VC Platform in the Age of AI

ver the last decade, platform teams have moved from nice-to-have to an integral part of most funds. They help a firm win competitive deals, help founders execute faster, and also help funds show real leverage. But, over the last three years, the job has changed again. AI now sits inside almost every workflow, and agentic AI further highlights the rapid rate of change. Whilst this technology has the power to let small teams dramatically increase their productivity, it also means that the differentiator for VC platform personnel becomes strong domain expertise.

But before we talk about the future of VC platform teams, it’s important to set the context.

What a platform team is

A platform team builds and runs services that support founders and the firm. It operates alongside the investment team, serving as a front-line function that touches sourcing, post-investment work, data, community, and the firm’s brand.

The function has been professionalised. VC Platform’s The Power of Platform study analysed 850 venture firms from 2000-2022. It found that just over half of firms now have a moderate or significant platform team. At firms with more than one billion in assets, more than nine in ten report a moderate or significant platform team. About one in eight core employees in VC sit on the platform. The same study links higher platform investment to stronger pooled net IRR and higher TVPI, especially over the last decade.

What platform teams do

In simple terms, platform teams cover the work before and after the check.

Before the check, the team supports brand, content, events, and community. It improves sourcing and win rates through relationship building and better funnel hygiene. After the check, the team supports hiring, customer introductions, go-to-market, fundraising, and sometimes ESG reporting. 

Additionally, and perhaps most critically of all, platform teams can be deployed to fill critical skills gaps within portfolio ventures, in a “management consulting-esque” model. Companies like OpenView Labs of OpenView, Insight Partners’ Onsite and Sequoia’s Creative Labs are all testament to this usage.

The difference from ad hoc partner help is scope, scale, and reliability. Partners tend to help in the domains they know well. On the other hand, platform teams have traditionally covered the common needs across the portfolio. Partners can only help a few companies at once, so platform teams help many companies through repeatable services at scale. Partner help is often episodic, whereas platform is traditionally scheduled, tracked, and measured.

But, this has already started to change…

Why Platform matters more now

VC is a highly competitive landscape, and the bar for support is higher. In this market, systematic and high-quality help can be a real edge, both as a marketing USP and value creation itself. Funds with significant platform teams have outperformed funds with no platform on pooled net IRR and net multiple. That gap grew more pronounced in the last decade. Correlation is not causation, but it is a clear signal that organised support can compound outcomes.

What changed with AI

Three shifts define the last three years.

First, AI moved from experimental tools to daily infrastructure. Enterprise spending on generative AI jumped sharply in 2024 and 2025, and adoption moved from pilots to production in core workflows. That shift set the base for platform teams to redesign intake, matching, reporting, and founder support with AI in the loop.

Second, the rise of agentic AI changed what one person can reasonably do. Agentic systems can plan, call tools, take actions, and hand work back for review. Gartner expects about one-third of enterprise software to include agentic AI by 2028, up from a tiny base in 2024. The same forecast says a meaningful share of day-to-day business decisions will be made autonomously by that time. This does not remove people from the picture, far from it. Rather, it empowers them, raising the leverage of each person who can orchestrate these systems.

Third, results across the board depend on domain expertise. A large field experiment with 758 BCG consultants found that people using AI completed more tasks, worked faster, and produced higher quality output, but only on tasks inside the current frontier of AI capability. On tasks outside that frontier, performance dropped. The lesson is simple: AI multiplies capable experts, but it does not replace true expertise.

These factors matter now more than ever. Agentic AI lifts the leverage of a small expert team. One platform lead can orchestrate agents for research, outreach, and reporting while keeping human judgement on the hard calls. Analyst work suggests agentic features will be common in enterprise software over the next few years, though many early projects get scrapped without clear value or governance. In short, expertise still decides outcomes, but AI lets that expertise reach further.

The leverage story in plain terms

One capable platform lead can now create outsized value. There is clear evidence that AI raises individual productivity in complex knowledge work. In the BCG study, consultants using AI completed about 12% more tasks and worked about 25% faster. Quality rose more than 40% compared with the control group. The right design and oversight matter. The same experiment shows quality falls when people apply AI outside its current strengths. That is why platform teams with deep subject knowledge and good process win.

Agentic AI raises the ceiling again. Systems that can string together steps, call software, and maintain context over time reduce busywork and speed up response. Gartner expects this to become standard in enterprise software over the next three years. Teams will see fewer status checks and more outcomes delivered to a review queue. The human keeps responsibility for judgement and trust, but now, the agent handles the legwork.

Why subject expertise matters more than ever

AI is powerful, but also generic and brittle. It performs well within its current frontier and can fail outside it. That creates a premium for people who can judge fit, shape prompts, select tools, and review outputs with a critical eye. The BCG study calls this the jagged frontier. Experts who know the terrain decide when to rely on AI and when to slow down and check. Those choices protect quality and speed.

Agentic systems add more moving parts. They chain tasks. They make calls to external tools. They change state. That creates new failure modes and new risks. Gartner warns that many agent projects will be scrapped by 2027 due to cost and unclear value. The same note flags vendor agent washing and limited maturity. Again, the implication is clear. Platform leaders with real subject matter depth, good vendor taste, and strong governance will deliver value. Shallow teams will waste time and budget.

A useful comparison with consulting

Consulting shows what happens when software lifts individual leverage. Industry reporting points to slower or falling headcount at the big firms since 2022, along with a shift toward deeper expertise and more use of AI. One analysis cites a drop in job postings, fewer new partners, and cutbacks in strategy roles, while firms invest in AI skills and training for the people they retain.

At the same time, firms like Accenture have moved to train very large workforces on agentic AI. This illustrates the direction of travel. Fewer generalists, more specialists, and stronger AI literacy across the team. VC Platform teams are expected to follow a similar arc.

What this means for funds

For the most part, the types of funds that have prioritised platform teams historically are large, early-stage funds, because more of the management fee can be allocated to support, as well as because needs repeat and cycles are faster. Larger funds tend to have platform teams because they can carry the fixed cost, and they face more competition for the best founders. The data confirms that platform concentration rises with assets under management and that prevalence is above 90% for firms over $1B AUM.

The practical shift is this - individuals can now see a dramatic increase in quality and quantity of output whilst combining agentic systems with strong subject leads. A single talent lead with the right agent stack can screen inbound candidates, enrich profiles, draft outreach, coordinate scheduling, and keep a clean pipeline. Human judgement still decides who to interview and who to hire, but AI removes the repetitive steps.

A single business development lead can maintain customer maps, prepare intro briefs, log outcomes, and push follow-ups. Agents enrich accounts and suggest next actions. The lead focuses on fit and relationship. A single data or operations lead can collect portfolio metrics with light automation, check for errors, and produce partner and LP views. AI helps standardise charts and drafts variance notes, but the human retains financial judgement.

These are not far-fetched ideas. They are already present in many firms and in many portfolio companies. Analyst notes from leading venture firms also frame the market as moving from ideas to building blocks and from monolithic models to agents that can reason, call tools, and work across modalities.

How AI changed daily platform work

Intake is now smarter. Simple forms and shared inboxes are still common. The change is that AI can classify requests, suggest answers from the knowledge base, and route to the right owner. The person reviews and sends. Time to first respond drops.

Matching is now faster. For hiring, partners, and customers, AI can clean lists, score fit, and draft first contact. The person checks tone, edits, and decides. Hit rate goes up when an expert owns the brief.

Reporting is now lighter. Portfolio data collection can use templated requests, reminders, and simple checks. AI turns raw exports into standard views. The person still writes the narrative and calls out the real issues. The quality of board prep improves.

The community is now more accessible. AI helps summarise calls, extract actions, and update the knowledge base. It keeps the library alive. People still host the sessions and keep the network healthy.

Governance has grown in importance. Agentic workflows touch tools and data across company and fund boundaries. Teams need clear rules for privacy, model training, data handling, and audit trails. Gartner expects agent adoption to grow fast, but also warns of scrapped projects and vendor hype. Strong governance protects you from that.

What to look for in platform hires now

Hire builders with depth. The strongest platform leaders have shipped services and run systems. They also bring real expertise. That might be talent, go to market, finance, or data. They can write clearly. They can design simple processes. They can evaluate vendors. They can use AI without outsourcing their judgement.

Give them ownership rather than a list of chores. Let them pick a small set of outcomes and report progress. Do not drown them in tools. Buy the plumbing. Build the parts that express your edge.

Expect them to be AI fluent. Not tool collectors. People who can frame a workflow, define steps for an agent, and decide when to hand work to a person. The BCG study shows that quality rises when experts use AI on the right tasks and falls when people apply it in the wrong places. That is the judgement you are hiring.

The limits to be honest about

Agentic AI is early in many categories. Many projects will be cancelled for cost or unclear value in the next two years. Vendor labels are noisy. Not every agent can really act. Plan for pilots with clear gates. Keep humans in the loop for external-facing work. Track real outcomes such as hires made, intros converted, and time saved.

AI results also vary by task. The same study that shows strong gains also shows clear drops when AI is applied outside its strengths. Make it normal to stop and check. Design workflows where AI proposes and a person disposes.

Where this lands

Platform is now part of the core engine of a venture firm. The profession has scaled over two decades. Most larger firms have moderate or significant teams, and there is a measured link between platform investment and pooled returns. That is the baseline.

AI raises the ceiling. Agentic systems let one person do the work of many. The consulting industry offers a preview - fewer generalists, more specialists, and strong AI literacy. Venture platform teams can take the same path. Keep the team small and expert, use AI to handle the legwork, and keep people responsible for taste, judgement, and trust. That balance will help founders now and will age well as the tools improve.

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