AI is everywhere in finance conversations right now. Webinars, vendor pitches, board updates, hallway chatter at every conference. But after the hype clears, finance leaders are left with the same hard questions: where do we actually start, what’s worth investing in, and how do we know it’s working?
Consero’s AMPLIFY 2026 panel featuring Kimberly Blascoe, Senior Director of CAS professional services at CPA.com, and Suma Chander, who leads technology and AI advisory for PFK O’Connor Davies, cut through the noise on exactly these questions.
Their conversation surfaced what’s working, what’s breaking, and what finance teams should focus on right now. Here’s what stood out.
The Pressure on Finance Has Fundamentally Changed
Monthly financial statements used to be the deliverable. Today, that’s not even close to enough. Sponsors and investors expect weekly — sometimes hourly — visibility into cash, margins, and KPIs.
Boards want context and forward-looking analysis, and finance teams are expected to deliver all of it with leaner headcount and a thinner talent bench.
“Today, investors and sponsors are looking for weekly and maybe even hourly visibility into cash, margins, KPIs. We didn’t even talk about that stuff probably even five years ago, for sure 10 years ago.” — Kimberly Blascoe
The expectations have moved and most finance functions haven’t kept pace.
The Talent Gap Is Forcing the AI Conversation
The accounting talent pipeline isn’t keeping up with demand, especially at the controller level where PE-backed portfolio companies are competing for an increasingly small pool of experienced candidates. That math doesn’t work out, and it’s why AI has stopped being optional.
“It’s not ‘should we adopt AI’ anymore. That question’s settled. It’s really how fast and where should we start adopting.” — Kimberly Blascoe
Smaller teams now have to punch above their weight. Technology is the lever that lets them do it.
Where AI Adoption Breaks Down
Chander noted that the real challenges in AI adoption aren’t technical. Culture and change management are what’s slowing companies down. Getting people to actually use the technology, measuring ROI, and figuring out how to extend early proof-of-concepts into real productivity gains.
“Where companies are faltering and having real challenges is two things: culture — one of the biggest hurdles — and change management.” — Suma Chander
Plenty of finance teams have stood up a pilot. Far fewer have figured out how to scale it.
Going from Pilot to Production Requires Real Foundations
Anyone can spin up an AI agent in a few weeks. The harder work is making sure that agent actually pulls accurate data, runs inside guardrails, and can be monitored once it’s autonomous.
That requires a foundation most companies skip:
- A knowledge graph
- A defined data ontology
- Clear governance around what the AI is allowed to do
“If firms fail to look at it at the early stage and they decide, ‘OK, we’re going to take care of it later,’ … companies are going to struggle.” — Suma Chander
Speed without structure leads to errors, missed GAAP rules, and agents that drift outside their lane. Foundations aren’t glamorous, but they’re what separates a demo from a production system.
Connectivity Is the Unlock
One of the biggest accelerators recently has been MCP — the Model Context Protocol — which makes it dramatically easier to connect AI tools to ERPs, accounting platforms, and the rest of the finance tech stack. What used to require lengthy integration projects can now be built in weeks.
“Once you have all these plugs in, you can literally prompt and create agents on a large language model and also create simple front ends. Imagine the power of that.” — Suma Chander
This is what makes AI useful in finance, not just impressive. When the AI can actually reach into NetSuite, QuickBooks, or your reporting layer, the value gets real.
The Real Value Shows Up When End Users Take the Wheel
Chander described a CFO assistant who reached out for help pulling board reports from Power BI. The request wasn’t, “can you send me the data?” – it was, “I want an MCP connection and Claude Desktop on my machine so I can get the data myself.”
“She’s not asking me for the report. She’s asking me for the tool to pull the report herself. I really think that her way of thinking is the way it is going.” — Suma Chander
AI’s real value in finance is giving non-technical users the power to get answers without waiting on a queue.
People and Process Still Drive the ROI
For all the conversation about models, agents, and MCP, both panelists came back to the same point: the technology is the easy part. The hard part is the change management, the governance, and the willingness to standardize processes that have always been bespoke.
“The technology could potentially be the disruptor, but it’s the change management, the processes in place, and how people embrace it that’s going to ultimately drive the ROI.” — Suma Chander
Most firms are blocked more by a lack of structure than a lack of tools.
Move From Noise to ROI
Cutting through the AI noise is one thing. Building a finance function that actually delivers on it is another.
That’s where Consero comes in. We’re an AI-enabled, modular finance partner built for PE-backed and growing companies — combining best-in-class systems, intelligent automation, and expert talent so your team gets faster closes, sharper visibility, and the kind of forward-looking reporting investors are asking for.
If you’re trying to figure out where AI fits in your finance function — or whether your current setup can keep up with what’s coming — let’s talk. It’s a 30-minute conversation on where you stand and what’s possible.
Talk to a Consero finance expert about what a modern, AI-enabled F&A function looks like for your business. We’ll map it out together — it’s 30 minutes, zero pressure.
No sales pitch. Just a roadmap tailored to you.
