The work is the deliverable.
No theory, no shelfware strategy. I build and ship the thing — then embed it into how the team actually works, day to day, until it sticks. An unused tool is a failed project, however elegant.
Know which AI workflow is worth backing — and prove one.
Next Step Ventures AI Consultancy takes one recurring private-markets workflow, establishes its economics and risk, builds a working proof inside your approved environment, and gives you a clear go/no-go case with controls and next steps. No platform sale. No big transformation programme. One named specialist working directly with your team.
The Proof Sprint
01
About
NSV / LDN
Next Step Ventures AI Consultancy is led by James Bell, based in London. The practice works with private-markets teams globally, including clients in New York and San Francisco. He's spent his career increasing AI adoption and engagement, and building and implementing it inside large private-markets finance firms and startups. The focus has always been sitting with the people actually doing the work: what tools they've really got, how their processes actually run, where things break down.
That is the key difference - It isn't generic tech consulting bolted onto finance, and it isn't a finance CV dressed up in AI vocabulary. It comes from starting with how the work actually gets done, and the people doing it, not the org chart.
profile.json
Led byJames Bell
BasedLondon, United Kingdom
Client reachGlobal
BackgroundFinance, Data & AI
FocusHands-on implementation
Builds in publicLinkedIn ↗
02
Approach
Applied > theoretical
The difference between AI consulting that sells potential and applied work that ships.
Your stack, your permissions, your maturity.
Regulated firms rarely have access to the latest and greatest — some have Cowork, some only Copilot, most sit somewhere in between. Startups just want to move fast. Either way, my job is to find the best approach for your actual situation, not to demand you adopt something new.
Controls first-class, not an afterthought.
Adopting AI is as much about guardrails, policy and operating procedure as it is about tooling. I design the safe, defensible way to work — so what gets built survives contact with compliance, and with reality.
Real outcomes, honestly framed.
No inflated claims, no buzzword soup, no promises the technology can't keep. Where AI genuinely helps, we go deep. Where it doesn't, I'll say so — that's what makes the rest credible.
03
Services
5 lines of work
AI consulting services for private equity, private credit, investment management, infrastructure and regulated firms.
Role-based AI literacy and practical training for investment and operating teams, using real workflows, real documents and the tools people already have access to. Explore AI literacy for private markets or private-equity-specific training.
Hosted AI hackathons and structured working sessions that surface, prioritise and prototype the highest-value use cases with your team - so investment goes where the evidence points, not where the hype does. See AI hackathon hosting.
Automated processes and agentic workflows, built and shipped. Internal AI tools and apps, built around approved data and the systems you already run. Explore AI implementation or sector support for private credit, investment and asset management and infrastructure investing.
The direction, policies, controls, use-case priorities and ways of working that let a firm adopt AI safely and defensibly. See the AI foundations programme for private markets.
Figuring out the right approach given your current stack, regulatory constraints and risk appetite — a clear-eyed view of what's worth doing now, what to watch, and what to ignore. Explore all AI services for private markets.
04
Flagship engagement
Typically four weeks
One workflow. Working evidence. A decision you can defend.
The Private Markets AI Proof Sprint is a fixed-scope route from recurring workflow friction to a tested proof and a clear investment decision. It uses your approved environment, representative material and the people who own the work.
Define the case.
Map the current workflow, owner, input data, judgement, output, failure cost and success test. Establish what would make the opportunity worth backing—and what would invalidate it.
Output — workflow brief, baseline, risk map and agreed acceptance criteria.
Build and test.
Create the smallest end-to-end version that can be judged on real work. Test it against representative cases, sources, edge conditions, permissions and explicit human-review points.
Output — working proof, test pack, source controls and visible failure behaviour.
Make the call.
Set out the evidence, economics, controls, operating owner and remaining constraints. Recommend go, revise or stop, then hand over the proof and the next-step plan.
Output — decision pack, handover and a proportionate 30 / 60 / 90-day roadmap.
// What leaves the room
Workflow underwrite Working proof Validation pack Source & failure controls Go / no-go case Handover and a practical roadmap. Fixed scope, no platform tie-in, and no obligation to expand if the evidence does not support it.
05
Use cases
Illustrative, not exhaustive
Where applied AI earns its keep.
Every firm's highest-value use cases are its own. These are the themes where the work most often lands.
Communications
Taking the drafting burden out of high-volume, high-stakes correspondence.
e.g.Automated investor responses · LP query drafting · templated email workflows
Document review & interrogation
Reading at machine speed with an auditable trail behind every answer.
e.g.Data-room & CIM analysis · NDA and contract redlining · document comparison
Deal & pipeline workflows
Removing the manual drag from the processes deals actually run on.
e.g.Deal logging · pipeline tracking · briefing and memo drafting
Knowledge & research
Making what the firm already knows queryable — and what it doesn't, findable.
e.g.Queryable internal knowledge bases · thematic screening
Analysis & modelling
Getting structured data out of unstructured sources, and models that maintain themselves.
e.g.Data extraction & enrichment · spreadsheet and model automation
Internal tooling
Purpose-built software, at a fraction of the traditional cost and timeline.
e.g.Lightweight dashboards · agentic scripts built around existing systems
06
Blog
Field notes, sourced
Notes from the applied side.
Working notes from real AI consulting engagements - hackathons, private equity training, governance and adoption. No hype, sources at the bottom of every post. Start here:
Featured guide
Click here to read more →
End-to-end AI adoption at your firm.
A practical route from maturity assessment to team training, use-case discovery, governance, automation and productionised delivery.
10 Sep 2026
AI adoption is becoming a bottom-up movement
AI Adoption
→
04 Sep 2026
The AI rollout has happened. The training has not.
AI Training
→
30 Aug 2026
How to run a 30-minute AI working session in private markets
Implementation
→
21 Jul 2026
The Future of Grocery Shopping? How Supermarkets Will Use MCP
Retail AI
→
19 Jul 2026
How to Create Skills in ChatGPT and Claude
AI Skills
→
16 Jul 2026
The Copilot-Only AI Programme
Microsoft 365
→
21 Jul 2026
How to Score AI Use Cases Before Anyone Builds Anything
Implementation
→
12 Jul 2026
The AI literacy and implementation game plan
Implementation
→
30 Jun 2026
The gap between "AI can do this" and shipping it inside a regulated firm
Positioning
→
19 May 2026
How to run a use-case hackathon that actually ships something
Playbook
→
28 Apr 2026
What a three-hour AI crash course looks like
Training
→
09 Jun 2026
An unused tool is a failed project
Adoption
→
07 Apr 2026
Centralising knowledge: the one-stop shop your AI rollout is missing
Knowledge
→
View all posts →
07
Videos
Applied AI walkthroughs
Practical AI demos, shown in context.
Short walkthroughs created by James Bell, covering AI implementation, automations, document search and connector-based workflows.
Implementation
The 30-Minute AI Working Session for Private Markets
A practical framework for turning workflow friction into one specific, owned and buildable AI use case by inspecting the data, process, judgement and required output.
Retail AI
The Future of Grocery Shopping? How Supermarkets Will Use MCP
A concept demo showing how dietary requirements can become recipes, a weekly meal-prep plan and an itemised supermarket basket through an MCP-style product connection.
AI Skills
How to Create Skills in ChatGPT & Claude (Step-by-Step): SKILLS 101
A practical tutorial on turning a repeated task into a reusable skill, using an actionable email-triage workflow to explain triggers, inputs, process and output.
Implementation
AI Literacy Is Not Training: The Implementation Game Plan
A roadmap for moving from scattered AI experimentation into governed, productionised adoption: maturity assessment, use-case mapping, internal marketplaces, triage and adoption loops.
Automation
ChatGPT Task Scheduling - News Aggregator Automation
A practical walkthrough of scheduled ChatGPT tasks for recurring work, reminders, background checks and news aggregation, with notes on prompt structure and reliable task design.
Knowledge search
ChatGPT Search Over Unlimited Documents with Agent Builder
How to connect Agent Builder to a vector store so large document libraries become searchable AI assistants for knowledge bases, policies, compliance material and support.
Connectors
Quick & Easy: Connect ChatGPT to MCP (Financial Data) using CONNECTORS | Applied AI
A quick setup demo connecting ChatGPT to the free Alpha Vantage MCP through Connectors for live stock and market data, aimed at non-coders as well as operators.
> nsv --request-fit-call
Bring one workflow.
Share the recurring task, the people who own it, the approved tools or data, and what a good result needs to look like. If a Proof Sprint is not the right starting point, I will say so and suggest the smallest credible next step.