Five AI Breakthroughs From 2025 That Will Show Up in Your P&L

/ Hans van 't Riet

A year ago, if you asked an AI to handle a complex customer refund, it might draft an email for you to send.

As 2025 comes to a close, AI agents in some organisations can now check the order history, verify the policy, process the refund, update several systems, and send the confirmation. That is not just a better copilot; it is a different category of capability.

Throughout 2025, the story has shifted from “we are running pilots” to where AI is quietly creating real value inside the enterprise: agents that execute multi-step workflows, voice AI that resolves problems end-to-end, multimodal AI that works on the messy mix of enterprise information, sector-specific applications in life sciences and healthcare, industrial and manufacturing, consumer industries and professional services, and more reliable systems that leaders are prepared to trust with high-stakes work.

This newsletter focuses on what is genuinely possible by the end of 2025 that was hard, or rare at the end of 2024 and where new value pools are emerging.

1. From copilots to autonomous workflows

End of 2024, most enterprise AI lived in copilots and Q&A over knowledge bases. You prompted; the system responded, one step at a time.

By the end of 2025, leading organisations are using AI agents that can run a full workflow: collect inputs, make decisions under constraints, act in multiple systems, and report back to humans at defined checkpoints. They combine memory (what has already been done), tool use (which systems to use), and orchestration (what to do next) in a way that was rare a year ago.

New value pools

  • Life sciences and healthcare: automating  start-up administration, safety case intake, and medical information requests so clinical and medical teams focus on judgement, not paperwork.
  • Industrial and manufacturing: agents handling order-to-cash or maintenance workflows end-to-end. From reading emails and work orders to updating ERP and scheduling technicians.
  • Professional services: agents that move proposals, statements of work, and deliverables through review, approval and filing, improving margin discipline and cycle time.

2. Voice AI as a frontline automation channel

At the end of 2024, voice AI mostly meant smarter voice responses: slightly better menus, obvious hand-offs to humans, and limited ability to handle edge cases.

By the end of 2025, voice agents can hold natural two-way conversations, look up context across systems in real time, and execute the simple parts of a process while the customer is still on the line. For a growing part of the call mix, “talking to AI” is now an acceptable – sometimes preferred – experience.

New value pools

  • Consumer industries: automating high-volume inbound queries such as order status, returns, bookings, and loyalty program questions, with seamless escalation for the calls that truly need an expert.
  • Life sciences and healthcare: patient scheduling, pre-visit questionnaires, follow-up reminders, and simple triage flows, integrated with clinical and scheduling systems.
  • Cross-industry internal support: IT and HR helpdesks where a voice agent resolves routine issues, captures clean tickets, and routes only non-standard requests to human staff.

3. Multimodal AI and enterprise information

Most early deployments of generative AI operated in a text-only world. The reality of large organisations, however, is multimodal: PDFs, decks, images, spreadsheets, emails, screenshots, sensor data, and more.

By the end of 2025, leading systems can read, interpret, and act across all of these. They can navigate screens, and combine text, tables, and images in a single reasoning chain. On the creation side, they can generate on-brand images and videos with consistent characters and scenes, good enough for many marketing and learning use cases.

New value pool

  • Life sciences and healthcare: preparing regulatory and clinical submission packs by extracting key data and inconsistencies across hundreds of pages of protocols, reports, and correspondence.
  • Industrial and manufacturing: combining images, sensor readings, and maintenance logs to flag quality issues or emerging equipment failures before they hit output.
  • Consumer and professional services: producing localised campaigns, product explainers, and internal training content in multiple languages and formats without linear increases in agency spend.

4. Sector-specific impact in the P&L

In 2024, many sector examples of AI looked impressive on slides but were limited in scope. By the end of 2025, AI is starting to move core economics in several industries.

In life sciences and healthcare, AI-driven protein and molecule modelling shortens early discovery cycles and improves hit rates, while diagnostic support tools help clinicians make better real-time decisions. In industrial and manufacturing businesses, AI is layered onto predictive maintenance, scheduling, and quality control to improve throughput and reduce downtime. Consumer businesses are using AI to personalise offers, content, and service journeys at scale. Professional services firms are using AI for research, drafting, and knowledge reuse.

New value pools

  • Faster innovation and time-to-market: from earlier drug discovery milestones to quicker design and testing cycles for industrial products and consumer propositions.
  • Operational excellence: higher asset uptime, fewer defects, better utilisation of people and equipment across plants, networks, and service operations.
  • Revenue and margin uplift: more profitable micro-segmentation in consumer industries, and higher matter throughput and realisation rates in professional and legal services.

5. When AI became trustworthy enough for high stakes work

Through 2023 and much of 2024, most organisations treated generative AI as an experiment.

By the end of 2025, two developments make it more realistic to use AI in critical workflows. First, dedicated reasoning models can work step by step on complex problems in code, data, or law, and explain how they arrived at an answer. Second, governance has matured: outputs are checked against source documents, policies are encoded as guardrails, and model risk is treated like any other operational risk.

New value pools

  • Compliance and risk: automated checks of policies, procedures, and documentation, with AI flagging exceptions and assembling evidence packs for human review.
  • Legal and contract operations: first pass drafts and review of contracts, research memos, and standard documents, with lawyers focusing on negotiation and high judgement work.
  • Financial and operational oversight: anomaly detection, narrative reporting, and scenario analysis that give CFOs and COOs a clearer view of where to intervene.

What this sets up for 2026

Everything above is the backdrop for 2026 – a year that will be less about experimentation and more about pragmatic implementation under real financial and regulatory scrutiny.

In my next newsletter, I will zoom in on:

  • Five strategic shifts – including the move from hype to “AI factories” with CFOs as gatekeepers, agents embedded in everyday roles, new organisational structures (CAIOs, AI CoEs, agent ops), governance moving from optional to existential, and the data-quality bottleneck that will decide who can actually scale.
  • Seven conditions for success – the financial, operational, and organisational foundations that separate companies who turn AI into EBIT from those who stay stuck in pilots.

Rather than extend this piece with another checklist, I will leave you with one question as 2025 closes:

Are you treating today’s AI capabilities as isolated experiments – or as the building blocks of the AI factory, governance, data foundations, and workforce that your competitors will be operating in 2026?

In the next edition, we will explore what it takes to answer that question convincingly.

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