In an era where artificial intelligence (AI) is changing the way we work, the question arises: What does AI mean for the economist profession? Is this new technology a threat to jobs, or a powerful tool that will help financial professionals work better? Below, we will examine how AI is impacting the work of economists and accountants, what the main opportunities and risks are, and what practical steps can be taken to adapt to this change. We will also tie this discussion to the concepts of Jim Collins on the advantages of technology in business, to understand whether AI is an enemy or an ally for finance professionals.
What is AI and how does it work?
Artificial Intelligence is a field of technology that enables computers to perform tasks that typically require human intelligence. These tasks include learning from data, pattern recognition, decision-making, and even generating texts or analyses. In simple terms, AI is like an extremely fast and smart digital assistant: it can process large amounts of information in seconds and draw conclusions or make predictions that would take a person hours or days.
An AI program can be trained on millions of financial data points to learn how markets move. Then it can predict future movements or suggest investments based on those patterns. Unlike traditional programs that follow only predefined rules, AI learns from the results: the more data it analyzes, the more accurate it becomes in its predictions.
It's important to understand that AI is not magic or some sentient being – it's simply advanced software. It works thanks to mathematical algorithms and computing power, which enable it to find correlations in data and offer solutions. In fact, AI has already become part of our daily lives: from the apps on our phones to the automatic recommendations on websites.
Why does AI matter to economists?
Economists, accountants, and finance professionals are constantly dealing with data, analysis, and decision-making. AI is of particular importance to them because it can ease the burden of routine tasks and help them focus on the more complex aspects of their work. That's why AI is seen as a very important factor and actor in the financial field:
Fast data processing
AI can analyze thousands of pages of figures, reports, or invoices in seconds. This means that tasks such as daily record-keeping, account reconciliation, or invoice verification can be easily automated. As a result, economists gain valuable time that they can devote elsewhere.
Better predictions
With advanced machine learning algorithms, AI can predict financial or economic trends with greater accuracy based on historical patterns. For example, forecasting cash flows or seasonal sales becomes more reliable when AI models that take hundreds of factors into account are used.
Informed decision-making
By analyzing data, AI can help financial leaders understand the consequences of different decisions. For example, a Chief Financial Officer (CFO) equipped with AI can test how the company would be affected by changes in exchange rates, raw material prices, etc., and make more informed strategic decisions.
Reduction of human errors
Manual processes (e.g., manually recording financial transactions) are often accompanied by errors or discrepancies. AI-driven automation reduces this risk, as the system can automatically detect anomalies or incorrect calculations. This increases the accuracy of financial reports and lowers the likelihood of costly errors.
Essentially, the main opportunity that AI brings is an increase in the productivity and value that economists deliver. By delegating tedious tasks to machines, finance professionals can focus on deeper analysis, strategic advice, and generating insights for businesses.
Some practical examples of using AI in finance
AI is no longer just theory – it's already in action in the financial sector. Many firms, large and small, are integrating artificial intelligence into their workflows. Here are some concrete practices where AI is being used today:
Audit and financial control
Big auditing firms have begun using AI for the automated review of documents. For example, Deloitte has developed an internal platform that analyzes contracts and financial documents using AI., automatically highlighting the key information that auditors need to verify. Also, EY (Ernst & Young) has integrated AI into the audit process – their intelligent system scans unstructured data, such as contracts or invoices, and flags potential risks of fraud or financial misrepresentation.
Accounting automation
For smaller businesses, there are now AI-powered accounting software programs that serve as “accounting assistants.” These programs can scan invoices (using Optical Character Recognition and AI), convert them into structured data and automatically record them in the financial system. Tasks such as processing purchase invoices, recording daily expenses, or reconciling bank accounts are becoming increasingly automated, saving time and reducing errors.
Risk analysis and fraud detection
Banks and financial institutions use AI algorithms to detect suspicious transactions or patterns that indicate fraud. AI can monitor thousands of transactions per minute and automatically flag anything that appears out of the ordinary (e.g., a transaction of an unusual value or in an unusual location for the customer). This greatly helps prevent financial fraud and money laundering.
Virtual assistants and customer service
Many banks and investment firms have implemented AI-powered chatbots to answer customer questions. These virtual assistants can provide basic information about balances, transactions, and even initial investment advice, 24 hours a day. This allows economists and financial advisors to focus on more complex issues while the AI handles routine questions.
Improving internal efficiency
Leading companies in the financial sector are also using AI for internal processes. One example It is PwC, which equipped its employees with a customized version of ChatGPT to help them with tasks such as document compilation and coding. The result was a 20% to 50% increase in productivity in some software engineering processes. This shows that when used properly, AI can significantly accelerate the pace of work in many aspects.
Although many of these practices are initially being implemented by large companies (such as the “Big 4” in auditing), it's worth emphasizing that AI isn't just for the giants. Today there are powerful tools and platforms (some even free or low-cost) that an accounting firm or a small business can use. A significant portion of financial professionals are simply using publicly available tools like ChatGPT for their daily needs, alongside industry-specific tools. This means that the barriers to entry have been lowered – anyone can experiment with AI in their own field without staggering investments.
The risks and challenges of AI for economists
As with any new technology, alongside opportunities, AI also brings risks and challenges that must be carefully managed. Here are some of the key issues that raise questions:
Fear of job replacement
The question that often arises is whether AI will render economists, accountants, or financiers redundant. This fear is not without precedent – decades ago there were similar concerns when personal computers and Excel were introduced. In fact, history shows that such technologies initially replaced some roles but created new ones and increased demand for higher-level skills. For example, after the spread of Excel, the number of people employed as data processors fell, but the number of accountants, auditors, and financial analysts rose significantly by 2000. This shows that AI is expected to transform the role, not eliminate it entirely. However, fear remains a real psychological challenge and must be addressed.
The loss of some traditional duties
We cannot deny that some routine tasks that until yesterday were performed by junior economists or support staff can now be carried out by AI. For example, the initial preparation of financial statements, standard reports, or basic expense checks can be automated. This could reduce the need for entry-level staff in these tasks. The challenge for the profession is redefining the value that the economist brings: the focus will shift to qualitative analysis, data interpretation, and strategic decision-making—things that AI alone cannot do well.
The risk of errors
The expression “garbage in, garbage out” applies strongly to AI. If the data fed into the system are inaccurate or biased, the results will also be incorrect or distorted. An AI model can misinterpret if, for example, certain cases are missing from the data (e.g., an unforeseen economic crisis). Similarly, algorithms can inherit flawed assessments from historical data – for example, they may underestimate women in financial leadership roles if the historical data contain few women, etc. These errors can lead to wrong decisions if there is no human context to correct them. Therefore, human oversight and intervention remain essential.
Ethical issues and privacy
The use of AI in finance also raises ethical questions. It is necessary to ensure that decisions made by AI (e.g., granting a loan to a client or recommending an investment) are fair and transparent. There is a risk that even the users themselves may not fully understand the reasoning of a complex AI model. Also, handling the large volume of financial data raises confidentiality concerns – care must be taken to comply with privacy regulations and ensure that sensitive data is protected.
Non-compliance with legal changes
In the financial and fiscal sectors, laws and regulations change frequently. One challenge is training AI to keep up with rapid legal changes. If an AI model is trained on outdated rules, it may provide recommendations that do not comply with the new law. This requires ongoing maintenance of the AI system and expert intervention to update it with the latest legal information.
It's clear that AI doesn't come without risks, but most of them can be mitigated with the right steps. One of those steps is education—both of the AI systems themselves with high-quality data and of human professionals on how to use AI and its limitations. This brings us to the next question: Should we be afraid of AI, or view it as something fleeting?
Will AI replace economists? (Should we be afraid?)
Fear of the unknown is human, and AI is, to some extent, uncharted territory for many professionals. However, the facts and experts suggest that AI will not completely replace the economist, but will change the way he works. Here are some reasons why the panic may be overblown:
AI is a tool, not a replacement for human reasoning.
Even the most artificially intelligent systems operate within certain limits. They lack human judgment, creativity, or context understanding like an experienced professional. As mentioned earlier in the article, AI can analyze data and provide recommendations, but only a human can fully grasp the big picture, cultural nuances, and political or emotional influences on the economy. A good economist brings intuition and experience – elements that aren't easily coded into an algorithm.
Read also Will artificial intelligence replace the work of an economist?
History of technologies in finance
Many times in the past, technology has been predicted to “eliminate” a profession, and often the opposite happens. The example of ATMs is illustrative: when ATMs were invented, it was said they would eliminate the role of the bank teller. In reality, ATMs lowered the cost of opening bank branches, banks opened more branches and hired even more tellers than before (but with slightly different tasks). The same thing is expected with AI: there will be a shift in tasks, but not the complete disappearance of the role. Maybe we'll have fewer accountants shuffling papers, but we'll have more analysts, financial advisors, and fintech experts.
AI as an assistant, not a competitor
A good way to eliminate fear is to see AI as an ally. The combination of humans and machines yields better results than either humans alone or machines alone. As emphasized, the question is not whether the computer is more accurate than the human, but whether the human, aided by the computer, is more accurate than the human alone – the answer is yes. For example, an economist with an AI tool at their disposal will avoid calculation errors, have more complete information, and thus provide a much higher-quality service than someone without these tools.
Professionals' attitude toward automation
One Global survey It has been found that 89% of employees say that automation (including AI) has made their work easier, and 91% of them admit that it saves them time and gives them a better work-life balance. So, the majority of those already working with automated tools don't want to go back. This shows that once they've experienced the benefits, professionals no longer view AI with fear but with confidence that it makes them more productive and their work more interesting.
Instead of thinking “AI will replace us,” a more useful approach is: “How can we use AI to replace the boring parts of our work?” Because, as has become a meme in professional circles, “AI will not replace economists, but economists who use AI will replace those who don't.”In other words, the greatest risk isn't that you'll be replaced by a machine, but that you might be “replaced” by a colleague or competitor who has embraced technology and is offering more efficient services.
Is AI something entirely new or just the next change?
Economists have become accustomed to rapid changes, especially in legislation and regulation. Every year new tax laws, new reporting standards, new accounting rules, and so on are introduced. Often, economists have to learn and adapt their practices overnight to comply with a new law. Compared to this reality, is AI a greater revolution, or just another change, albeit a technological one?
It can be argued that AI represents a more fundamental change, since it is not just a new rule to be applied, but a completely new way of doing the work. Instead of manually changing his procedures, with AI the economist must learn a new tool, a new work partner. It's more or less like the shift from manual calculations to using a computer and Excel decades ago—a major change. But at the same time, the basic principles of the profession remain the same. The purpose of an economist's work does not change: providing accurate financial information and advising on the best economic decisions. Professional and ethical standards do not change—integrity, care, and critical analysis are the same. In this sense, AI can be seen as another step in the evolution of the tools economists use, comparable to electronic calculators, accounting programs, cloud-based financial software, etc.
What makes AI perhaps more challenging than rapid legal changes is its speed and scale. New laws are issued one by one; AI is developing in many directions simultaneously and exponentially. Therefore, the learning curve for professionals can be steeper. Many new concepts need to be grasped (e.g., what is machine learning, what is deep learning, how a language model like ChatGPT works, etc.). But a good approach is to build on existing capabilities. Just as when a new law is enacted, a true economist reads it, studies it, and incorporates it into practice, the same goes for AI: first you need to build a basic understanding of the technology, then put it into practice in everyday tasks. In other words, the flexibility and adaptability that finance professionals have already gained from facing legal changes is the key weapon for this technological shift as well.
AI is a bigger change than just a new law, but it's not something extraordinary the world has never seen. It's part of the long technological evolution in finance. Those who have survived and thrived professionally—moving from spreadsheets to computers, from national standards to international standards, from paper tax returns to online filings—have every skill needed to survive and thrive in the AI era.
How Economists and Businesses Can Adapt to AI – Practical Tips
To successfully embrace AI, economists and businesses must take proactive steps. Below are some practical tips for integrating AI into daily work and developing the right skills:
Start with small steps (test)
There's no need to transform all your processes at once. Identify a routine task that takes up a lot of your time—for example, processing monthly invoices—and explore an AI tool that does that work. Try it out for a short period as a pilot. This will give you a practical idea of the benefits without much risk.
Invest in training and education.
Just as you take courses for legal changes or professional certifications, take the time to learn the basics of AI. There are plenty of free online courses on “AI for Beginners” or specific training on using tools like Excel with automated formulas., Business Intelligence with AI features, etc. The more informed you are, the more confidently you'll use the technology.
Improve the quality of your data
AI feeds on data. Make sure the financial data you work with (e.g., accounting records, customer databases, etc.) is clean and well-organized. Avoid duplications, correct errors, standardize formats. This “data hygiene” will ensure that any AI tool delivers much more reliable results. Remember: garbage in, garbage out.
Don't disregard professional judgment.
Even once you start using AI regularly, always review the results with a critical eye. Consider AI your advisor, but not your director. If an AI-generated analysis seems odd or counterintuitive to you, investigate it further. Combining your experience with the speed of AI will be the best recipe.
Collaborate with IT departments or external experts.
Developing and maintaining AI solutions may require technical expertise. Don't hesitate to seek help from IT specialists within your organization or from external consultants. They can help you choose the right tool, integrate it with existing systems, and train your staff for optimal use.
Be careful with security and privacy.
When using AI tools, especially cloud-based or third-party ones, think about the security of the financial data you're providing. Read the privacy terms; ensure that confidential information (e.g., payroll lists, business plans) is handled in accordance with regulations. If possible, use enterprise or licensed versions that offer more control over your data than the free public versions.
Create an ethical approach to AI
As a financial professional, uphold ethical principles even when using AI. Ensure you are transparent with clients or management if a recommendation has been influenced by analysis conducted by an AI model. In critical decision-making, use AI as a second opinion, but don't let it replace your professional responsibility.
By following these steps, economists and businesses can gradually benefit from AI, without shocks and with control. The right approach is to be proactive: AI is here to stay, so it's better to start getting to know it and putting it to work for you than to ignore it until it becomes unavoidable.
Technology as an Advantage: What Jim Collins Teaches Us
In his books and studies on successful companies, Jim Collins (the well-known author of “From good to great”, etc.) offers a very insightful observation on the role of technology. He notes that companies that excel do not see technology as an end in itself, but as an accelerator of objectives that They already have them clear.. In other words, technology is an “accelerator”: if an organization has the right people, the right culture, and the right strategy, then technology (whether AI or something else) will accelerate its success. But if these foundations are missing, technology alone cannot make a business great.
How does this relate to economists and AI? According to the Collins principle, a financial consulting firm or an independent professional must first clarify what they excel at, what unique value they offer clients, and ensure they operate with discipline and integrity. Once these things are in place, AI can serve as an additional competitive advantage – it enhances your capabilities, multiplies the impact of your efforts, and increases the speed and reach of your services. But AI does not replace vision or strategy. A business without a clear vision can spend money on expensive AI technology and still fail to achieve results, because it doesn't know what to do with it.
Jim Collins also discovered that successful leaders were curious but cautious about technology. They don't jump on every new trend just because it seems interesting; they assess whether that trend (e.g., implementing AI) aligns with their business model and serves their customers. This is a valuable lesson: AI implementation must be done strategically. The key questions every economist or businessperson should ask are: Which specific problems do I want to solve with AI? How will it improve my customer service? Do I have the capacity to maintain it? By answering these, the technology takes the right direction.
So, Collins's message in our context is: use AI as a tool to better achieve your goals, not as a substitute for them. Technology should serve people and vision, not the other way around. The economist who understands this need not fear AI; on the contrary, they will turn it into a competitive advantage over those who fall behind.
Conclusion
Artificial intelligence is bringing major changes to the profession of the economist, just as it is in every other field. Like any major change, it brings extraordinary opportunities but also challenges to overcome. The good news is that the role of the economist is not doomed to disappear; it is destined to evolve. AI will take over repetitive, routine tasks, but that only gives the modern economist more room to create value—whether through in-depth analysis, strategic consulting, or client communication.
The key is attitude and adaptation: those who embrace technology and invest in their skills will see AI as a powerful ally. Those who remain passive risk being left behind in an industry that is moving forward. But there's no reason to panic – history teaches us that people have coexisted with and benefited from new technologies whenever they were willing to learn and use them wisely.
To illustrate this, we can bring up the analogy mentioned earlier: Galileo, when he used the telescope, did not replace the astronomer's work—on the contrary, he enabled them to see further and understand more. AI is the telescope of the modern economist. By using this “tool” to extend our gaze into the details of data and future forecasts, we can make more informed, faster, and smarter decisions. The important thing is to use it with care, with ethics, and with good intentions.
To wrap up, let's return to Jim Collins's idea: technology, including AI, can be your accelerating force toward success—as long as you put it in service of your objectives rather than let it divert you from them. AI is neither good nor bad in itself; it all depends on how we use it. By staying informed, open to change, and at the same time faithful to the basic principles of our profession, we can turn this technological revolution to our advantage as well as to the advantage of customers and the businesses we advise.