Enterprise leaders predict 2026 will be the year AI moves from experimentation to integration as Agentic systems, governed data and accountable design reshape how software is built, trusted and used.
In 2026, how we interact with software will fundamentally change. We’re moving from static interfaces to Generative UIs – user experiences that are dynamically created by AI based on the user, context and data, not manually designed or coded.
At the same time, the relationship with AI agents will shift just as dramatically, as the direction of interaction is reversing. Today we instruct agents, but soon they’ll be more proactive, guiding end users, flagging what to focus on, how and when to act. As agents improve, especially in context, memory and reasoning, they will begin to remember every interaction, learning continuously from experience.
By 2027, people may delegate half of their daily work to these systems, but 2026 will be the proving ground, the year we perfect model reasoning, memory and cross-agent collaboration so that systems can think, remember and eventually work seamlessly with others across platforms. The next phase of AI isn’t just about bigger and better models but making work more collaborative between humans and machines.”
Pratima Arora, Chief Product Officer, Smartsheet
In 2026, the most successful products won’t be designed; they’ll be taught. Software will move from static interfaces to living systems that learn from their users. The next generation of enterprise tools will be co-created in real time, where people shape AI agents the way they once customised dashboards. That shift will redefine what ‘product design’ even means.
AI will stop being an add-on and start being accountable. AI FOMO has driven many organisations to rush to market with capabilities that miss the mark and customers are feeling fatigued. The real challenge for 2026 is building AI that earns trust. Enterprises will begin to judge AI tools by their transparency, governance and measurable business impact, not by their novelty. Responsible AI design will be the new product differentiator.
Lisa Spira, VP of Content Intelligence, Persado
In 2026, AI will upend how people discover information and decide what to buy. Systems like Google are now in direct competition with their own customers – enterprises – for attention. Marketers will need to pivot content creation approaches to meet their customers where they are instead of relying on traditional content funnels.
Generative AI has introduced a new audience dichotomy: those who trust it, those who don’t and those who may not realize the content they’re engaging with was created by AI at all. Each group represents a distinct opportunity and responsibility for enterprise marketers.
In 2026, the differentiator will be using Generative AI intelligently to reach each of these segments in a way that gains their trust. This can take the shape of transparency, where brands acknowledge and demonstrate how AI contributes to content creation to build trust and credibility. However, it can also mean explaining how AI interacts with data to make informed content decisions, emphasising how LLMs are a key component of a more complex decision-making ecosystem. For other audiences, when the key differentiator is producing the most relevant, on-brand content that meets their marketing KPIs, simplicity will be the best route.
Sohrob Kazerounian, Distinguished AI Researcher, Vectra AI
As organisations rush to bring AI into every aspect of labor-intensive work, calls to understand and implement solutions in a more careful and responsible manner will be drowned out by short-term efficiency gains. For example, one of the fastest growing application areas of GenAI is in the domain of programming, where new models, tools and workflows are constantly being invented and adopted. Despite being rather impressive, coding agents and AI largely generate bloated and inefficient code relative to more robust and elegant solutions of an even half-decent human developer. The near-term cost-savings of using GenAI over human programmers outweighs the potential long-term risks that may arise from over reliance on AI workers. Over time, code will become increasingly difficult to understand or debug by anything other than an LLM, and organisations will not be prepared to respond to challenges that result from short-term thinking.
Matt Belkin, President and General Manager, Data and Analytics, insightsoftware
Eighty-eight percent of AI pilots fail due to insufficient AI-ready data and by 2027, 40% of AI projects will be cancelled due to escalating costs. The deeper issue? AI hallucinations. When AI gives you different answers each time or can’t explain where it got its information from, that’s unacceptable in production environments. Leaders can’t defend decisions they can’t trust or verify. Today, data teams spend more time cleaning and managing permissions than delivering AI applications that drive business value.
In 2026, the AI winners won’t be the companies with the biggest models. Success will be defined by who cracked the code on giving AI systems governed, contextual access to enterprise data without hallucinations. That’s how data becomes a competitive advantage instead of digital dust.
Ram Mohan, Chief Strategy Officer, Identity Digital
Every brand wants to be an AI company, and owning a .ai domain has become the ultimate signal of that ambition. Just as legacy domains once defined legitimacy in the early Internet era, AI has become synonymous with innovation, intelligence and modern credibility. Creative domain extensions are not just labels; they are the architecture of digital identity.
Manny Rivelo, CEO, ConnectWise
As we head into 2026, we’ll see the managed services and small business ecosystem move from talking about AI to truly applying it. Currently, most organisations are experimenting – dabbling in one or two use cases that simplify operations or enhance customer experience. However, the real breakthrough will come when vendors make AI accessible: packaging solutions in ways that don’t require deep technical expertise. MSPs and SMBs are facing a talent crunch and expecting them to hire specialised AI roles overnight isn’t realistic. Instead, success will hinge on simplicity – delivering clear value without complexity.
In the year ahead, there will be a major shift toward ‘AI in a box’ solutions: ready-to-adopt tools that help SMBs automate repetitive tasks, strengthen cybersecurity posture and gain actionable insights without needing to reinvent their operations. The partners and service providers who embrace this mindset early – focusing on practical, consumable AI rather than abstract innovation – will define the next wave of business growth. The opportunity is massive, but only if we make AI understandable, attainable and valuable for every business, not just the largest ones.
Bryan Olshock, CMO, ServiceTitan
AI has moved from experimentation to execution. In 2026, its impact on marketing will hinge on two things: proving ROI and scaling automation. AI ROI will decide what survives and automation will decide who leads.
At ServiceTitan, we built automation into the foundation of how we operate. A dedicated AI leader now drives our roadmap and workflow design. In our first AI-powered campaign, we cut launch times by 80%. Automation at scale is the new marketing muscle – faster launches, smarter spend and execution that runs itself.
AI is no longer a tool; it’s the operating system for growth. The CMOs who systemise success and scale automation across teams will define the next era of performance marketing.
For commercial and residential skilled trades, the real opportunity lies in AI’s ability to automate and adapt in real time. By taking over processes that once required hours of manual work, like adjusting budgets, throttling marketing spend, dispatching the right tech for the right job and sending perfectly timed follow-ups, AI is turning ‘automatic ROI’ into reality. The result: faster growth, higher profit, happier customers and businesses that run themselves.
Ed Frederici, CTO, Appfire
AI adoption is moving from experimentation to execution. The past few years of rapid testing created progress, but also confusion and uneven results. The next stage will focus on clarity, control and measurable value. The organisations that lead will embed AI into daily operations rather than treat it as a separate initiative.
They will align it with existing systems, ensure that it works with reliable data and maintain oversight at every stage. AI will be managed as part of business infrastructure, with the same expectations for accuracy, security and performance as any other critical system. This shift will also change how people make decisions. AI will help teams process information, find patterns and validate assumptions. It will not replace human judgment but will improve how decisions are made. The companies that succeed will emphasize transparency, documentation and review so that every AI-driven outcome can be understood and verified.
Progress in 2026 will depend on discipline. Organisations that apply structure, maintain data quality and hold AI to the same standards as other business systems will see lasting results. Those that combine automation with human accountability will create stronger, more consistent decision-making across the enterprise.”
Keerti Melkote, CEO, Anyscale
Every major technology shift has required a new computing foundation. Virtualisation and cloud computing each defined their eras. Now, AI is forcing a rethink of the infrastructure that powers it. AI workloads are dynamic and complex, with compute patterns that span CPUs and GPUs across data processing, model training and inference.
In 2026, we’ll see the rise of AI-native computing – with an open-source stack built on PyTorch, Ray, vLLM and Kubernetes – that delivers the scale, flexibility and developer agility required to build and run AI in production. This transition will determine which companies will lead in the AI era and which will fall behind. Those that embrace AI-native computing will run more efficiently, innovate faster and turn AI into a true competitive advantage.
Dr. Joseph Nathan, Co-founder, President and Chief Medical Officer, ForSight Robotics
* The future of robotic funding and healthcare tech investment: Robotics funding has skyrocketed this year, already pushing 2025’s total to more than US$8.5B. With additional financing flowing into AI and the robotics sector, I expect to see more companies being funded that blend software and hardware development. Over the past few years, medical device robotics has quickly become an area of interest for VCs to invest in advancing robotic technology with the goal of transforming outdated healthcare systems and practices, including surgical treatments and device applications. In 2026, I predict funding will emphasize companies that have a strong data collection pipeline and a go-to-market strategy to ensure greater success down the line.
Tifenn Dano Kwan, CMO, Amplitude
In 2026, companies will no longer measure teams just by headcount and programme count. A new organisational metric – agent count – will emerge as AI becomes a core part of how marketing and every function operates. CMOs will be tasked to justify not only headcount and budget, but also which workflows will be enriched by AI agents and which can be fully delegated to AI. This will spark a fundamental shift in organisational design. By 2026 planning cycles, this kind of thinking will be table stakes, and by 2027, it will be the norm across every function. Teams that rethink their workflows will unlock time and contractor savings that can be redirected to new headcount or new AI agents. If done right, everyone wins – employees gain more time to focus on meaningful work while companies benefit from agility, productivity and cost-savings.
Rishi Kaushal, CIO, Entrust
In 2026, AI will become both the attacker and the defender – and trust will determine who wins. The most significant cyber-risks won’t just come from the speed of AI-enabled attacks, but from their realism. Generative AI is already being weaponised to create hyper-realistic deepfakes, synthetic identities and voice spoofs that can bypass authentication systems. These threats will intensify as executive impersonation and social engineering become nearly indistinguishable from real human interaction.
Maintaining transparency in AI-to-AI interactions will be critical to preserving trust and control. Ultimately, resilience in 2026 will depend on the trustworthiness of the AI itself and the authenticity of the identities behind every digital interaction.
We’re entering a new phase of AI adoption, one where companies must live with what they built during the hype cycle. The question is no longer ‘Can we build it?’ but ‘Can we trust it?’ The winners won’t be those with the most AI products, but those who make them reliable, observable and accountable. The late-2025 outages were a wake-up call, proof of how fragile and interconnected our systems have become. Leaders now have to find the right balance between automation and oversight.”
Emily Nakashima, VP of Engineering, Honeycomb
In 2026, the cracks from years of flattened org charts will start to show. Engineering teams need strong managers to navigate massive change, from driving AI adoption to rethinking security, but many have now underinvested in those capabilities. Most haven’t yet updated their practices for the ‘vibe coding’ era, and it will likely take a major AI-driven security incident to spark that reckoning. The teams that thrive will be those grounded in observability and other DevOps fundamentals, teams that can see clearly how systems behave, learn quickly from real signals and use that insight to turn automation into lasting progress.”
Elizabeth Maxson, CMO, Contentfu
* Brands will spend more time cleaning up ‘workslop’ than Creating: In 2026, the biggest marketing crisis won’t be AI misuse, it’ll be AI overuse. The number of companies with fully AI-led processes nearly doubled last year, and the pursuit of high-volume output has proven that more content doesn’t equal better content. This ‘AI workslop’-generic, low-value messaging – dilutes brand identity and audience trust. Even personalized campaigns risk falling flat if AI is used without intent, treating people as data points rather than real customers. Marketing teams should be clear about what role AI plays in their workflows. For some, AI will handle mundane tasks like translation. For others, AI will serve as a starting point for creative development. The key is understanding where the machines should stop and where the humans should take over. Brands that fail to be disciplined on roles and responsibilities will spend more time undoing AI workslop than creating business impact.
* 2026: The year AI moves from experimentation to integration: In 2025, marketing teams poured time and budget into AI, eager to understand its potential. In fact, 65% of CMOs made meaningful investments in AI tools. But 2026 will be the year of proof, not promise. Marketers will be expected to show tangible ROI: sharper personalisation, faster content creation and measurable growth. The difference will come down to clarity of purpose – those who define clear goals for what AI should solve, build strong data foundations and integrate AI across workflows will move beyond experimentation and into impact. Teams that treated AI as a side project will find themselves lagging behind.
Radha Basu, CEO and Co-founder, iMerit
Generative AI must build and continuously maintain trust if it is to solidify itself within the value chain and accelerate innovation in 2026. That trust is dependent on the effectiveness of developers and experts co-creating with AI. On the developer side, this trust is built and sustained through designing human-centric systems, while experts must develop AI further in terms of human partnerships versus replacements. The future of AI will be predicated on the human capacity to co-create effectively with machines, instead of purely technical breakthroughs. The organisations that prioritise this balance will define the next chapter of AI.
Arvind Purushotham, Head of Citi Ventures
* Engineers start to lean on their AI agents: While humans learn to accept that agents can independently create production-level code, they will be trusted for more mundane – but critical – tasks such as reviewing code, unit testing and documentation.
* AI-native business models outshine legacy businesses: AI-native start-ups will seek out legacy businesses as M&A targets across legal, accounting and compliance sectors, where new AI business models can more rapidly and more cost effectively deliver.
* Agentic commerce as autonomous personal shopping experience: Agents will increase their roles in the shopping journey, expanding beyond pure recommendations, to increasingly embed across discovery, curation, payments and post-purchase.
Munu Gandhi, President of IT Solutions, Xerox
We’re in the middle of an extraordinary global buildout of AI infrastructure. It’s a boom that, like every major technological wave, will eventually correct. But history shows that overcapacity often fuels the next great leap forward. As computing power becomes more abundant and accessible, it will unlock new opportunities for practical innovation, especially across the workplace. The differentiator won’t be how much capacity organisations have, but how intelligently and responsibly they use it to make work simpler, smarter and more productive. At Xerox, we see AI as a practical enabler, helping clients build secure, efficient systems tailored to their data, workflows and people. That’s how intelligent innovation becomes sustainable.
