1. The emergence of autonomous and reasoning agents with AI as a core organisational strategy
By 2025, most experts and studies anticipate the rapid rise of autonomous agents —intelligent AI systems that will handle routine tasks so fluidly that their presence will be as commonplace as email or smartphones are today.
-
Beyond chatbots: The next generation of AI agents will proactively manage your calendar, negotiate for you, co-create much of the content you produce, and handle email triage, freeing up more time for tasks requiring high-level human insight.
-
Enterprise applications: Expect AI agent pilot programmes to scale company-wide, reducing operational costs and accelerating decision-making.
-
Business implications: The reconfiguration of human roles will raise challenges around governance, oversight, and accountability if these agents make erroneous decisions.
My perspective: “Some of the most intriguing searches I’m running involve AI for non-technology centred roles — highlighting just how pervasive AI has become across all business functions. It’s reminiscent of my experiences with Microsoft’s consulting team, where we treated AI as a strategic lever rather than a simple cost-saver. While some organisations are still focused on AI’s potential for efficiency gains, high-performing companies see it as a ‘co-creator,’ driving new product development and deepening customer relationships.”
2. Quantum computing and AI: solving the unsolvable
Quantum computing is set to revolutionise AI by tackling problems that are currently computationally impossible. The synergy between quantum computers and AI algorithms will unlock new frontiers in various industries.
What's different?
-
Material science breakthroughs: Quantum-powered AI will lead to the discovery of new materials with properties tailored for specific applications, such as superconductors for lossless energy transmission.
-
Climate modelling precision: Quantum AI enable better prediction of natural disasters and inform policy decisions on climate change mitigation.
-
Advanced cryptography and security: Quantum AI will develop new cryptographic techniques that are theoretically un-hackable, securing data in an era of increasing cyber threats.
Early quantum applications
-
Revolutionising agriculture: Quantum AI will optimise agricultural processes such as soil nutrient management and crop disease prediction, significantly boosting global food production.
-
Personalised medicine on a quantum scale: Treatments can be personalised with unprecedented accuracy, minimising side effects and maximising efficacy.
My perspective: “Quantum is already emerging as a game-changer for AI-driven research across pharma, finance, and climate science, to name a few. I’m often asked, ‘How soon until quantum AI is mainstream?’ My answer remains: much sooner than most people believe. Major players are investing heavily in this space, and it’s fascinating to help find the candidates who will shape this next wave of innovation.”
3. AI-driven healthcare: from reactive to proactive wellness
The healthcare industry is on the cusp of a transformation where AI doesn't just treat illnesses but predicts and prevents them. This shift from reactive to proactive healthcare will have profound implications for longevity and quality of life.
What's different?
-
Digital twins for health monitoring: AI creates a digital twin of your physiology, allowing for continuous monitoring and simulation of health scenarios.
-
Early disease detection with biomarkers: AI algorithms are identifying subtle biomarkers in medical imaging and genetic data that indicate the early stages of diseases like cancer or Alzheimer's, years before symptoms manifest.
-
AI nutrigenomics: Personalised nutrition plans based on AI analysis of genetic, microbiome, and lifestyle data will optimise individual health outcomes.
Unexpected applications
-
Mental health AI companions: Advanced AI companions are being developed to provide emotional support, monitor mental health indicators, and even alert professionals in case of severe risk factors.
-
Epidemic prediction models: AI will analyse social media, travel patterns, and environmental data to predict and prevent outbreaks before they occur, enabling swift public health responses.
My perspective: “AI-driven healthcare will become one of my primary areas of focus in 2025. I’m already engaged in promising discussions with companies pushing the boundaries of medical innovation — from a start-up leveraging AI-powered digital twins to simulate drug effects in lieu of early-stage human trials, to a global firm using AI to streamline pharmaceutical development. AI having a profound impact on patient outcomes and industry evolution is one of my personal bets for 2025.”
The ripple effects we should not underestimate
My perspective: The three trends above will have cascading impacts beyond their immediate applications. These will include:
-
Redefining privacy: As AI becomes more integrated into our personal and professional lives, the line between convenience and intrusion will blur. The ethical management of data will become a critical concern.
-
Education transformation: Educational institutions will need to overhaul curricula to prepare students for AI-augmented roles that don't currently exist.
-
Legal and ethical frameworks: The emergence of AI colleagues and quantum-powered decision-making will outpace current laws and regulations, necessitating new frameworks for accountability and ethical considerations.
Organisations should foster a culture where understanding AI is not limited to IT departments. Cross-functional AI literacy will be essential. Investing in AI literacy should be a top priority for any organisation in 2025. In addition, partnerships between businesses, governments, and educational institutions will be crucial in navigating the challenges and opportunities presented by these AI advancements.
Author’s note: So, how did this article come to life?
Writing about AI without leveraging AI would feel a bit out of sync. Yet simply letting AI generate the entire piece would also be of little value, given how easily anyone could do the same. That’s why I’m sharing my process, which I see as a balanced blend of AI-driven brute power and humble human perspective:
- In-depth AI research: I began by running AI-powered web searches across numerous public papers, expert analyses, industry reports, and thought leadership articles from reputable sources — Gartner, The Verge, Capgemini, and AI Magazines, among others.
-
Collaborative AI drafting: Armed with these findings, I used one of the most advanced large language models for reasoning — OpenAI o1 — to select the top ten trends, then iteratively narrowed them down to the three most pivotal, using various prompt schema to challenge the prioritisation process. The LLM also generated the initial draft of this article, serving as a helpful springboard.
-
Final human touch: Finally, using my trusty (albeit old-fashioned) keyboard, I refined the AI-generated draft by adding my own observations and concluding with a note on the ripple effects of AI that I think we should not overlook.