2025 marked a turning point for AI. After several years of pilots and proofs of concept, generative AI models became embedded in day-to-day enterprise operations, with agentic systems and long-term memory capabilities maturing in real-world environments.
This shift represents clear forward momentum for enterprise AI. However, concerns about a potential market correction and the AI bubble bursting are surfacing after a wave of heavy investment ahead of meaningful returns at scale. The past year laid bare both the potential and the limits of large generative AI models, pushing businesses to confront questions of precision, governance and reliability. As a result, today’s AI discourse has shifted towards how practical application of AI can deliver tangible value in real operational contexts.
The rise of proactive, initiative driven intelligence
Long-term memory is cemented within many of our AI systems already. As this capability becomes more advanced, AI will increasingly predict user needs instead of waiting to be prompted. Long-term memory provides AI with greater context and understanding of what a user is likely to need, or how a user is likely to act. This enables the transition from reactive to proactive decision support. OpenClaw is the latest example of this shift toward proactive agents that anticipate user needs and act on context, rather than simply responding to demands.
But as agency increases so does the potential for something to go wrong. Autonomous agents like OpenClaw introduce serious security risks, from unintended actions taken on a user’s behalf to new attack surfaces for data exposure. As AI begins to take initiative on behalf of users, workers will have to renegotiate how much autonomy and trust they extend to these systems. Increased awareness of potential for bias and privacy issues will become essential. Organisations will need to continuously retrain employees on how to collaborate with proactive systems, set boundaries and interpret AI-driven interventions, rather than relying on one-off onboarding sessions that quickly go out of date.
Embedded AI powers more decisions
Even before generative AI became a widely recognised concept, many AI-driven systems were already embedded in everyday services without people realising. For years, Netflix has had recommendation systems, thumbnail selection and content surfacing powered by machine learning. The video game FIFA has also had AI embedded for a long time, using the technology to control opponent behaviour and manage match difficulty.
Tools like ChatGPT brought AI back into the fore by making intelligence conversational, encouraging users to actively consult these systems. Despite this, we will see generative capabilities increasingly being woven into products, services and interfaces in ways that feel intuitive rather than attention grabbing. It will no longer be enough to simply bolt generative features onto products in the hope that they look innovative. The platforms that succeed will be those where AI operates quietly in the background, continuously improving the experience instead of drawing focus to itself.
Scale gives way to specialisation
Many of the toughest challenges in applied AI now revolve around trust, domain-specific knowledge, evaluation methods and integration into established workflows. Solving these problems increasingly depends on tailoring systems to specific datasets and operational environments. As a result, sector-specific and use-case-driven solutions will multiply, as organisations prioritise subject-matter expertise over general-purpose breadth. This focus on making AI genuinely useful in specific contexts matters. As Microsoft CEO Satya Nadella cautioned at Davos in January, AI risks failing to deliver broad economic and industry impact if its benefits do not translate into practical, widely adopted applications.
We are already seeing signs of this transition towards using AI for real-life use cases across industries. Anthropic’s launch of Claude for Life Sciences in October 2025 marked an initial step, with the system designed to help researchers accelerate discovery and, over time, enable AI to independently generate scientific breakthroughs. In January 2026, OpenAI introduced ChatGPT Health, a sandboxed area within ChatGPT intended to let users ask health-related questions in a more secure, personalised setting.
Rather than channelling investment solely into ever-larger general models, we expect leading AI players will increasingly back specialised systems. These purpose-built tools enhance accuracy, accelerate ROI, and align more naturally with regulatory expectations.
Measurable impact becomes the bar
As the sector moves past peak hype, AI is settling into a more disciplined phase. Funding and deployment decisions are shifting away from grand claims toward clear, measurable business outcomes, with organisations finally applying the same scrutiny to AI that they do to any other enterprise technology.
Although the underlying capabilities remain impressive, the novelty of interacting with machines is wearing off. The next year will be shaped less by flashy breakthroughs and more by thoughtful integration, where the most successful technology is the kind that fades into the background because it simply does its job.
Sarah Hoffman
Sarah Hoffman is Director of AI Thought Leadership at AlphaSense. With a career spanning two decades in AI, machine learning, natural language processing, and other technologies, Sarah’s expertise has been featured in The Wall Street Journal, CNBC, VentureBeat and on Bloomberg TV.
