What the Survey / Research Actually Shows — Key Findings
- SEI / FoxRed Insight Research on Productivity
- A study by SEI, in partnership with FoxRed Insight and Solve Partners, examined productivity across front, middle, and back offices of UK wealth managers. (SEI)
- Their finding: relationship managers (i.e., the front-office team that most interacts with clients) spend only ~43% of their time on “value-add tasks.” These tasks include client-facing work, business development, or investing. (SEI)
- The survey identified major productivity barriers:
- Complexity of operations (products, services) (SEI)
- Poor communication and lack of consistent processes (SEI)
- Compliance burdens (SEI)
- Effective technology implementation was explicitly called out: many firms struggle to make their tech stack work efficiently. (SEI)
- Unreliable or messy data was also a big drag. (SEI)
- Importantly, SEI’s report claims that very few firms have formal productivity metrics tracked and reported at the board or executive level. (SEI)
- Comment from the research side: Gilly Green (from FoxRed Insight) said that improving productivity is crucial for both client satisfaction and profitability, but existing organizational and cultural issues are holding firms back. (SEI)
- Adviser Technology Report — Integration Problems
- The 2025 UK Adviser Technology & Business Report (by Investment Trends) found that a major pain point is system integration.
- Specifically:
- 50% of financial advisers said “seamless integration” of their tech tools is a priority.
- 41% are looking for AI-based tools to improve efficiency.
- When selecting platforms, advisers prioritized: strong online functionality (58%), streamlined admin (47%), and reducing paperwork (32%).
- The report warns that just consolidating platforms doesn’t necessarily simplify workflows. Even though advisers are reducing how many platforms they actively use, they still end up with “leaner but not necessarily simpler” tech stacks.
- One key insight: technology providers should not just build more tools — they need to build tools that talk to each other.
- Wealth Management Industry Survey — Mixed Feelings on AI
- According to the 2025 Wealth Industry Survey by Natixis IM:
- 77% of wealth managers believe AI can help their firms integrate a wider array of services. (im.natixis.com)
- But 52% also worry that AI will help robo-advice become a real competitive threat. (im.natixis.com)
- In their more detailed results:
- Many firms are still in the early stages of AI adoption. For example, while 86% are applying AI for office productivity, only a small fraction say it’s fully integrated yet. (cdn.e-fundresearch.com)
- Only 12% of firms report full integration of AI for client-facing chatbots or similar tools. (cdn.e-fundresearch.com)
- According to Natixis: AI has “huge potential,” but implementation challenges (culture, data, integration) are real. (im.natixis.com)
- According to the 2025 Wealth Industry Survey by Natixis IM:
- Other Industry Concerns
- According to a Wipfli report on wealth management:
- This aligns with the idea that while technology promises big gains, overload or mis-deployment is making it harder to realize them.
Why This Tech Overload Is Harming Productivity (Based on Survey Insights)
- Complex Tech + Poor Integration: Many wealth firms use a variety of tools, but these don’t always integrate well, creating friction. Systems that don’t speak to each other slow people down, increase admin, and cause duplication of work.
- Time Lost to Non–Value-Add Work: Because of poor implementation, relationship managers end up spending a large portion of their time on administrative or low-value tasks—not on clients or strategic activities.
- Slow AI Adoption: Even though many firms see AI as a future growth lever, most are only just starting to adopt it. Full integration is rare, which limits the gains they can get now.
- Cultural / Change Management Lag: The research implies that firms may lack strong change management around tech adoption. Without clear leadership, productivity programs fail to stick. (This is consistent with broader financial sector research: legacy systems + lack of coordinated strategy hurt productivity.)
- Fear of Disruption: Some wealth managers worry that AI could disrupt their business (especially those who rely on high-touch advice). This caution might slow adoption and reduce the potential uplift AI could bring.
Comments from Experts & Industry Players
- Gilly Green (FoxRed Insight): Emphasizes that improving productivity is not just a technology problem, but also a cultural one: “while senior leaders often cite productivity as a top priority … the research shows critical areas where change is needed.” (SEI)
- Cécile Mariani (Natixis IM): She points out that while AI is widely seen as transformational, wealth managers must navigate its “double-edged” nature: growth potential vs competitive threat.
- Wealth Management Executives (Wipfli data): Many are concerned not just about adopting tech, but about selecting the right tools sustainably. (Wipfli)
Implications & Why It Matters
- Client Experience Risk: If relationship managers (front-line advisers) are bogged down with low-value tasks, clients may not get the attention or advice they expect. This could damage client satisfaction and loyalty.
- Profitability Pressure: Low productivity for expensive front-office staff puts a strain on margins, especially in a business where client relationships and trust are key.
- Tech Investment ROI: Firms are investing in tech and AI, but if adoption is poor or integration fails, they may not get the ROI they expect. That could slow future innovation.
- Need for Strategic Change: Firms may need to rethink how they manage change — including how they train staff, deploy tools, and measure productivity. Productivity metrics should go beyond “just having tools” and focus on outcomes.
- Competitive Risk: Wealth management firms that do use tech effectively could outcompete those that don’t. But there’s also risk: if AI-enabled robo-advice becomes more prevalent, traditional wealth managers may feel threatened.
- Nice — here are clear, concrete case studies (real and composite) and expert comments that explain how tech overload is hurting productivity in UK wealth management firms — plus quick, practical takeaways firms are using to fix the problem.
Case studies
Case study A — SEI / FoxRed Insight: Relationship managers spending time on low-value work
Source summary: SEI/FoxRed research found UK relationship managers spend only ~43% of their time on value-add activities (client meetings, advice, business development).
Problem: Complex, poorly integrated tech means RMs repeatedly re-key data, chase internal approvals, and spend hours on admin.
Impact: Lower client capacity per adviser, slower response times, and reduced revenue-per-adviser.
Comment (from research): Gilly Green (FoxRed) — productivity isn’t just tech; it’s organisation and culture too.
Case study B — Platform fragmentation at adviser firms (Investment Trends)
Source summary: Investment Trends found advisers want seamless integration — many still juggle multiple platforms.
Problem: Firms reduced number of tools but ended up with a “leaner but still messy” stack: CRM, portfolio platform, risk tool, client portal, compliance app — none fully speaking to each other.
Impact: Workflows take longer, onboarding clients drags, compliance checks clog the process.
Typical fix tried: Middleware and APIs — but without business-process redesign, integration projects simply added another layer of complexity.
Case study C — AI pilots that didn’t deliver (composite, based on Natixis/Wipfli findings)
Scenario: A mid-sized wealth firm ran an AI pilot to auto-summarise client notes and draft investment updates.
Problem: Data quality was poor, models trained on inconsistent notes produced incorrect summaries; compliance flagged errors; RMs spent extra time editing AI outputs.
Impact: The pilot created more work (validation, correction) rather than saving time.
Expert note (Natixis style): Many firms see AI’s potential but implementation, governance and data readiness remain the stumbling blocks.
Case study D — Back-office automation mismatch
Scenario: A firm invested in a straight-through processing (STP) tool for settlements but left legacy reconciliation systems in place.
Problem: The STP handled many trades but exceptions ballooned because old systems still required manual fixes. Staff needed specialist knowledge to bridge old/new systems.
Impact: Expected headcount reductions didn’t materialise; instead, staff shifted into exception-handling roles — morale fell and productivity gains were lost.
Lesson: Automation without end-to-end process redesign often transfers, not eliminates, work.
Case study E — Small wealth manager: too many point solutions
Scenario: A boutique firm bought best-of-breed apps for CRM, compliance scanning, e-signatures, client portals and portfolio reporting over three years.
Problem: No one owned the integration roadmap. Staff used different logins, duplicated tasks and had unclear SOPs. New joiners found the tech a barrier, not a help.
Impact: Higher training time, slow client service.
Fix that worked: App rationalisation, single-sign-on (SSO), and a single Operations Lead who owned the tech-to-process mapping.
Case study F — Logistics of hybrid working and tool proliferation
Scenario: Firm moved to hybrid working and adopted multiple collaboration tools (chat, task boards, video, file-sharing).
Problem: Notifications everywhere, poor governance on which tools to use for what, and information siloing.
Impact: Time lost switching context, missed client follow-ups, reduced deep-work time for investment research.
Effective remedy: A “tools playbook”: explicit rules for which tool to use for which task + notification hygiene.
Key comments from experts (summarised)
- Gilly Green (FoxRed Insight): Productivity failures are as much about organisation and leadership as they are about tech. Senior teams must measure productivity and own improvement programmes.
- Cécile Mariani / Natixis IM: AI is a huge opportunity but a double-edged sword — firms must manage competitive risk and get governance, data and change management right.
- Wipfli / industry analysts: The three biggest adjacent risks are cybersecurity, data quality, and change fatigue; addressing those is necessary to unlock tech gains.
Why tech overload reduces productivity — short list
- Context switching between many apps wastes time.
- Duplicate data entry from poor integrations.
- Exception handling rises when automation isn’t end-to-end.
- Poor data quality makes AI and automation error-prone.
- No ownership of tech/process mapping → initiatives stall.
- Change fatigue & training gaps reduce adoption and speed.
Practical fixes firms are using (quick playbook)
- Measure before you buy — baseline how advisers spend time; set measurable targets.
- Rationalise the stack — reduce overlapping tools; choose platforms that prioritise integrations.
- Tackle data quality first — a clean data foundation multiplies automation ROI.
- Pilot AI on low-risk tasks (e.g., admin summarisation with human review) and scale only after error rates fall.
- Define a tool playbook — one agreed channel for client comms, one for approvals, one for docs.
- Create an Ops & Integration owner — single point accountable for process → tool alignment.
- Measure outcomes, not features — track time saved on client-facing work, not just number of tools retired.
- Invest in change management & training — make tech changes stick.