How AI is powering the next generation of professional services operations

At Agileday, we’ve always believed that AI should make work better. Not by adding another layer of complexity, but by improving the systems teams already rely on. For professional services firms, that starts with the workflows that sit between the opportunity pipeline and client invoicing. The workflows that drive planning, staffing, sales, and delivery every single day.

"AI is only useful when it’s grounded in the way your business actually runs. That’s why we built it into the everyday workflows." - Jaakko Hartikainen

The impact isn’t marginal. It’s systemic. Cleaner workflows. Faster staffing. Smarter sales support. More confidence in every decision.

If you couldn’t join us, here’s a recap of the webinar highlights, and a look at how it could work for you.

Beyond AI assistants

Many firms tinker with generative helpers for slide decks or draft emails. This is helpful, yet they barely touch the real bottlenecks. The heartbeat of a professional services business sits between the CRM and ERP. It is in the middle where projects are planned, people are staffed, time is captured, and revenue forecasts rise and fall. We focused on that space and explored how AI, embedded in day‑to‑day workflows, turns scattered data into confident decision-making.

“Our platform lives in the gap between the pipeline to which you’re selling, and the invoices that you're sending,” Jaakko said. “Optimize that layer and everything speeds up.”‍

Vertical AI, not one‑size‑fits‑all

Most AI tools on the market today respond to prompts and generate content across a broad range of industries, but they don’t understand how a professional services business actually operates. Copilots and chatbots can be powerful - but without context, your people data, project history, margin targets, and client nuances can’t be applied to support planning, staffing, sales and delivery.

That’s where vertical AI comes in. Whether built directly into operational workflows or layered into general models using frameworks like Model Context Protocol (MCP), vertical AI bridges the gap between generic intelligence and business-specific action. It connects what your systems know with what your business needs to do.

Operational AI is optimized for this purpose. It relies on specific tooling and logic that reflects how services firms actually run - accounting for staffing logic, margin sensitivity, bench risk, project delivery data, and client expectations, for example. Without that foundation, even the most advanced models struggle to support the real work that happens day to day.

Operational AI is optimized to follow custom agentive workflows that power real use cases, like:

• Enriching RFP information with the firm’s delivery history and pre-matched teams
• Matching consultants based on skills, availability, margin and willingness
• Auto-generating resumes and sales materials to match opportunities

These aren’t just productivity boosts that reduce operational friction. They’re performance levers - measured by higher win rates, better utilization, and faster, more confident decisions.

The data foundation: three horizons

For AI to work across operations, it needs more than a large language model. It needs meaningful data structured to reflect how your business runs. But it also needs functionality and tooling built in a way that AI can leverage those to process large quantities of data that otherwise wouldn't fit in its context window. 

As Joni put it, “You could probably easily match ten experts to an RFP, but when you have fifty or more experts and a vast project history, you need tools for the AI to help it find the right matches.”

At Agileday, we use a three-horizon model to define the intelligence stack behind every AI decision. This framework was originally developed by McKinsey, and we’ve adapted it to reflect the realities of professional services operations.

1. Internal operational data: such as skills, work history, preferences, bench status, bill rates, margin targets. Much of it unstructured and under‑used. 

Jaakko noted, “The asset of every company are the people inside it. Matching soft data with hard numbers unlocks huge value.”‍

2. Industry benchmarks: anonymised data at scale reveals patterns a single firm cannot see, from over‑run risks to emerging rate trends. 

Joni added, “AI is able to process large quantities of unstructured data and find signals and patterns that might be difficult to spot in isolation."‍

3. External signals: market demand, client news, and technology trends enrich internal context and keep decisions grounded in reality.

The maturity journey

AI in professional services is evolving fast. And while the maturity journey isn’t new, many firms are still early in their adoption. We see it across firms we speak with: some are experimenting with basic AI tooling, others are already deploying Agileday’s intelligent agents that nudge, predict, and optimise across business-critical workflows.

The landscape can be described through six levels of maturity. While we won't dive deep into each one, the key idea is progression - from AI that supports a single task to AI agents that operate across interconnected workflows.

Firms typically start by automating isolated actions like CV parsing. Then they evolve toward enabling workflows, deploying task-specific agents, and ultimately orchestrating autonomous decision-making across staffing, planning, and forecasting.

"Automating tasks will give you a nice improvement on things, you know, 10 percent or 20 percent. But when we get to these system-level changes, we're talking about 10x benefits." – Jaakko Hartikainen

Live examples from the demo

These aren’t just technical demos. They’re real workflows used by Agileday clients today. Examples include:

• A consultant uploads a CV. Agileday parses it, maps relevant skills, fills in missing details from internal delivery data, and links it to upcoming needs. It then suggests a development plan that aligns with actual client demand.
• A project is marked as complete. The system uses team composition, skills used, and delivery data to automatically generate a polished reference story. This removes the lag that often delays future sales enablement.
• A salesperson receives an RFP. They upload the file, and Agileday does the heavy lifting - identifying required roles, matching them with available consultants, generating tailored arguments for each fit, rewriting CVs based on the clients' needs, and automatically building the delivery plan based on consultant skills, experience, availability, and willingness.

What’s next

The future of operational AI is not about replacing people. It is about supporting them with the right context, at the right time, so they can make better decisions, faster.

Consultants will no longer be left wondering what’s next. Instead, they’ll be prompted with personalized suggestions based on live operational data. I.e. “There’s business demand for ABS skills. You’ve got a three-day gap next week. Do you want to complete the certification while you’re available?” That’s not a notification - it’s operational alignment.

Managers will shift from reactive planning to proactive scenario modeling. With access to future sales pipeline, skills, and capacity insights in one view, they can ask “what if” questions - what happens if we land this deal, or miss that one - and get actionable staffing strategies in return.

Behind the scenes, agents will constantly monitor operational health. They’ll flag early signs of over-utilization, spot mismatched skills in proposals, highlight mentorship needs for junior consultants, and suggest upsell opportunities based on historical delivery data.

“The whole idea is that people and agents work together,” said Jaakko. “The system doesn’t replace human decisions. It just makes better ones possible.”

Behind that idea is a technical foundation that’s built to evolve.

“We’re building Agileday’s AI logic so that we can continuously leverage the latest models as they become available,” Jaakko added. “That means improving our AI capabilities in sync with the broader industry, while also advancing the specialised workflows and matching systems inside Agileday that those models rely on to get real work done.”

Why act now

Leaving the CRM‑to‑ERP layer untouched slows everything. Operational AI turns it into an advantage. Yes, automating single tasks can lift efficiency. But redesigning the system with workflow agents increases throughput by a factor of ten.

Sustained impact rests on three pillars: real-time snapshots of clean operational data, open APIs that let AI see everything in context, and agents trusted to make proactive recommendations.

See it in action

The example workflows above are not a roadmap. They are live inside Agileday today. Get in touch to request the webinar recording or meet our team for a walkthrough to see how AI can re‑shape how your firm operates.

Let the Agiledays begin.