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.

AI is moving from demonstration to infrastructure in construction, and the constraint has shifted from model capability to data quality, integration and trust. Capital and acquirers are rewarding companies that connect existing systems or sit close to the physical work, while adoption data shows most firms have not yet embedded AI across core workflows. The edition's central tension is a fast tool layer built on top of fragmented project data.

Industrial AI moved from positioning to execution across the period. Leaders framed contextualized data and decades of production history as the real source of advantage, while vendors shipped agentic and physical AI foundations, extended digital twins into commissioning and shipbuilding, and pushed MES and edge platforms toward AI-driven decisions. Consolidation set the tone at the top of the market, with Schneider Electric agreeing to acquire PTC for $22.6 billion.

The period shows two parallel shifts. Condition monitoring is moving from sensor availability to data quality, installation discipline and software that works with equipment data already in place, while cordless tool platforms are extending into heavy duty work and attaching tool data to traceability. Commercial activity follows the same logic, with acquisitions, rental partnerships and distribution agreements concentrating on connected assets.

Robotics moved from demonstration to production over this period, and the constraint is shifting from what machines can do to what organisations can absorb. Humanoid makers began shipping at volume, foundation models and cloud toolchains turned physical AI into a software platform race, and capital concentrated in a few large rounds and strategic acquisitions. Europe holds more companies than China but trails on growth capital, while healthcare remains the least funded application relative to its need.

Digital construction is moving from asking which tools to buy to asking who owns the workflow around them. Vendors are embedding AI into BIM platforms and practitioners are building their own automation, while the people running projects name trust, process ownership and experience as the binding constraints. Capital is selective, labor gaps are widening, and data center demand is shaping both opportunity and risk.

Industrial AI in manufacturing is moving from pilots to scaled deployment, and the conditions for scale are now explicit: trusted data, measurable ROI and control over where the intelligence runs. Vendors are backing this shift with product releases, large partnerships and the Schneider Electric acquisition of PTC, while governments and enterprises tie competitiveness to sovereign infrastructure. The trade show circuit, led by IMTS 2026, shows the conversation moving from what AI could do to what it already does on the shop floor.

Sovereignty has stopped being a residency checkbox and become a line item with real numbers attached, from Airbus's exit cost off AWS to Germany's 250 million euro sovereign AI tender. At the same time, private cloud is pulling AI inferencing back from the public cloud on pure economics, hyperscalers keep shipping product at a pace that outstrips most enterprise roadmaps, and FinOps and security teams are both being rebuilt around agentic workloads rather than human-run reviews. The throughline across this edition is that infrastructure placement, once a default, is now a negotiated decision with a price attached to every option.

Sustainable AI is shifting from pledges to plumbing. Grid operators in Denmark, California and Ontario moved to price or ration data center power, builders turned to liquid cooling, tape storage and engineered timber to cut physical footprints, and frugal AI tooling showed model choice and workload routing can cut energy use by orders of magnitude without new hardware. Research kept revising the problem's scale upward, from water and land use to energy spent beyond training, while governance work pushed to make green AI claims verifiable. Efficiency only counts once it is measured, disclosed and designed in.

Product management's center of gravity is shifting from managing roadmaps to owning outcomes under AI native conditions. Across skill building, strategy, craft, operations, and the events circuit, the same tension surfaces repeatedly: teams can now ship faster than ever, but shipping faster is exposing gaps in judgment, operating models, and measurement rather than closing them. The result is a field re-sorting itself around who can turn speed into an actual outcome, not just more output.

The period was defined by a widening gap between the speed of the drone and counter-drone fight and the speed of the institutions meant to govern, fund and regulate it. Ukraine's BRAVE1 ecosystem and its Western partners kept compressing the innovation cycle from concept to front line, while primes, the Pentagon and European capitals worked to institutionalize procurement, marketplaces and treaties fast enough to keep pace. Underneath both stories sits a capital question. Venture money is flowing into defense tech at a volume and valuation level few in the sector have seen before, and the test now is whether that capital is chasing genuine capability or just a hot category.

Epic and Oracle are pulling their AI ambitions inward, building agents, cost meters, and patient tools directly into their own platforms rather than opening them to outside developers. That shift is pushing governance questions, from AI token costs to cyber risk to data normalization, onto the same agenda as adoption. Around the two EHR giants, device makers, diagnostics labs, and virtual care providers are each proving out narrower, more clinically anchored bets.

Cybersecurity leaders are shifting from point-in-time compliance toward continuous, attacker-centric resilience, a move accelerated by AI agents that now sit on both sides of the threat equation. Quantum computing is moving in parallel from research milestone to boardroom budget line, with hardware progress, national defense programs, and enterprise hiring all advancing at once. Underneath both trends sits a harder infrastructure and governance problem: data centers running into physical limits, ERP and digital transformation programs losing value to early decisions rather than technology itself, and organizations working out who is accountable when the actor behind a privileged account is an AI rather than a person.