University of Kwazulu-Natal

Ai In Enterprise Software 2026 Predictions From Outsystems

Bonisiwe Shabane
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ai in enterprise software 2026 predictions from outsystems

Part 2: As the year draws to a close and the new year looms, the industry takes out its collective crystal ball for a look at what 2026 has in store for AI. While today’s AI adoption often starts with generic LLMs and isolated prototypes, enterprises are realising that real value doesn’t come from the model alone – it comes from how well that model is connected... In 2026, the focus will move away from “building your own” models and towards deploying AI that natively integrates with internal assets: data sources, tools, APIs, operational workflows, and governance layers. Models and agents will increasingly use MCP-like connectors to enrich prompts with internal organisational context, retrieve real-time business data, and perform actions across existing enterprise systems. This shift turns AI from a static text generator into an operational participant – one that queries, validates, updates, and orchestrates tasks based on live internal information. As a result, companies will reduce drift, improve reliability, and unlock far faster time-to-value.

Instead of experimenting in isolation, enterprises will rely on integrated, governed, production-ready AI systems that understand their business, operate within their environment, and continuously stay aligned with their internal truth. The surge in advanced AI tools, such as Model Control Platforms (MCPs), is raising urgent questions for security teams: How do we build trust in AI, govern its adoption, and ensure secure integration? Governance will play a pivotal role, such as the EU AI Act, Cyber Resilience Act (CRA), DORA and various state regulations, such as California’s AI Transparency Act (SB 942), providing clear standards and accountability... OutSystems's CEO- Woodson Martin has met with more than 80 CIOs across banking, insurance, manufacturing, and other highly regulated industries. He found that these leaders are wrestling with integrating #agentic AI into systems where failure is not an option. And what they told him challenges a lot of the conventional wisdom.

In his article, he shared 10 predictions based on these conversations about how #AI is reshaping enterprise software in 2026. https://lnkd.in/gCifzYRi The most significant advances in artificial intelligence next year won’t come from building larger models but from making AI systems smarter, more collaborative, and more reliable. Breakthroughs in agent interoperability, self-verification, and memory will transform AI from isolated tools into integrated systems that can handle complex, multi-step workflows. Meanwhile, open-source foundation models will break the grip of AI giants and accelerate innovation. Here are six predictions for how AI capabilities will evolve in 2026.

By 2026, the power of foundation models will no longer be limited to a handful of companies. The biggest breakthroughs are now occurring in the post-training phase, where models are refined with specialized data. This shift will enable a wave of open-source models that can be customized and fine-tuned for specific applications. This democratization will allow nimble startups and researchers to create powerful, tailored AI solutions on a shared, open foundation—effectively breaking the monopoly and accelerating a new wave of distributed AI development. With improvements in foundation models slowing, the next frontier is agentic AI. In 2026, the focus will be on building intelligent, integrated systems that have capabilities such as context windows and human-like memory.

While new models with more parameters and better reasoning are valuable, models are still limited by their lack of working memory. Context windows and improved memory will drive the most innovation in agentic AI next year, by giving agents the persistent memory they need to learn from past actions and operate autonomously on complex, long-term... With these improvements, agents will move beyond the limitations of single interactions and provide continuous support. In 2026, the biggest obstacle to scaling AI agents—the build up of errors in multi-step workflows—will be solved by self-verification. Instead of relying on human oversight for every step, AI will be equipped with internal feedback loops, allowing them to autonomously verify the accuracy of their own work and correct mistakes. This shift to self-aware, “auto-judging” agents will allow for complex, multi-hop workflows that are both reliable and scalable, moving them from a promising concept to a viable enterprise solution.

by Jason Lemkin | Artificial Intelligence (AI), Blog Posts, SaaStr.Ai So we decided to do something fun this year and write up our 2026 predictions together with every single SaaStr speaker and AI post of the year! How? Every single SaaStr AI session, interview and speaker was ingested into Claude so we worked the Top 10 for 2026 up … together. Our Top 10 Predictions for B2B + AI for 2026: The Prediction: AI-native B2B companies will run sales teams that are 50% smaller than their predecessors while maintaining or increasing revenue.

Here are a few predictions for 2026 from: Ben Sekhon, RVP UKI at OutSystems, commented: 1. UK’s Hidden Edge: How Legacy Knowledge is key for 2026 – The UK has a lot of legacy enterprises who have been, historically, slow to adopt new technologies. However, we’ve seen them eagerly dive head-first into AI alongside the young startups. Fearmongering headlines may point to an AI bubble, but our on-the-ground feedback tells us the UK is going all in with AI mandates for 2026.

Old companies often mean old infrastructure; the number one priority for UK companies adopting AI in 2026 is app modernisation. Many existing systems are several decades old, patched together with countless upgrades. We no longer know how they operate and must therefore rely on AI to understand the system, rebuilding it to fit the modern architecture. However, legacy systems are also a largely untapped goldmine of institutional knowledge. They contain decades of built-in business rules and process logic; knowledge that no documentation library can fully capture. AI-powered systems and agents, deployed on the right kind of platform, can turn that dormant knowledge into a strategic asset.

As the dust settles on the hype of 2025, the AI landscape is shifting from "what’s possible" to "what works." We’ve seen the demos, we’ve played with the agents, and we’ve witnessed the rise... The market is maturing, capital is concentrating, and enterprises are demanding receipts. 1. The Great Capital Shift: Vertical AI Takes the Lion’s Share In 2026, we predict that 70% of venture capital deployed in AI will shift to Vertical AI. Investment will move away from commoditized horizontal LLMs and generic "wrapper" copilots in favor of "Systems of Action" built on proprietary data for specific, regulated industries.

HR As The Exception: Vertical AI will scale in operational workflows but will face a hard adoption ceiling at autonomous decision-making due to "bias reinforcement": the risk that AI simply automates and scales existing... The Takeaway: The era of "AI for everything" is over. The smart money is moving to "AI for this specific thing."

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