The shift from AI-assisted to AI-agentic is no longer theoretical. Models now reliably complete multi-hour autonomous tasks, and the trajectory suggests they will handle multi-day work by year-end. More significantly, the way frontier engineers work has fundamentally changed. They no longer write code with AI assistance - they orchestrate fleets of agents working in parallel, each tackling different features or problems simultaneously. The human role is shifting from execution to direction: defining problems, reviewing outputs, and steering strategic choices.

This has profound implications for how organisations think about productivity. Traditional metrics assumed a linear relationship between headcount and output. In an agentic world, that relationship becomes exponential for those who restructure their workflows accordingly. A single engineer running eight parallel agents isn’t eight times more productive in the old sense - they’re operating in an entirely different paradigm, exploring solution spaces that would have been impossible to traverse sequentially.

Acceleration without preparation creates new problems as fast as it solves old ones. As AI dramatically speeds up certain functions, constraints emerge in unexpected places predictable constraints emerge – the bottleneck simply shifts downstream. The organisations seeing the greatest sustained gains are those thinking end-to-end about their processes - not just optimising individual steps, but redesigning entire workflows to accommodate radically different capabilities.

In software development, this shift is already visible. Code generation is no longer the bottleneck - human review and steering are. When agents can produce thousands of lines of code in hours, the scarce resource becomes human comprehension. Then there’s a growing risk of what might be called “comprehension debt”: shipping code that works but that nobody truly understands. This creates fragility. When something breaks, teams find themselves unable to debug systems they didn’t really build. Security vulnerabilities slip through because review processes weren’t designed for this volume. The cracks appear suddenly and spread quickly.

The pattern repeats across functions. In go-to-market ("GTM"), AI can generate unlimited outreach and content - but the constraint shifts from production to strategy and execution quality. When product teams ship new capabilities in weeks, sales cycles measured in quarters create a fundamental mismatch. Enablement teams can't train salespeople fast enough to sell new capabilities, and incentive structures weren't designed for outcome-based pricing models. In operations, automation accelerates transaction processing - but compliance verification and exception handling become choke points.

A new capability is emerging to address this: go-to-market engineering. These are centralised ops teams that use AI to run dozens of experiments weekly, testing messages, channels, and targeting approaches at a pace traditional marketing structures cannot match. They generate Quarterly Business Review ("QBR") decks in minutes rather than hours, run automated social listening that flags competitive mentions in real time, and build personalised outreach at scale that would have required armies of Sales Development Representative ("SDRs"). The common thread is systematic experimentation - treating go-to-market as an engineering discipline where hypotheses are tested rapidly and insights compound over time.

One of the most counterintuitive insights from the frontier is that AI doesn’t reduce the importance of talent - it amplifies it. AI raises the floor for individual productivity, making average performers more capable. But it raises the ceiling far more dramatically. The best people, with the right curious mindset and willingness to experiment, become extraordinarily more powerful. The gap between top performers and everyone else widens, not narrows.

This has significant implications for talent strategy. Your best people are worth more this year than last year, and they’ll be worth even more next year. Investing in identifying, developing, and retaining high performers yields compounding returns in an AI-enabled environment. Conversely, there’s a harder truth that leaders must confront: a meaningful percentage of current talent may not make the transition. Not because they lack intelligence, but because they lack the adaptability or curiosity to fundamentally change how they work.

The traditional approach to product development - top-down annual roadmaps, careful scoping before engineering begins, extensive design iterations before building - was designed for a world of scarce engineering capacity. When coding was expensive and slow, it made sense to plan exhaustively before committing resources. That world no longer exists. When agents can prototype features in hours, the entire planning paradigm must shift.

The new model inverts the traditional sequence. Instead of specifying exhaustively then building, teams prototype rapidly then validate with customers. Instead of debating features in conference rooms, they ship variations and measure response. The cost of being wrong has dropped dramatically - which means the optimal strategy involves far more experimentation and far less up-front planning. Twelve-month roadmaps don’t survive contact with this pace of technological change.

For B2B software companies, AI presents both a challenge and a generational opportunity. The landscape is shifting: capabilities that once took years to build can now be prototyped in weeks. Some procurement teams are exploring internal alternatives during vendor evaluations, and switching costs that once protected incumbents are becoming less reliable. The old moats - implementation complexity, integration depth - still matter, but they're no longer enough on their own. SMEs are particularly innovative adopters of this approach. Faced with vertical SaaS solutions that don’t quite fit their needs, they increasingly turn to AI-powered development platforms first. Custom CRMs built for specific workflows, form tools replaced in minutes, HR systems tailored to actual processes rather than generic templates. The psychology has shifted: it’s more rewarding to build something that fits perfectly than to adapt to software built for someone else.

But the opportunity for incumbents is equally significant. You have distribution, customer relationships, and domain expertise that start-ups can't replicate overnight. The companies moving fastest are using AI to expand what they offer - shifting from tools that support work to platforms that can deliver outcomes directly.

One challenge worth noting: agents need rich context to be effective, and traditional systems of record may not capture enough. As Sarah Guo observed, the best salespeople know things about their customers that never make it into the CRM. Incumbents should be asking whether their data architecture is ready to feed the next generation of AI.

Two strategic paths are crystallising. The first is to become a platform: structure as API-first, let customers and agents build on top, create lock-in through the ecosystem rather than switching costs. The second is to sell work directly: shift from tools that help humans do work to outcomes delivered by AI agents, priced on value rather than seats. The choice isn't binary - many companies will pursue both through distinct product lines - but it must be made explicitly. Drifting between models captures the benefits of neither. And the window matters: incumbents who don't move to deliver this value will create space for new entrants who will.

The technology is ready. The tools exist. The path is increasingly clear. What remains is the hardest part: leading an organisation through genuine transformation. Change management has always been difficult, but the stakes have never been higher. Early movers don’t just gain temporary advantage - they enter a compounding cycle where better AI capabilities fund more investment in AI capabilities, widening the gap with each iteration.

The transformations that succeed share common characteristics. They start with unambiguous commitment from the top - not enthusiasm for AI in the abstract, but willingness to change everything about how the organisation operates. Every process, every job description, every incentive structure becomes subject to fundamental questioning. Leaders embrace uncertainty publicly, acknowledging they don’t know exactly what the organisation will look like in twelve months while committing fully to the journey.

Successful transformations also require leaders to go first. Executives who haven’t personally used the tools, who delegate AI understanding to technical teams, who maintain comfortable distance from the messy reality of adoption - these leaders become obstacles. The organisations moving fastest are those where senior leaders have rebuilt their own skills, publicly embracing beginner status and learning alongside their teams.

The cultural mechanism matters as much as the strategic direction. Clear expectations that transformation isn’t optional, combined with genuine support for those making the transition. Training, enablement, patience with the learning curve - but ultimately, accountability for adoption. The uncomfortable truth is that some people won’t make the journey. The compassionate approach is clarity: here’s the direction, here’s the support, here’s the timeline. The organisations that thrive will be those that move decisively while bringing their people along.