet human judgment remains critical, providing the context, ethics, and nuance that AI cannot replicate. As organizations scale, they must design systems where oversight is built in, not bolted on, with clear frameworks defining when and how people step in and remain accountable for outcomes. The companies that get this balance right, where humans and machines operate in true tandem, will build the trust and integrity needed to stay ahead of the curve.”
Multi-agent Orchestration Gets Prioritized – Jakob Freund, CEO, Camunda, “In 2026, enterprise agentic automation (EAA) will bridge the gap between the vision and reality of agentic AI by enabling organizations to scale beyond isolated pilots and safely automate complex, exception-heavy, or cognitive work through dynamic AI, deterministic guardrails, and human-in-the-loop checkpoints. By unifying policy and technology to govern agents, humans, and systems across end-to-end processes, EAA delivers trusted autonomy, balances control with oversight, reduces risk and technical debt, and helps organizations achieve meaningful business outcomes and real ROI.”
CX, Customer Support, Content Generation, and Creativity
AI Uplifts Customer Journeys – Nate Barad, VP of product marketing, Algolia, “More organizations will lead with the customer experience, not the gen AI model. Organizations are moving away from generic AI implementations and instead adopting AI for specific functions, such as improving relevance or optimizing website infrastructure for AI agents. Successful early adopters start with a specific journey and explore the right mix of traditional and modern tactics.”
Consumers Will Expect AIs That Remember Everything – Shanea Leven, co-founder and CEO, Empromptu, “User experience shifts from prompt-based to persistent. AI stops asking you to repeat yourself and starts anticipating what you need. Persistent memory, anticipatory UI, individualized workflows, and AIs become the OS layer of all software. The chat box dies in 2026.”
AI-Powered CX Will Drive Transformation – Priya Vijayarajendran, CEO, ASAPP, “In 2026, the biggest shifts in customer experience will not come from adding more AI tools. They will come from transforming how organizations operate by redesigning processes, roles, and workflows with AI at the foundation of the customer journey. Over the next few years, the winners in AI-powered CX will be the organizations that treat AI as a true business transformation engine. They will tie every deployment to measurable impact, retain ownership of their data, and competitors by learning and adapting faster.”
Workslop Operational Risks Becomes a Focal Point – Thomas Kinsella, CCO, Tines, “As AI-generated content flooded internal workflows this year, ‘workslop’ emerged as the shorthand for low-quality and inconsistent outputs. In 2026, businesses will see workslop as more than a buzzy term, but a real operational risk. Poorly validated AI content will show up in performance reviews, financial summaries, customer communications, and decision-support workflows, creating serious governance challenges. Leading enterprises will respond by enforcing structured formats, applying validation layers, requiring grounded data, and using human review where judgment is essential.”
Regulations Will Require AI-Enabled Customer Support in Private Environments – Brad Murdoch, CEO, Deskpro, “With help desk and CX SaaS offerings using public-cloud hosted foundation model services to power their AI capabilities, industries with strict security, compliance, or data sovereignty concerns have been understandably slow in adoption. The recent industry survey, State of AI in Support Operations: Balancing Innovation and Compliance, finds that 64% of respondents in regulated sectors said they would only deploy AI if they could retain full control over their data and infrastructure. In 2026, help desk and CX platforms will add the ability to support AI in private and sovereign environments, facilitating the increased adoption of AI for customer service in regulated industries.”
Demand Will Drive Dynamic Case Management – Agim Emruli, CEO, Flowable, “For most workflows, automation was never designed to adapt to unpredictable business environments. In 2026, we’ll see static automated workflows start to fall apart under the weight of real-world complexity. As customer demands and regulatory challenges grow, IT leaders will seek flexible frameworks that can dynamically automate messy, multifaceted processes. Dynamic case management will rise to meet the challenge, providing the adaptability that today’s rigid systems lack.
AI Book Publishing – Yehuda Niv, CEO and co-founder of Spines, “AI isn’t changing how books are written. It’s changing who gets to publish them. Book publishing in 2026 will be defined by accessibility, where creative voices once sidelined by cost or complexity finally get a seat at the table. AI will democratize opportunity by turning publishing into a truly open, global stage for human creativity.”
LLMs and AI Models
Beyond LLMs; Onto Knowledge Models – Louis Landry, CTO, Teradata, “The next wave of AI isn’t just about language models, it’s about knowledge models, the stuff that actually maps how the world works. Some say graphs are the future of data. But they’re not the whole story; no single tech ever is. The teams that win in 2026 will be the ones that mix graphs, agents, and every way of representing knowledge to actually understand context, human intent, and all the messy edge cases no single tool can handle.”
Smaller, Smarter AI Models Will Power Enterprise Innovation – Ed Macosky, chief product and technology officer, Boomi, “In 2026, enterprises will shift away from large, general-purpose models toward smaller, specialized systems trained on their own data. These Small Language Models (SLMs) will deliver more accuracy, control, and efficiency, proving the point that bigger isn’t always better. Companies will start building networks of narrow models, each designed to excel in a specific area, from HR to supply chain to customer support. The real advantage will come from how well these systems work together. Training and maintaining proprietary SLMs will also become a key competitive edge for companies. Those who invest early in grounding AI on their unique data and workflows will create models that are not just tools, but strategic assets that reflect how their business truly runs.”
AI Models Include Geospatial Data – Tendü Yogurtçu, PhD, CTO, Precisely, “Geospatial data will play an increasingly critical role in AI training, shaping how systems perceive, interpret, and interact with the world around them. Geospatial intelligence, including satellite imagery, GPS coordinates, and other location-based insights, introduces a new dimension of context, filling information gaps when data is incomplete and offering a more objective, complete, and verifiable view of real-world conditions. When combined with an organization’s own proprietary data, geospatial data creates a powerful foundation for differentiated insights and lasting competitive advantage.”
AI and Data Governance
Data Governance as an AI Value Driver – Hugh Cumming, CTO, Vena Solutions, “The limiting factor for GenAI success won’t be determined by the quantity of available data, but whether the data is clean, connected, and governed with an effective policy. Leaders who focus on data quality and data availability will see faster progress than those trying to build AI value on top of fragmented data siloes.”
AI Accountability is the New Target – Manasi Vartak, chief AI architect at Cloudera, “As AI systems become increasingly complex, responsible AI must address not only model bias and fairness but also end-to-end accountability, encompassing data handling and system behavior. Enterprises adopting agentic AI will need to implement stronger governance frameworks, much like security and compliance reviews in traditional software procurement.”
AI Governance Shifts to Accountability-in-the-Loop – DV Lamba, CTO, OneTrust, “Governance isn’t a checkpoint anymore; it’s a circuit breaker built into the pipeline. In 2026, accountability-in-the-loop will be the standard for high-risk AI, making approvals and audit trails as integral as code commits.”
Agentic Command Centers Enable Oversight – Raghu Malpani, CTO, UiPath, “Organizations’ adoption of AI in all its forms has advanced faster than their ability to govern, manage, and orchestrate it. The need for stronger oversight is only increasing as organizations deploy more agents and give them access to a wider range of core processes, demanding continuous, embedded visibility and real-time control. A new approach is emerging to meet these challenges: the establishment of an operational layer that centralizes and integrates governance, control, and orchestration – an agentic command center.”
Explosive Growth in Shadow AI – Nic Benders, chief technical strategist, New Relic, “How many un-approved AI tools are running in your environment? Spoiler: It’s going to be a lot. In 2026, tech leaders need to find an acceptable middle ground with their internal users. Allow some tools, block others, and get visibility into the whole thing. And be prepared for that list to change throughout the year.”
Demand for Automated discovery, classification, and auditability – Yair Cohen, co-founder and VP product, Sentra, “Going into 2026, the biggest shift in data security is protecting where data sits to governing how it moves and how AI systems interact with it. Organizations need accurate tagging of sensitive data, strict least-privilege access, and continuous oversight. Regulators are signaling that automated discovery, classification, and auditability will become essential. Leaders in 2026 will treat governance as an engineering practice by embedding classification and tagging, along with access rules, directly into data pipelines, warehouses, and AI workflows. DSPM platforms that understand data in context and enforce policy automatically will define the market because protecting AI era environments requires visibility that never stops.”
Trust, not Perfection, Will Define the Next Phase of AI – Bennie Grant, COO, Percona, “The industry will stop chasing ‘bulletproof’ AI. Just as organizations have adapted to occasional cloud outages, they’ll recognize that imperfect answers are an inherent part of generative systems – not fatal flaws, but challenges to be managed. The focus will shift from eradicating every error to designing AI that earns trust through transparency, accountability, and resilience. The leaders of this next phase won’t be those who promise perfection, but those who build systems that users can rely on, even when they’re imperfect.”
Increase in AI Model Audits – Avani Desai, CEO, Schellman, “Sectors under the most scrutiny, financial services, healthcare, and critical infrastructure, are leading in adopting AI model audits, cloud risk management, and sustainability verification. At the same time, major technology providers are setting the pace for AI governance standards such as ISO 42001. Large SaaS and IaaS platforms, along with frontier model developers, understand that independent validation of their AI governance is not just compliance; it’s a competitive advantage in earning market trust.”
AI Ethics Shifts To Preventing Harm and Improving Life – Nikola Mrskic, CEO and co-founder, PolyAI, “The idea that disclosure equals trust is a leftover from early AI anxiety. In the next year, the most trusted AI systems will be the ones that disappear into the experience. The real ethics debate will shift away from performative caution and toward practical governance that prevents harm. The future of AI ethics isn’t about labels and disclosures, but about proving that what we build actually makes life better.”
AI and Information Security
Emergence of End-to-End Autonomous AI Attacks – Sohrob Kazerounian, distinguished AI researcher, Vectra AI, “Attackers are still not at the point where they will trust AI to run end-to-end autonomous attacks in critical scenarios, but that doesn’t mean it isn’t actively being explored. End-to-end attacks will begin to occur, though most high-profile hacks will only make use of LLMs in highly guard-railed scenarios in order to prevent detection. Remember: Attackers are early adopters! And they aren’t restricted by legal departments concerned with Intellectual Property (IP), Personally Identifiable Information (PII), and data governance. They are exploring the uses of AI in their operations, at a rapid scale.”
Shift From Data Security to Intent Protection – Elad Schulman, CEO and co-founder, Lasso, “As autonomous AI agents become pervasive in enterprise, the primary security concern will shift from data protection to intent security in 2026, ensuring AI systems act according to organizational goals and policies. Intent security will become the core discipline of AI risk management, replacing traditional data-centric security as the primary line of defense. Organizations that fail to monitor and align AI intent will face operational, reputational, and strategic risks at speeds far beyond what conventional cybersecurity can mitigate.”
AI Drives Absolute Immutability, Zero Trust Data Protection – David Bennett, CEO, Object First, “As AI becomes more integrated into business operations, it sometimes brings unexpected outcomes, such as the Replit incident, where an AI assistant deleted an entire production database. In 2026, organizations will further tighten administrative controls and prioritize backup storage solutions that offer Zero Trust and Absolute Immutability. This combination ensures that backup data is fully protected from the moment it’s created, eliminating the risk of accidental or malicious deletion – even by privileged users or AI tools.”
Growth in AI Browsers Will Require New Defenses – Etay Maor, chief security strategist, Cato, “AI browsers will move fully into an ask-and-act model, changing how people interact with the web. These AI browsers can complete forms, call APIs, and take actions while keeping user context. This convenience introduces new security risks, as malicious pages or hidden content can trick an AI browser into leaking data or performing unauthorized actions instantly. To protect users in this era of AI browsing, defenses must protect their personal data.”
Growth in quantum-aware security frameworks – Rut Lineswala, founder and CTO of BQP, “Security teams will discover in 2026 that their existing playbooks don’t account for quantum software already running in mission-critical environments – quantum-inspired algorithms integrated seamlessly into MATLAB, Python, and standard engineering workflows without SecOps realizing fundamentally different computational methods are in play. The organizations building quantum-aware security frameworks now, asking where computation happens and how results are validated, will have a significant advantage as adoption accelerates while others scramble to retrofit security onto systems already deemed mission-critical.”
AI Code Generation and DevOps
Agentic AI Will Evolve Into Real-Time Self-Generating Intelligence – Dustin Snell, SVP of agentic solutions, Automation Anywhere, “Next year, agentic AI will shift away from static chatbot experiences and begin functioning as real-time, auto-generated intelligence software. Systems will dynamically create user interfaces, adapt workflows to changing intent, and generate structured, context-aware flows on the fly. Enterprises will increasingly look for AI that includes governance, guardrails, and oversight. This is the next stage of Agentic AI: software that not only understands what a user wants but instantly builds the tools, screens, and logic required to get it done.”
AI improves product, design, and dev collaboration – Kristin Marsicano, VP of engineering, CallRail, “Over the past year, many AI-enablement efforts in engineering have focused on code generation. Writing code is rarely the bottleneck; the real opportunity lies in improving how teams make decisions, define requirements, review code, and ensure quality. As AI continues to mature, the biggest gains will come from how it reshapes collaboration across the entire software development lifecycle. This coming year, I expect to see more teams blurring the lines between product, design, and engineering, reducing friction in handoffs and enabling people to contribute across functions in ways that weren’t practical before.”
Avoid Overreliance on AI Workers – Sohrob Kazerounian, distinguished AI Researcher at Vectra AI, “As organizations rush to bring AI into every aspect of labor-intensive work, calls to understand and implement solutions more carefully and responsibly will be drowned out by short-term efficiency gains. The near-term cost savings of using GenAI over human programmers outweigh the potential long-term risks that may arise from overreliance on AI workers. Over time, code will become increasingly difficult to understand or debug by anything other than an LLM, and organizations will not be prepared to respond to challenges that result from short-term thinking.”
Data Management and Visualization
Transition to Data Lakes – Jacob Leverich, CTO and co-founder, Observe, “In 2026, the biggest bottleneck to enterprise AI won’t be model quality, but fragmented data. Companies still can’t unify the operational, observability, and business data needed for AI to understand how machines, people, and external factors interact. Expect a rapid shift toward data lakes that support open data formats, such as Apache Iceberg, as they become the default for centralizing and governing data at scale.
Unlock Unstructured Data for Competitive Advantages – Andres Rodriguez, founder and CTO at Nasuni, “The era of AI is presenting enormous challenges and opportunities: Unstructured data is perhaps the biggest frontier for enterprises. It is vast, dispersed across multiple silos, and difficult to access and manage efficiently. Businesses that can unlock the full value of their unstructured data repositories and gather insights from that data are the ones that will gain the biggest competitive advantage in 2026.”
Infrastructure May Be Hot, But Data Will Only Accrue in Value – Josh Rogers, CEO, Precisely, “Companies are pouring billions into AI infrastructure to meet the capacity demands of the AI moment. But we’re only just starting to see some of these same companies think about the data that will sit in that infrastructure. In 2025, we saw several high-profile acquisitions of data players, as top enterprises look for competitive differentiation. 2026 will further ignite the data industry consolidation, as the organizations that have invested in major infrastructure projects look to fill those data centers with high-quality, context-rich data to fuel their AI.”
Conversational Decision-Making Overtakes Dashboards – Yigal Edery, SVP of Product & Strategy, Sisense, “In 2026, business decisions shift from dashboards to dialogue. Leaders will ask questions, validate answers, and act from within conversational interfaces — all powered by governed data. Developers benefit too, building analytics-powered apps with far less data expertise.”
Data scientists shift to driving use cases, not on the technical steps – Artur Borycki, VP of AI research and development, Teradata, “By 2026, the 80/20 grind of data preparation will no longer define a ‘data scientist.’ We are entering an era where the real demand is for scientists who focus on understanding and driving use cases, not on the technical steps it takes to get there. Success will favor those who can generate ideas, test and verify multiple hypotheses, and translate insights across domains from genomics and bioinformatics to physics, nuclear science, to supply chains. The future of the role belongs to those who can think creatively with data, connect patterns and possibilities that others might miss, and turn raw information into actionable insights that drive real impact.”
PostgreSQL Demand Grows – Grant Fritchey, product advocate at Redgate, “The one prediction I can make with great confidence is that PostgreSQL adoption is going to continue to grow at a rapid pace. While I truly don’t think anything can slow it down, it will face headwinds as the challenges of learning Postgres are confronted by more and more people, and the community that supports this great project may not be completely ready for the oncoming volume.”
AI IT Operations and Infrastructure
AI Will Require Autonomous CloudOps – Mary Elizabeth Porray, EY global vice chair of client technology and COO, Growth and Innovation, “As we head into 2026, AI is transforming how cloud environments operate on a daily basis. Teams are now supervising, guiding, and orchestrating systems that can manage many processes autonomously, rather than manually navigating tools. Importantly, humans remain at the center of this transformation, and though their roles will be redefined, they won’t be diminished. The organizations that will lead in this next phase are those that continuously measure what’s working, refine workload placement based on evidence rather than trends, and build adaptable cloud foundations that can evolve as quickly as their AI ambitions.”
AI Guided Traffic Routing – Nuha Hashem, CTO and co-founder, Cozmo AI, “In 2026, the real value of GenAI will show up in how systems handle production traffic and the varied conditions that shape enterprise work. Leaders will look for proof that a system can take in the internal context of the business rather than rely on fixed scripts. That level of behavior will guide the direction of customer work and the internal operations that depend on it.”
Observability Warehouses Scale Operations – Eric Tschetter, chief architect at Imply, In 2026, the era of the all-in-one observability black box will be over as AI drives massive amounts of logs, metrics, and traces. 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.”
Data Infrastructure Becomes the Production AI Bottleneck – Nick Heudecker, VP market strategy and corporate development, Cribl, “By 2027, 90% of production AI deployments will fail due to unsolvable scaling challenges at the data tier. While AI developers grapple with the blocking and tackling of data management for their early prototypes, an insurmountable problem awaits them on the other side of the AI deployment lifecycle: production-class workloads. As enterprises move from the prototype phase into production, they run a significant risk of systemic failures — not because of the LLMs, but because of the inadequate data infrastructure used to train and run them.”
Boom in Wireless Infrastructure to Support AI – Kelly Burroughs, director of strategy and market development at iBwave Solutions, “Enterprises are investing in the wireless infrastructure needed to support AI, automation, and data-intensive operations. Rising uplink demand and constant mobility mean designers must think about how to maximize success across indoor and outdoor environments. Enterprises that anticipate these requirements and strengthen their foundational wireless infrastructure today will be able to adopt today’s existing automation and AI capabilities and be ready to scale for next-generation capabilities when they arrive.”
AI on Smaller Devices Emerge – Mike Finley, co-founder, StellarIQ, Answer Rocket, “Next year, we’ll see booming use of AI on smaller devices, demanding far lower power than AI on larger form factors. Ultimately, this trend will drive a tidal wave of device replacement over the next 5 years – everything from TVs to thermostats.”
Leadership
Purposeful Communications Drive Adoption – Bill McLaughlin, CEO of Thrive, “In 2026, clear and purposeful communication will be key to business success, and it can’t be siloed to just the leadership team. Employees are far more likely to adopt new technologies and policies when they understand the “why.” When leaders take the time to explain how an initiative directly helps employees perform their jobs more efficiently and effectively, whether by reducing manual work, improving collaboration, or freeing up time for higher-value tasks, the message resonates. If leaders can share their reasoning and demonstrate that they are not simply chasing the hype, but rather driving meaningful improvements in how the business operates, employees will be more likely to get on board and become champions of change.”
CTOs Evaluated on Delivering Strategic Vision – Phillip Goericke, CTO, NMI, “The role of the CTO has changed dramatically. As innovation becomes the connective tissue between execution, strategy, and revenue, CTOs are shifting from behind-the-scenes technologists to front-line business leaders. As a result, we’ll see more companies recognize and deploy CTOs as critical decision-makers driving growth and transformation. These CTOs won’t be measured by velocity, as they were in the past. Instead of defending how many features they shipped or how fast they delivered, they’ll now be judged on how effectively they align product, go-to-market, and business strategy around a shared vision.”
Top CIOs Will Clean Up Their AI Debt – Ha Hoang, CIO, Commvault, “Just like technical debt, many organizations will confront ‘AI debt,’ scattered, redundant, and ungoverned models created in silos. 2026 will be the year CIOs focus on rationalizing and centralizing their AI ecosystems. The AI gold rush is over, and now comes the cleanup– 2026 will separate organizations experimenting with AI from those actually operationalizing it with discipline, governance, and measurable impact.”
Leaders Target High Value, Not Experiments – Nikhil Mungel, Director, AI R&D, Cribl, “The companies that report real economic impact from AI will pick a few high-value business outcomes and put senior leaders in charge of delivering them with AI, instead of ‘trying AI’ in random pilots. McKinsey’s latest State of AI survey and BCG’s global study show only a small minority do this, and they are the ones seeing outsized financial gains.”
CISOs Become More Strategic – Cynthia Overby, director strategic security solutions, zCOE at Rocket Software, “CISOs become key drivers of business growth. CEOs must better understand the Chief Information Security Officer’s role and redefine it as a strategic decision-making role, including board-level reporting, to align security initiatives with business growth. This reframes cybersecurity from a defensive cost center to a business enabler that protects brand reputation and shareholder value.”
