AI & Innovation

AI is changing how we work, invest and make decisions. But using more AI does not automatically mean better results. The real question is simpler: where can AI save time, improve a decision or help people see something they could not see before?

At Sheyconomics, we look at AI through a business lens. We connect it with strategy, investment, finance, M&A, operations and procurement. The goal is not AI for the sake of AI. It is useful AI that solves a real problem and creates measurable value.

Where are we today?

AI has moved very quickly from experiments into everyday work. Stanford’s 2025 AI Index reported that 78% of surveyed organizations used AI in 2024, compared with 55% one year earlier. The World Economic Forum found that 86% of employers expect AI and information processing technologies to transform their business by 2030.

We can already see it in simple tasks. A team can summarize a long report in minutes. An investor can organize information from hundreds of companies. A procurement team can find patterns across thousands of suppliers. A finance team can spot unusual movements in its numbers earlier.

But having the technology is only the beginning.

So, what is the real problem?

Many companies start with the tool instead of the problem. They buy software, launch pilots and ask teams to use AI, but they do not always change the way the work is done.

Imagine a team that spends five hours every week preparing the same report. Adding AI may reduce that to one hour. Great. But perhaps the report itself is no longer useful. Automating it faster does not solve the bigger problem.

That is why we start with a few basic questions. What are we trying to improve? Who uses the result? What takes too much time today? Where are mistakes happening? What information is missing? And what should always remain a human decision?

What could AI actually do?

Investment and Finance

A VC or private equity team may receive hundreds of decks and opportunities. AI can help organize them, extract key information, compare companies with the investment thesis and flag what is missing. Instead of spending the first hour copying information into a spreadsheet, the investor can spend that hour asking better questions.

Another example is portfolio monitoring. AI can bring together revenue, cash, hiring, sales and operating data from several portfolio companies and flag an unusual change. It does not decide whether a company is in trouble. It helps the team know where to look first.

M&A and Due Diligence

During a deal, teams may review hundreds of contracts, financial files, customer documents and management reports. AI can help organize the information, compare documents, find missing clauses or unusual numbers and prepare questions for the team.

For example, imagine reviewing 300 customer contracts. Instead of opening every document just to find renewal dates, pricing terms or termination clauses, AI can help create a first structured view. The legal, financial and investment teams then focus on the points that really need judgment.

Operations and Procurement

A global company may work with thousands of suppliers across different countries and systems. AI can help classify spend, compare suppliers, review contracts, identify concentration risk and detect unusual price changes.

Think about a company buying the same material through different business units. One team may be paying more without knowing it. AI can help connect the data and show the difference. The procurement team can then decide whether to renegotiate, consolidate suppliers or change the sourcing strategy.

Strategy and Market Intelligence

A company considering a new country or market needs information on competitors, customers, regulation, prices and local trends. AI can help collect and structure that information faster and compare different scenarios.

For example, a European company considering expansion into Latin America could compare several markets using the same criteria: market size, growth, competition, regulation, cost and potential partners. AI makes the research faster. Management still decides where, when and whether to enter.

Everyday Business

Some of the best uses are not complicated. A sales team can prepare for client meetings faster. A finance team can explain why actual results differ from budget. A project manager can turn meeting notes into actions. A company can search its own internal knowledge instead of asking the same questions again and again.

Small improvements across hundreds of decisions can become a meaningful business advantage.

What could this look like by 2030?

By 2030, we may talk less about an “AI project” because AI will increasingly sit inside normal business tools and workflows.

A buyer could have an AI assistant that watches supplier prices and risks. An investment team could have one that follows portfolio performance and prepares a first view of new opportunities. A CEO could ask a question about the business and receive an answer built from finance, sales and operating data. Employees could spend less time searching, copying and formatting information.

PwC’s 2025 Global AI Jobs Barometer found that industries more exposed to AI were seeing three times higher growth in revenue per employee than less exposed industries. It also found that the skills requested by employers were changing 66% faster in roles more exposed to AI.

This does not mean every company will see the same result. But it suggests something important: the advantage may come less from simply having AI and more from learning how to redesign work around it.

Let’s see a case study

Imagine a mid market investment firm. The team receives opportunities through emails, decks, introductions, calls and data rooms. Every analyst has a slightly different way of reviewing them, and a lot of time is spent moving information from one place to another.

The firm creates one simple AI assisted workflow. New opportunities are organized in the same format. Key company, market and financial information is extracted. The opportunity is compared with the fund’s investment criteria. Missing information is highlighted. A first review is prepared for the analyst.

The analyst checks everything, challenges the assumptions and adds the human view before the opportunity goes any further.

What changes? The firm can review more opportunities in a consistent way. Analysts spend less time copying data and more time speaking with founders, understanding markets and testing the investment case.

This is a simple example. The same idea could work in procurement, finance, corporate strategy, M&A or portfolio operations.

A few more examples

For an LP: organize fund reports, compare portfolio exposure and prepare questions before a GP meeting.

For a GP: screen deal flow, follow portfolio KPIs and make investment research easier to search.

For a founder: understand customer feedback, prepare market research, improve forecasting and keep investor information organized.

For a corporate team: compare markets, monitor competitors, prepare management reports and identify operating issues earlier.

For procurement: understand spend, compare suppliers, review contracts and identify savings opportunities.

Start small. Learn. Then scale.

You do not need to transform the whole company on day one. Start with one real problem. Understand the current process. Choose what AI should help with. Keep a person responsible for the decision. Test the new workflow with real users and measure whether it is actually better.

If it saves time, improves quality or helps people make better decisions, scale it. If it does not, change it or stop.

How Sheyconomics can help

Sheyconomics connects AI with the way businesses and investors actually work. We bring together strategy, economics, investment, M&A, operations, procurement and data to identify where AI can make a practical difference.

That can mean finding the right use cases, improving a workflow, testing a pilot, building a decision framework, defining the right KPIs or helping a team move from an idea to something it can actually use.

Start with the decision, not the tool.

If you are exploring how AI could help an investment process, a company or an operating team, Sheyconomics can help turn that question into a practical plan.

Sources: Stanford HAI, AI Index Report 2025. World Economic Forum, Future of Jobs Report 2025. PwC, Global AI Jobs Barometer 2025.