Marketing teams are moving from experimenting with AI to organizing work around it. Agents now own defined jobs inside marketing operations, while search visibility is shifting from rankings to citations inside AI answers. Measurement lags adoption: 47.8% of marketing teams have replaced tasks with AI, yet only 6.5% measure ROI.

LinkedIn rewired how reach is earned this fortnight, splitting the feed, shrinking network distribution and flagging AI content, while charging for networking features that used to be free. Brands responded by moving the voice from company pages to employees and creators, a shift Microsoft formalised and Billion Dollar Boy Group backed with its first acquisition. For sellers, the common thread is that specificity, first hand proof and buyer problems now outperform volume and templates.

The edition marks a move from partner programs built around structure to partner programs built around business outcomes. Co-sell, marketplaces and AI are now judged on revenue, attribution and trust rather than on listings, tiers or tooling. Frontier AI vendors and hyperscalers are formalizing their partner networks, which raises the bar on operating discipline for everyone selling alongside them.

Agents now sit on top of the martech stack, and the vendors who own the data and permissions underneath them are repositioning around that fact. The constraint on value has moved from which agent to deploy to the quality of customer data, context and governance beneath it. In the same period, a new marketing automation entrant launched against the incumbents, and CDPs, MAPs and consultancies each had to defend their role.

GTM teams spent the period consolidating tools into agent ready stacks, with Clay, Salesforce and HubSpot competing to own the context layer beneath them. The GTM Engineer is now a defined role, though its scope, ownership and long term survival are still contested. Practitioners keep returning to buyer trust, problem led demand and clear ownership as constraints that tooling does not solve.

Field marketing is shifting from counting attendance to proving pipeline. Smaller, better targeted formats and interactive activations are replacing scale and logoed giveaways, while event tech stacks struggle to carry booth conversations through to revenue. Market data from Forrester, Norwest, and Trade Show Executive confirms that events remain a core channel, and that organizers still have limited proof of booth value to offer exhibitors.

Capital is concentrating in the largest managers while the rest of the market adapts. Mega funds across infrastructure, energy transition and buyout closed above target, yet fundraising for mid-sized managers is tightening as distributions lag and rates stay high. The response is structural: record co-investment volumes, evergreen vehicles, continuation funds and a sharper LP focus on cash returned over reported value.

Venture capital is concentrating faster than at any point this decade, with megafunds absorbing most LP capital while first time and emerging managers shrink in number. Distributions remain the binding constraint, as strong paper returns sit alongside near zero DPI for recent vintages and an exit queue that keeps growing. AI dominates both deal value and, increasingly, how funds themselves are run, while Europe and other regions confront structural gaps in speed, capital supply and early-stage pipeline.

Deal value has outpaced deal count in the UK and Germany is recovering selectively, while cybersecurity and Finland are setting activity records. Capital and strategic appetite are widely available, so the constraint has moved to execution: seller preparation, integration discipline and risk management now separate deals that close and create value from those that do not. AI has become both a rationale for acquisitions and a source of late stage deal risk.

The exit market is reopening unevenly. Capital is returning to a narrow group of managers and deals, while a large inventory of aging portfolio companies still waits for a buyer and holds back liquidity across the system. Sponsors, founders and finance leaders are responding by treating exit readiness as an operating discipline that starts years before a sale.

AI has moved professional services from experimentation to restructuring. EY's results show the scale of the shift: total revenue grew 4.7% while sales from AI services grew 49%. Across talent, pricing, firm strategy, research and SAP and Oracle delivery, judgment, trust and governed data now carry more of the value than the speed of producing a first draft.

Private equity value creation moved from financial engineering to operating execution across this period. Compressed exit multiples, longer holds and higher rates left EBITDA growth and cash conversion carrying returns, while AI adoption widened the gap between firms that deploy it and firms that can show results. Talent, ownership and execution capacity became the binding constraints on every plan.

The defining question across this period was not whether manufacturers can run AI pilots, but whether they can turn those pilots into governed, enterprise-scale operations. Software vendors moved to consolidate the data and orchestration layer that scaling requires, capital kept flowing into US and European plant modernization, and the physical AI conversation shifted from robotics demos toward edge deployment at named factories. Sovereignty and infrastructure emerged as a parallel theme, as European players tied AI ambitions directly to power, cooling, and data center capacity.

Connected Tools & Equipment saw cordless brands push out a dense run of new launches this period, field commentary kept surfacing the same safety and calibration gaps, and asset tracking visibly moving away from RFID toward lighter, infrastructure aware alternatives. Beneath all of it sits a steadier pattern: procurement terms and battery economics, not feature lists, are what actually determine which tools stay in service on a jobsite.

Physical AI moved from framing debate to funded infrastructure this period. Humanoid makers signed real production contracts rather than pilot agreements, investors pushed capital into embodied AI platforms at a pace that outstrips the entire prior year, and enterprise software vendors raced to own the layer that turns robot hardware into a manageable industrial system. The throughline across the period is that the constraint has shifted from proving a robot can act to proving it can be deployed, financed, and supported at scale.

Construction technology is moving past the era of digitizing paper and into a phase where data ownership, agentic tools, and connected infrastructure decide who captures value. The strongest signal across this edition is a split between platforms consolidating project data into a single operational layer and point tools that still leave contractors to do the integration work themselves. Funding and acquisitions are following the same logic, with buyers paying for data and workflow control rather than standalone features.

Manufacturers are no longer debating whether to adopt industrial AI, they are debating how to govern it, where to run it, and who owns the data underneath it. Across this edition, the same pattern surfaces in different forms: sovereignty over data and infrastructure, orchestration platforms that unify fragmented systems, and physical AI moving from foundation models into robots, drones, and shopfloor machines. Investment activity and customer case studies show the return side of that equation is already measurable, from record results to thousands of hours of avoided downtime.

The professional tools market is being reshaped by two parallel forces: cordless platforms are closing the performance gap with corded and pneumatic equipment, and vendors are wiring sensors, geofencing and production-system integrations directly into everyday tools. At the same time, torque and calibration discipline is becoming a distinct competitive layer of its own, with battery-powered bolting tools displacing pneumatic fleets and calibration intervals being rebuilt around real performance data rather than fixed calendars. Distribution and brand strategy are shifting in parallel, as manufacturers expand into new geographies and lean on training and demonstrations to build trust at the reseller level.

Healthcare AI has moved from capability to accountability. Buyers, regulators and vendors are now judging systems on ownership, evidence and workflow fit rather than on model performance alone. At the same time, capital and commercial strategy are concentrating around data platforms, surgical and imaging ecosystems, and consumer channels that own patient access.

Quantum computing is shifting from a contest over qubit counts to a question of industrial manufacturing, independent verification and measurable scientific utility, while post quantum cryptography has moved from a future concern to a current planning problem. In cybersecurity, exploitation that reaches attackers before patches exist is changing the premise of defense, so recovery and governance now carry as much weight as prevention. Across wider ICT infrastructure, decisions on virtualization, edge computing and connectivity are being rebalanced around resilience, local control and regulation.

The center of gravity in enterprise AI moved from the model to the system around it. Agent incidents during frontier testing pushed safety and governance from policy documents into runtime controls, while new releases, process redesign and usage based costs set the commercial agenda. The organizations pulling ahead are those that redesign the work, control what agents can do, and match model spend to outcomes.

Sovereignty moved from policy debate to procurement in this edition, with hyperscalers, regulators and regulated buyers all committing to concrete architectures. In parallel, AI spend is forcing a new discipline in cloud cost control, while private cloud and virtualization are being repriced and repositioned. The common thread is control: over data location, over cost, and over where workloads run.

The conversation has moved from whether AI carries an energy cost to how that cost is measured, disclosed and regulated. Efficiency metrics are widening beyond PUE to water, thermal and per token units, while European rules on data centre ratings and impact assessments turn transparency into a requirement. Capacity keeps scaling on dedicated power, and operators now compete on published efficiency figures.

AI has made building faster and cheaper, and the edition's central finding is that the constraint has moved to judgment, discovery and organizational design. Product roles are converging on builders who can prototype, yet the evidence points to teams, accountability and clear ownership as the scarce inputs. Meanwhile, operating models, not tooling, separate the organizations that scale AI from those that simply move faster.