🏠︎ | Past Sessions | Move Fast or Fall Behind Why AI Will Leave the Waiting to See Behind
- Event: Finance Forum 25
- Date: 7 October 2025
- Speaker: Piers Linney, Co-Founder and Executive Chairman, Implement AI
- Estimated read time: 8-9 minutes
Quick summary
This session argued that AI is not a future consideration for finance leaders, it is already reshaping how value is created and defended.
Piers Linney framed AI as an exponential shift rather than a linear improvement, warning that waiting for certainty is itself a strategic risk. He challenged finance leaders to stop focusing on the noise around AI and instead concentrate on where it already delivers operational and commercial advantage.
The core message was practical. Focus on revenue, capacity and experience. Start where AI works today, design around what it can already do, and prepare for a world where human judgement is augmented rather than replaced.
AI is not improving linearly, and that changes the risk profile
Linney began by reframing how leaders think about technological change. Most finance models assume linear improvement, small gains compounded gradually over time. AI does not behave that way.
He described AI as an exponential force, one that feels underwhelming at first, then accelerates rapidly once it crosses a threshold. His warning to the room was direct. AI today is the worst it will ever be.
This matters because organisations often judge new technology based on its current limitations. In an exponential curve, that instinct leads to underinvestment at precisely the wrong moment.
Linney urged leaders not to anchor decisions to what AI cannot yet do. Instead, they should assume rapid improvement and design their operating models accordingly.
From human first to AI assisted work
A key distinction in the session was between tools that assist humans and systems that can act independently.
Linney argued that finance is entering an AI assisted era. Tools are no longer passive. They can reason, converse, generate outputs, and operate workflows without constant human instruction.
He dismissed debates about consciousness as a distraction. What matters, he said, is capability. AI systems are approaching human performance across a growing range of cognitive tasks, while operating at machine speed and scale.
For finance leaders, this marks a shift away from manual delivery and towards judgement, prioritisation and strategic design.
Why finance delivery sits on the jagged technological frontier
Linney introduced the idea of a jagged technological frontier. Some roles are already exposed to AI disruption, while others are further away.
Finance delivery, particularly modelling and analysis, sits closer to the edge than many leaders realise.
He explained that fully integrated financial models can already be generated by AI when given the right inputs. What remains human led is not the mechanics of modelling, but the framing, interpretation and strategic use of outputs.
The implication for finance leaders is clear. The value of the function is moving away from production and towards decision support.
Stop chasing the noise, focus on the operational frontier
Much of the public discussion around AI sits at what Linney called the external frontier. Headlines, speculation and hype dominate attention but offer little guidance for action.
He encouraged leaders to focus instead on the operational frontier, where AI already works reliably and delivers measurable returns.
This means starting with use cases that are practical rather than ambitious. The goal is not to build the most advanced system, but to solve real problems faster, cheaper and at greater scale than before.
A simple framework for where AI delivers value today
Linney shared a practical lens for assessing AI opportunities using two dimensions, technical readiness and return on investment.
High impact opportunities exist where the technology works well enough today and delivers meaningful commercial value, even if it is not perfect.
He cautioned against waiting for flawless systems. In his experience, most businesses gain advantage by deploying AI early, learning quickly, and improving iteratively.
Revenue, capacity and experience are the three levers that matter
Across examples, Linney returned to three consistent outcomes where AI delivers value.
Revenue
AI is particularly effective at uncovering missed opportunities in existing data and interactions.
He described how analysing voice conversations, customer behaviour and transactional data can surface revenue opportunities that humans would never have the time or capacity to identify.
The advantage does not come from replacing people, but from seeing patterns and signals that were previously invisible.
Capacity
AI excels at high volume, repetitive activity that humans struggle to sustain.
Linney gave examples where AI agents handled thousands of outbound interactions in hours, at times when human teams could not operate, creating step changes in throughput.
This capacity gain allows teams to redeploy human effort towards higher value work rather than removing it.
Experience
AI enables personalisation at scale.
Instead of segmenting customers into broad groups, organisations can tailor interactions to individuals based on real behaviour, sentiment and intent.
This shifts experience from generic to genuinely responsive, without increasing cost.
Why data becomes strategic when AI can see the unknown unknowns
Linney described data as one of the most misunderstood opportunities in AI adoption.
Traditional analytics focus on known questions. AI can identify patterns and opportunities that were not explicitly searched for.
He explained how AI systems can monitor data streams in real time, identify conditions or behaviours of interest, and flag commercial opportunities automatically.
For finance leaders, this represents a move from retrospective reporting to proactive insight generation.
Voice is now a primary data source, not an afterthought
A major theme of the session was the importance of voice data.
Linney argued that conversations were historically recorded for compliance or training, but rarely analysed in depth. AI changes that.
Voice interactions can now be analysed at scale to understand sentiment, intent, missed opportunities and emerging risks.
This transforms conversations into a strategic asset rather than an operational by product.
Human and machine is the operating model, not replacement
Linney addressed common concerns about accuracy and hallucinations directly.
He argued that AI should not be compared to perfection, but to the human alternative. Where accuracy matters, checks and balances should be built in, just as they are for people.
He introduced a simple way to think about task allocation. Low to medium value, high volume, low variability work suits AI. High value, low volume, high variability work suits humans. Everything in between becomes a partnership.
This framing positions AI as an augmentation layer rather than a wholesale replacement.
What good looks like for finance leaders now
The practical takeaway from the session was not to become an AI expert, but to become an AI informed designer of workflows.
Linney argued that finance leaders already understand their processes, constraints and commercial priorities. That knowledge is what matters most.
The role of leadership is to imagine how those workflows change when AI can act at speed, scale and consistency.
Practical actions finance leaders can take now
Questions to ask this quarter
- Which finance activities exist primarily because humans are slow or limited
- Where do we rely on sampling rather than full data coverage
- Which decisions arrive too late to influence outcomes
- Where are conversations happening that we never fully analyse
- Whether AI is used to assist thinking or only to automate tasks
- Whether data is treated as static or as a live decision input
- Whether teams are redeploying time saved into higher value work
- Whether customer and stakeholder interactions feel more responsive
- Waiting for perfect accuracy before acting
- Treating AI as a technology project rather than an operating shift
- Assuming regulation will delay adoption indefinitely
- Underestimating how quickly competitors can redesign around AI
What good looks like in practice
A finance function that uses AI well is faster, more responsive, and more commercially connected. It spots opportunities earlier, scales insight without scaling cost, and supports better decisions across the business.
Conclusion, waiting is now a strategic choice
Linney closed with a stark message. AI adoption is not optional, and delay is itself a decision with consequences.
The organisations that move first will redesign how work gets done. Those that wait will find themselves competing against businesses with radically lower cost bases and faster decision cycles.
For finance leaders, the challenge is not to predict the future perfectly, but to start adapting now.
Key takeaways
- AI is an exponential shift, not a linear improvement
- Finance delivery sits closer to disruption than many realise
- Real value comes from revenue, capacity and experience gains
- Voice and data are emerging as strategic assets
- The future operating model is human judgement augmented by AI
Speaker
Co-Founder and Executive Chairman, Implement AI, Entrepreneur, investor and former Dragon on BBC Dragons’ Den, with a background in law, investment banking and corporate finance, now focused on practical AI implementation at scale.