Build vs. Buy: The Multi-Agent Platform Dilemma for Consulting Firms

The Fork in the Road

Every generation of consulting firms has faced a technology shift that redefined what it means to deliver value. For the 1980s, it was spreadsheets replacing armies of analysts with calculators. In the 2000s, it was data warehouses and visualization tools like Tableau. Today, it’s multi-agent AI platforms – orchestrated systems where teams of AI agents collaborate to replicate and accelerate complex consulting workflows.

The question isn’t if these platforms will become core infrastructure – it’s who builds them, and who buys them.

It’s a familiar dilemma. Do you lay your own train tracks, or do you ride the high-speed rail that’s already built?

What It Really Takes to Build

On the surface, building feels like the safer option. You already have analysts, IT capacity, and access to APIs for the latest models. But beneath the surface, you’re not building a “tool” – you’re standing up a software company inside your consultancy.

Think of it like deciding to manufacture your own aircraft instead of booking flights. Possible? Sure. But the hidden costs and risks pile up fast.

1. Orchestration & Architecture

• Agents must hand off work like a relay team – mismanage the baton pass and the whole race is lost.
• Context must persist across dozens of steps, or outputs drift off course.
• Systems must support both one-off client-specific workflows and standardized, repeatable ones.

Example: Imagine trying to automate a regulatory horizon scan. One agent gathers sources, another interprets changes, a third maps impact to the client portfolio. If handoffs fail, the result is an incomplete or misleading summary – unacceptable for client delivery.

2. Data Integration & Sources

• Public, proprietary, and client-provided data must flow into the system.
• Formats vary wildly: PDFs, APIs, scraped feeds, Excel sheets.
• Licensing and compliance must be managed – a single misstep can mean exposing a client to risk.

Example: A team estimating used-car financing dynamics quickly discovers public data is patchy. They need premium datasets – $70k per project – plus expert-network interviews. Without source integration, the AI system is flying blind.

3. Quality & Trust

• Guardrails are essential: consultants can’t hand clients hallucinated numbers.
• Outputs must be benchmarked against real consulting standards – speed alone is irrelevant if the answers are wrong.
• Human-in-the-loop review must fit naturally into consultant workflows, not add friction.

Analogy: An autopilot in aviation doesn’t remove the pilot – it keeps the plane level while the pilot focuses on judgment calls. AI agents should work the same way.

4. Enterprise-Grade Requirements

• Security and compliance (GDPR, SOC 2, data residency).
• Audit trails for every step – clients expect defensible work.
• SLAs for throughput and latency: large projects may mean thousands of workflows running at once.

5. Ongoing Maintenance

• Models evolve quarterly. What works today will be outdated next year.
• Prompts, workflows, and data connectors need continuous tuning.
• Internal IT teams risk becoming bogged down in maintenance instead of client-facing innovation.

Analogy: It’s not like building a bridge once – it’s like running a power grid that needs daily upkeep, expansion, and monitoring.

Why Buying Makes Sense

Specialist platforms exist because these challenges are non-trivial. Buying doesn’t just reduce cost – it accelerates time-to-value, lowers risk, and allows scarce talent to focus where it matters: winning clients.

Ready-to-Run Workflows

• Proven apps for consulting mainstays: market sizing, competitor benchmarking, regulatory monitoring, foresight.
• Validated in real client engagements, not just lab demos.

Example: Instead of spending 6 months coding a due diligence flow, firms can deploy a pre-built one tomorrow and customize it to their industry focus.

Data as a First-Class Citizen

• Platforms aggregate public, proprietary, and premium partner data.
• Tiered access models keep costs predictable: start with public, upgrade to bundled proprietary datasets.
• Consultants know what’s included and what’s missing, reducing second-guessing.

Built for Enterprise

• Security, auditability, and compliance built in.
• Guardrails and citations allow outputs to be shown directly to clients.
• Optimized for speed and consistency – what partners care about most.

Leverage vs. Fixed Cost

• No need to staff a permanent internal product org.
• Predictable subscriptions scale with project demand.
• ROI measured in weeks, not years.

Analogy: It’s the difference between building your own word processor and using Microsoft Word. You win by applying it, not by coding it.

The Sources Bottleneck

The biggest misconception is thinking analysis is the bottleneck. It’s not. Sources are.

• Peer-to-peer transactions → require expensive proprietary data.
• Value chain margins → depend on expert networks.
• Financing dynamics → lack reliable structured feeds.

Consultancies already spend millions annually plugging these gaps. A multi-agent platform that doubles as a data marketplace solves this pain point at scale.

Example: Instead of buying one $70k dataset per project, firms access bundled data directly inside the platform, applied across multiple clients and engagements.

The Adaptability Factor: Future-Proofing in an LLM World

The one certainty in AI is rapid change. Every quarter brings new models – cheaper, faster, more capable. A platform must not only handle today’s agents, but also be ready for tomorrow’s:

• Model Agnosticism – ability to plug in the latest frontier model without re-engineering workflows.
• Continuous Learning – workflows that improve as sources and prompts are updated.
• Scalable Experimentation – easy to test new models on small subsets before deploying across global engagements.

Analogy: Think of it like cloud infrastructure. Early adopters who built their own data centers couldn’t pivot when AWS and Azure scaled globally. Firms that bought into adaptable platforms leapfrogged ahead.

Build vs. Buy: A Simple Framework

Common Pitfalls When Firms Try to Build

• The Pilot Trap – impressive demo, but can’t scale beyond one client team.
• The Data Desert – orchestration works, but workflows collapse without reliable sources.
• Runaway Costs – token and infrastructure bills explode.
• Talent Drain – consultants diverted into platform maintenance.
• Innovation Lag – while maintaining internal tools, competitors move ahead with new workflows.

The Bigger Picture

Multi-agent platforms are not experiments anymore – they are quickly becoming the backbone of consulting delivery. The key question isn’t whether to adopt them, but how.

Building from scratch may feel like control, but it delays impact and risks leaving firms behind peers who are already buying proven platforms. Buying means focusing on differentiation where it matters: client trust, speed-to-insight, and expansion of services.

Analogy: You don’t win by making the tools – you win by delivering the skyscrapers with them.

Closing Thought

For the Big Four and other global consultancies, the stakes are clear: the next decade won’t be defined by the size of analyst benches, but by the ability to orchestrate data, automation, and expertise at scale.

Multi-agent platforms are no longer experimental – they’re fast becoming the backbone of modern consulting. The question isn’t whether to adopt them, but how quickly you want to start delivering value. With FifthRow, you don’t need to hire a product team or patch together data sources. You get proven workflows, built-in governance, and the confidence that your system will keep pace as AI advances. The firms that win will be the ones who buy, adapt, and move now.