JPMorgan CFO Sees AI Dealmaking Opportunities: Why the Next Wave of AI Investment Could Be Good for Business

JPMorgan CFO Jeremy Barnum remains optimistic about AI-related mergers, acquisitions and financing. I think there’s a compelling reason to share some of that enthusiasm. Artificial intelligence is creating an entirely new investment cycle, and the opportunities extend well beyond Silicon Valley. The interesting question is how companies turn that demand into profitable growth.

If I were an investment banker right now, I’d probably be looking at artificial intelligence with the same enthusiasm a wedding planner feels when someone announces a destination wedding for 400 people.

There’s money to be spent.

Lots of it.

And almost everyone involved needs professional help.

On October 13, 2026, JPMorgan Chase CFO Jeremy Barnum expressed optimism about AI-related dealmaking despite a recent slowdown in initial public offerings.

According to MarketWatch, Barnum sees continued demand for capital across the AI sector and an active pipeline of potential transactions. He acknowledged that market volatility can temporarily create disagreements between buyers and sellers over valuations.

That seems reasonable.

Buyers want a discount.

Sellers remember what their company was supposedly worth six months ago.

Investment bankers spend considerable time trying to persuade everyone that arithmetic is still a shared language.

But what caught my attention wasn’t the temporary slowdown in IPO activity.

It was the continued demand for capital.

Because I think we’re watching something larger than another technology investment cycle.

AI is creating opportunities for mergers, acquisitions, infrastructure financing and corporate expansion across industries that previously had very little connection to artificial intelligence.

And for corporate finance professionals, that creates an interesting set of possibilities.

New revenue streams.

New investment opportunities.

Different ways to finance growth.

And potentially, entirely new business models.

I wouldn’t dismiss that because a few IPOs were delayed.

Why JPMorgan’s AI Dealmaking Outlook Is Worth Watching

JPMorgan has a useful vantage point.

As one of the world’s largest investment banks, it works with companies raising capital, acquiring businesses, financing infrastructure and making major strategic investments.

Its earnings offer a window into corporate investment activity.

The bank reported third-quarter 2026 net income of approximately $15.3 billion, compared with $14.4 billion a year earlier.

Reported revenue reached approximately $50.2 billion.

Its commercial and investment banking business also benefited from stronger dealmaking activity, with merger and acquisition advisory fees contributing to the improvement.

Those figures matter because investment banking activity reflects actual corporate decisions.

Companies don’t hire advisers and arrange financing merely because somebody wrote an enthusiastic LinkedIn post about AI.

At least I hope they don’t.

They do it because management sees an opportunity worth investigating.

A company might need to acquire technology it doesn’t possess.

A data center developer might need financing to expand capacity.

A software business might seek capital to enter new markets.

An established manufacturer might acquire a smaller company with specialized automation capabilities.

Those transactions generate opportunities for banks.

But they also reveal where corporate investment is moving.

That’s what interests me from an FP&A perspective.

Investment banking activity can provide an early indication of which industries are preparing to expand.

And companies that understand those shifts may find opportunities before they become obvious in quarterly financial results.

AI Mergers and Acquisitions Are Creating Opportunities Beyond Technology Companies

When people discuss AI mergers and acquisitions, they often picture a large technology company buying a smaller AI startup.

That certainly happens.

But I think the more interesting opportunity may be the spread of AI capabilities into established industries.

Consider a manufacturing company.

It has spent years improving production efficiency, managing inventory and reducing equipment downtime.

Now it identifies an AI business specializing in predictive maintenance.

The manufacturer could purchase the software.

It could develop similar capabilities internally.

Or it could acquire the company and integrate the technology into its operations.

Each option has different financial implications.

An acquisition might accelerate implementation and provide access to specialized talent.

It might also create a product the manufacturer could eventually sell to other companies.

That’s where the opportunity becomes interesting.

A technology initially purchased to reduce internal costs could develop into an external revenue stream.

The company moves from being a customer of AI technology to owning a capability with commercial value.

I’m not suggesting every manufacturer should start acquiring software companies.

Some organizations have enough difficulty integrating a new expense reporting system.

But I do think CFOs should examine whether AI-related investments could create opportunities beyond internal productivity improvements.

The financial return might come from lower costs.

It might come from higher revenue.

Or it might come from a business the company couldn’t have entered previously.

Those are very different investment cases.

And they deserve separate forecasts.

The AI Investment Boom Could Create an Entirely New Corporate Financing Market

One reason I understand Barnum’s optimism is the sheer variety of financing requirements created by AI infrastructure.

Think about what goes into building and operating a large data center.

Land.

Buildings.

Electrical equipment.

Cooling systems.

Networking infrastructure.

Computing hardware.

Energy supply.

And an impressive amount of engineering work.

Somebody has to finance all of it.

The financing arrangements can involve corporate debt, equity investment, project finance, private credit, equipment leasing and strategic partnerships.

Different assets and revenue arrangements may support different structures.

A company building a facility with long-term contracted customers has a different risk profile from a startup developing a new AI application.

That distinction creates opportunities for financial institutions capable of evaluating and structuring the investments.

It also creates opportunities for companies that provide the underlying products and services.

Electrical equipment manufacturers may see additional demand.

Engineering firms may win new projects.

Energy providers may negotiate long-term supply agreements.

Real estate developers may pursue specialized facilities.

And financial advisers may help companies evaluate acquisitions, partnerships and capital structures.

The AI investment cycle has become much broader than software.

That’s why I wouldn’t evaluate its economic importance solely by looking at the share prices of technology companies.

The capital spending has consequences throughout the economy.

How AI Investment Creates Revenue Opportunities for Other Industries

Let’s imagine a mid-sized industrial company that manufactures electrical components.

Annual revenue is $300 million.

Operating margin is 12%.

The company identifies growing demand from data center construction projects.

Management believes it can enter that market by investing $20 million in additional production capacity.

The new business is expected to generate $35 million in annual revenue once fully operational.

Assume the incremental operating margin reaches 18%.

That produces $6.3 million in annual operating profit.

Now we’re talking about a potentially meaningful investment.

The new business could represent more than 10% of the company’s existing revenue.

And the expected incremental margin is higher than the company’s current operating margin.

I would be interested.

But I’d also want to understand the timing.

How quickly can the new production capacity become operational?

How much customer demand is supported by signed contracts?

Will the company need additional inventory?

What payment terms will customers require?

And can the existing organization support the expansion?

Those questions help turn an attractive market opportunity into a realistic financial plan.

The company might discover that the investment produces strong returns.

It might also find that a partnership or smaller initial expansion delivers a better result.

Either way, AI infrastructure growth has created a strategic option the company may not have considered previously.

That’s the kind of opportunity I think finance should actively investigate.

How CFOs Can Evaluate AI Acquisitions Using Financial Modeling

Let’s put an acquisition example together.

Suppose a company is considering purchasing a specialized AI software business for $120 million.

The target currently generates $20 million in annual revenue and $4 million in EBITDA.

At first glance, the purchase price represents 30 times EBITDA.

That’s a substantial multiple.

The buyer, however, believes it can use its existing customer relationships to expand the acquired company’s sales.

Management forecasts the following:

Financial measure	Current business	Year 3 forecast
Revenue	$20 million	$45 million
EBITDA	$4 million	$13.5 million
EBITDA margin	20%	30%

If the company achieves that forecast, the original $120 million purchase price represents approximately 8.9 times year-three EBITDA.

The acquisition begins to look more attractive.

But I’d want to understand where the growth comes from.

How much is expected from existing customers?

How much comes from the buyer’s distribution network?

What additional sales investment is required?

Are there integration costs?

Does the technology require significant ongoing development?

And when does the acquired business begin generating enough free cash flow to support the purchase price?

The model should also include the time value of money and a realistic terminal value.

I wouldn’t approve the acquisition simply because the year-three EBITDA multiple looks attractive.

Still, the example illustrates why established companies may have an advantage in acquiring AI capabilities.

A small technology business might struggle to reach customers.

A larger company may already have the relationships, distribution channels and operating infrastructure.

Combining those assets can create economic value.

That’s a legitimate reason for mergers and acquisitions.

And one of the reasons I expect AI-related dealmaking to remain interesting.

AI Investment Banking Opportunities Go Beyond IPOs

There’s a tendency to measure the health of investment banking through the IPO market.

IPOs attract attention.

They’re visible.

They produce headlines, valuation debates and occasionally spectacular first-day trading activity.

But the financing needs created by AI extend well beyond taking companies public.

A private company may raise additional equity capital.

An established business may issue debt.

A data center developer may arrange project financing.

A corporation may sell a minority interest in a subsidiary.

Two companies may create a joint venture to share infrastructure costs.

And an AI startup may choose to remain private while expanding through strategic partnerships.

Those arrangements can generate significant advisory and financing activity.

For JPMorgan and other investment banks, that creates several potential revenue streams.

M&A advisory fees.

Debt underwriting.

Equity capital markets activity.

Lending relationships.

Treasury services.

And ongoing financial relationships with growing companies.

That’s why Barnum’s optimism about the broader dealmaking environment makes sense to me.

A temporary slowdown in IPOs doesn’t necessarily mean companies have stopped investing.

Sometimes it means they’re considering other ways to raise capital.

And corporate finance has a remarkable ability to develop alternative structures when someone has an attractive project and insufficient cash.

What AI Dealmaking Means for FP&A Revenue Forecasting

Here’s where I think FP&A teams can contribute something particularly useful.

When a new investment cycle develops, companies often begin with a market opportunity estimate.

The market is expected to grow rapidly.

Management identifies a potential share of that market.

The revenue forecast follows.

I’ve seen enough market-share calculations to know how easily they can become optimistic.

A company looks at a $100 billion market.

Assumes it can capture 1%.

And suddenly there’s a billion-dollar revenue opportunity in the strategic plan.

Very efficient forecasting.

Unfortunately, customers generally expect somebody to sell them something first.

I’d build the forecast from operating drivers.

For a company supplying AI infrastructure, I’d start with expected projects, customer contracts, production capacity and delivery schedules.

For an AI software company, I’d examine customer acquisition, retention, pricing and implementation capacity.

For an investment bank, I’d look at transaction pipelines, expected completion dates, financing volumes and fee economics.

The objective is to connect market opportunity to measurable business activity.

That doesn’t mean the forecast has to be conservative.

A company with strong evidence of demand should reflect that opportunity.

But the assumptions need to explain how the business will actually generate the revenue.

Optimism is perfectly acceptable in financial planning.

I just prefer mine with supporting schedules.

A Practical Example of Forecasting AI-Related Deal Revenue

Suppose an investment banking team is pursuing five AI-related transactions.

Each transaction has a different fee opportunity and expected closing probability.

For illustration:

Transaction	Potential fee	Closing probability	Weighted fee
AI software acquisition	$8 million	80%	$6.4 million
Data center financing	$6 million	75%	$4.5 million
AI company IPO	$12 million	40%	$4.8 million
Infrastructure joint venture	$5 million	70%	$3.5 million
Technology acquisition	$9 million	60%	$5.4 million
Total	$40 million		$24.6 million

The potential fee opportunity is $40 million.

The probability-weighted forecast is $24.6 million.

That’s a useful starting point.

But I’d go further.

I’d examine when each transaction is expected to close.

I’d distinguish advisory fees from underwriting fees.

I’d consider how transaction size affects pricing.

And I’d account for the fact that several deals may depend on the same financing environment.

The probabilities are illustrative, not market statistics.

I wouldn’t use them without examining actual pipeline history and transaction-specific evidence.

The interesting part is what happens when the bank improves its conversion rate.

Suppose better execution, stronger client relationships and improved market conditions increase the expected closing probabilities.

The bank can generate additional revenue without increasing the number of opportunities.

That’s a valuable operating insight.

And it suggests that forecasting dealmaking revenue should involve more than predicting how many transactions enter the pipeline.

Conversion matters.

Timing matters.

Fee realization matters.

Those are familiar FP&A concepts.

The underlying market happens to involve artificial intelligence.

AI Could Make Investment Banking More Productive Too

There’s another opportunity I find interesting.

Investment banks aren’t simply financing companies that use AI.

They can also use AI to improve their own operations.

Consider the work involved in preparing a transaction.

Financial analysis.

Market research.

Comparable company valuations.

Due diligence.

Document review.

Presentation preparation.

Financial modeling.

A significant amount of that work involves collecting, organizing and analyzing information.

AI could help bankers perform some of those activities faster.

That creates an interesting operating model.

If a team can complete certain analytical tasks more efficiently, it may be able to support additional transactions without increasing headcount proportionately.

Or it may spend more time on the work that requires human judgment.

Negotiating terms.

Understanding client objectives.

Evaluating transaction structures.

Identifying strategic opportunities.

Those activities can be difficult to automate.

I can imagine AI becoming particularly useful in the early stages of transaction analysis.

A banker evaluating an acquisition could use it to organize financial disclosures, compare operating metrics and identify questions for due diligence.

The human team would still need to verify the information and evaluate the conclusions.

But the preparation process could become more efficient.

That could improve productivity.

And potentially profitability.

Although I’d still want someone to check the formulas before presenting the valuation to a client.

Some traditions are worth preserving.

Why AI Partnerships May Be as Important as Acquisitions

Not every company needs to buy an AI business.

Sometimes the better financial decision is a partnership.

Suppose a logistics company wants to improve route optimization using AI.

It could develop the technology internally.

It could acquire a specialized software provider.

Or it could enter a commercial partnership.

The partnership might require less upfront capital.

It could also allow the company to test the technology before making a larger commitment.

That’s valuable when the market is changing quickly.

I think we’ll see more companies examining joint ventures, licensing arrangements and strategic investments as alternatives to outright acquisitions.

Those structures can help companies share costs and access capabilities they don’t possess.

But the financial terms matter.

Who owns the intellectual property?

Who receives the revenue?

How are development costs allocated?

What happens if the partnership succeeds?

Can either party terminate the arrangement?

And who pays for additional investment?

These aren’t glamorous questions.

They’re the questions that determine whether a promising partnership produces attractive economics.

A strategic alliance can be an excellent way to enter a market.

It can also become a complicated arrangement where everyone agrees the opportunity is enormous and nobody agrees who should pay the next invoice.

I’d prefer to settle that before signing.

The Real Opportunity for CFOs Is Better Capital Allocation

When I think about AI-related dealmaking, I keep coming back to capital allocation.

Companies have several choices.

They can invest in their existing operations.

Develop new products.

Acquire businesses.

Enter partnerships.

Expand infrastructure.

Or return capital to shareholders.

AI creates additional opportunities within those choices.

The CFO’s job is to help determine which investments deserve funding.

And I think that role becomes more valuable when an industry is changing rapidly.

Consider a company with $100 million available for strategic investment.

Management identifies three opportunities.

An internal AI automation program requiring $15 million.

A specialized technology acquisition requiring $60 million.

And a new AI-enabled service line requiring $25 million.

The investments compete for capital.

But they also have different risk profiles, timing and strategic benefits.

I’d evaluate each using a combination of financial returns, execution requirements and strategic flexibility.

The internal automation program might offer relatively predictable cost reductions.

The acquisition could provide immediate access to customers and technology.

The new service line might create the largest long-term revenue opportunity but require more time to develop.

The highest projected return isn’t automatically the best investment.

The company needs to consider its capabilities, liquidity and other commitments.

That’s where FP&A can help management compare opportunities on a consistent basis.

And it’s where the excitement surrounding AI becomes something useful.

An actual investment decision.

What I’d Watch in JPMorgan’s AI Dealmaking Outlook

I think Barnum’s comments are encouraging.

The continued demand for AI capital suggests that companies remain willing to invest in technology, infrastructure and expansion.

That activity creates opportunities for investment banks and the businesses supporting the AI economy.

But I’d watch several indicators before deciding how much growth to put into a financial forecast.

First, completed transactions.

An active pipeline is encouraging, but completed deals generate the fees and establish market valuations.

Second, financing activity.

Are companies successfully raising the capital they need?

Are financing structures changing?

Are lenders and investors supporting new projects?

Third, the spread of AI investment beyond the largest technology companies.

I find that particularly interesting.

If manufacturers, logistics companies, healthcare businesses and financial services firms begin making larger AI-related investments, the opportunity becomes broader.

Fourth, the economics of the investments themselves.

Are companies generating measurable revenue and operating improvements from AI?

That will influence how much additional capital they’re willing to commit.

I wouldn’t expect every indicator to move in the same direction.

Some markets will grow faster than others.

Some transactions will be delayed.

And some investment strategies will work better than expected.

That’s normal.

The opportunity doesn’t require every company to succeed.

It requires enough genuine economic value to support continued investment.

I Think AI Dealmaking Could Become One of the More Interesting Corporate Finance Stories

I understand why JPMorgan’s CFO remains optimistic.

AI is creating new financing needs, acquisition opportunities and business models.

And those developments are spreading across industries.

Investment banks have opportunities to advise companies, arrange capital and support transactions.

Established businesses have opportunities to acquire capabilities, enter new markets and develop new revenue streams.

FP&A teams have opportunities to help management evaluate those investments and understand the financial consequences.

I find that genuinely interesting.

Not every investment will work.

Of course it won’t.

But that doesn’t make the broader opportunity less significant.

The important question is how companies identify the investments that fit their strategy and financial capacity.

And I think finance professionals should be involved early.

Before the acquisition target has been selected.

Before the financing structure has been finalized.

Before somebody announces that the company is entering a $100 billion market and only needs to capture 1% of it.

Because AI may create extraordinary business opportunities.

But someone still has to turn those opportunities into revenue, profit and cash flow.

That’s where the CFO and FP&A team come in.

And if JPMorgan’s outlook proves correct, there may be quite a few interesting deals for them to evaluate.

Personally, I’d welcome that.

Corporate finance is considerably more interesting when we’re helping businesses decide where to grow.

Although I would still insist on seeing the financial model.

Some things never change.