With Artificial Intelligence (AI) impacting industries globally, 2025 is set to reveal unimagined dimensions of its impact, with global spending on AI solutions projected to hit $307 billion and expected to grow to $632 billion by 2028.
According to a report by PwC, AI is expected to contribute $15.7 trillion to the global economy by 2030, with nearly 70% of this coming from improvements in productivity.
However, while chatbots and generative AI help with public discourse, a quieter but huge shift is occurring in research labs, boardrooms, and behind the scenes in corporate sectors.
This change is being led by innovators like Mel Morris, one of the UK’s tech investors and the visionary behind Corpora.ai, an AI-powered research engine.
Morris predicts that the real breakthroughs in AI will not be in flashy consumer applications but in how organisations conduct research, solve complex problems, and reveal insights.
Here’s a deep dive into the six key trends impacting the next chapter of AI in research.
1. AI Will Transform Corporate Research
Research and development (R&D) is the backbone of innovation, yet inefficiencies cost businesses billions annually. In 2025, AI will begin to overhaul these processes, automating laborious tasks like data collection and analysis while offering great visibility into research outcomes.
Beyond efficiency, AI will challenge stagnant methodologies by identifying hidden patterns across large datasets. In breaking through cognitive biases and blind spots, companies can make discoveries that were previously unattainable.
For example, pharmaceutical companies may cut drug development timelines in half, while financial institutions could leverage AI to spot systemic risks that human analysts overlook. This will change ROI in research and enable innovations across multiple industries.
2. The Democratisation of Research
Traditionally, research has been the domain of specialists, requiring years of training to scale through complex methodologies. AI is set to level the playing field, allowing individuals without extensive expertise to conduct sophisticated research.
This accessibility will help students, entrepreneurs, and small businesses to engage in high-level research, driving innovation from unexpected quarters.
In academia, this could mean a shift from teaching methodologies to deeper engagement with subject matter. For industries, it predicts a future where innovation is no longer restricted by the cost or complexity of research processes.
3. The Evolution of AI Search
AI search engines are proliferating, but their potential is at risk of being limited by advertising-driven models. Mel Morris warns that these self-serving designs could introduce biases, much like traditional search engines, limiting their transformative capabilities.
As businesses and researchers rely on AI search for high-level insights, the challenge will be to create unbiased, transparent platforms that prioritise discovery over profit. This calls for a rethinking of AI search technology, ensuring it remains a tool for enlightenment rather than a conduit for commercial agendas.
4. The Quiet AI Revolution
While generative AI like ChatGPT captures public attention, the real value of AI is emerging in less visible applications. Legal services, venture capital, and government agencies are leveraging AI to optimise resource-intensive processes.
These implementations may not make headlines, but their impact is huge, creating billions in value by improving efficiency and accuracy in areas that have remained unchanged for decades.
For instance, venture capital firms are using AI to assess startups with outstanding precision, while governments are deploying it to enhance public service delivery. This quiet revolution is reshaping industries behind the scenes.
5. Rethinking AI Hallucinations
AI hallucinations—when AI generates information that diverges from factual data—have often been condemned as flaws. However, Morris says these deviations might mimic the creative leaps made by human thinkers. In fields like art, design, and product development, these unexpected outputs could lead to great ideas.
The challenge for 2025 and beyond will be to balance creativity with reliability. Industries that rely on accuracy, such as medicine or law, will need solid systems to mitigate hallucinations, while creative sectors may explore their prospect for innovation.
6. The Rise of Private AI Networks
Data privacy and governance remain urgent concerns in AI adoption. As a solution, organisations are increasingly turning to private AI networks—secure, closed ecosystems that safeguard proprietary data.
In highly regulated industries like healthcare and finance, these private networks allow AI systems to operate within controlled environments, ensuring compliance and data sovereignty. In isolating research processes from public AI platforms, businesses can leverage AI’s capabilities while maintaining the highest standards of security and transparency.
The future of AI lies not in its public-facing applications but in its innovative abilities within research and corporate environments. Through automating mundane tasks, enhancing access to sophisticated tools, and addressing long-standing biases, AI is set to completely change how businesses and individuals approach discovery and problem-solving.
Mel Morris says organisations that recognise this change and adapt early will lead the next phase of innovation, changing the industries and economies of tomorrow.
