More than two-thirds of investment managers now use AI in the front office
January 19, 2026
AI is no longer a side project in asset management. According to SimCorp's 2026 InvestOps Report, 70% of buy-side firms are now using AI in the front office-up from roughly 10% experimenting a year ago. The study surveyed 200 senior leaders across asset managers, pension funds, and insurers, led by WBR Insights.
AI has moved from pilots to production
The focus has shifted to scaling what works. 58% are consolidating vendors and platforms, and 54% are modernizing technology architecture and data infrastructure. These moves cut tech sprawl, automate workflows, and give models consistent data signals so they can perform in real-world conditions.
Investment priorities have flipped
For the first time in three years, innovation leads the budget conversation. 55% prioritize competitive differentiation through innovation, compared to 44% for cost control and 33% for operational efficiency. That means front-office AI with measurable outcomes will win funding faster than back-office tinkering.
Vendor selection is getting stricter
Firms are more selective with third-party tools. Vendor stability tops the list at 57%, outranking access to innovation, analytical flexibility, and proven ROI. Strong data governance, cybersecurity, and regulatory alignment are non-negotiable-especially as firms feed proprietary data into models. For a practical framework, see the NIST AI Risk Management Framework.
Private markets are a growing focus
Confidence is rising in alternatives. The share of leaders who see alternatives as the best area for innovation jumped 24 points to 51% over the last year. Across the board, 72% ranked AI, generative AI, and advanced analytics as the top innovation opportunity.
What to do next (management checklist)
- Make the data layer a priority: Establish a single, governed data foundation for pricing, reference, and research inputs. Consistency beats scale when models hit production.
- Consolidate vendors: Reduce overlap, standardize integrations, and focus on partners that can deliver model performance and uptime-not just demos.
- Set clear buying criteria: Rank stability, model transparency, data controls, and time-to-value. Push for proof points with your data, not generic benchmarks.
- Lock down risk and security: Enforce data lineage, access controls, and audit trails. Align model governance with internal risk policy and external guidance such as the NIST AI RMF.
- Target front-office use cases with fast payback: Idea generation, signal research, scenario and risk analysis, pre-trade analytics, and decision support for portfolio construction.
- Prove ROI early: Baseline your KPIs (hit rate, turnover, slippage, time-to-insight) and review monthly. Keep what compounds, kill what doesn't.
- Invest in skills: Build capability for PMs, analysts, and quants-prompting, data hygiene, evaluation, and oversight. Curated options are here: AI tools for finance.
As one leader summed it up, "AI adoption has dramatically shifted from pilots to business-critical applications in the front office. The advancements in AI can deliver the most value for investment professionals to enhance decision-making and efficiency when it is underpinned by a centrally governed and unified data layer." - Peter Sanderson, CEO, SimCorp
The takeaway for management
The market has moved. Treat AI as core front-office infrastructure: a clean data layer, a pragmatic vendor stack, strict governance, and relentless focus on measurable outcomes. Do that, and your next planning cycle writes itself-more conviction, fewer science projects, better results.
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