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Critical Thinking: A Model of Intelligence for Solving Real-World Problems

Diane f. halpern.

1 Department of Psychology, Claremont McKenna College, Emerita, Altadena, CA 91001, USA

Dana S. Dunn

2 Department of Psychology, Moravian College, Bethlehem, PA 18018, USA; ude.naivarom@nnud

Most theories of intelligence do not directly address the question of whether people with high intelligence can successfully solve real world problems. A high IQ is correlated with many important outcomes (e.g., academic prominence, reduced crime), but it does not protect against cognitive biases, partisan thinking, reactance, or confirmation bias, among others. There are several newer theories that directly address the question about solving real-world problems. Prominent among them is Sternberg’s adaptive intelligence with “adaptation to the environment” as the central premise, a construct that does not exist on standardized IQ tests. Similarly, some scholars argue that standardized tests of intelligence are not measures of rational thought—the sort of skill/ability that would be needed to address complex real-world problems. Other investigators advocate for critical thinking as a model of intelligence specifically designed for addressing real-world problems. Yes, intelligence (i.e., critical thinking) can be enhanced and used for solving a real-world problem such as COVID-19, which we use as an example of contemporary problems that need a new approach.

1. Introduction

The editors of this Special Issue asked authors to respond to a deceptively simple statement: “How Intelligence Can Be a Solution to Consequential World Problems.” This statement holds many complexities, including how intelligence is defined and which theories are designed to address real-world problems.

2. The Problem with Using Standardized IQ Measures for Real-World Problems

For the most part, we identify high intelligence as having a high score on a standardized test of intelligence. Like any test score, IQ can only reflect what is on the given test. Most contemporary standardized measures of intelligence include vocabulary, working memory, spatial skills, analogies, processing speed, and puzzle-like elements (e.g., Wechsler Adult Intelligence Scale Fourth Edition; see ( Drozdick et al. 2012 )). Measures of IQ correlate with many important outcomes, including academic performance ( Kretzschmar et al. 2016 ), job-related skills ( Hunter and Schmidt 1996 ), reduced likelihood of criminal behavior ( Burhan et al. 2014 ), and for those with exceptionally high IQs, obtaining a doctorate and publishing scholarly articles ( McCabe et al. 2020 ). Gottfredson ( 1997, p. 81 ) summarized these effects when she said the “predictive validity of g is ubiquitous.” More recent research using longitudinal data, found that general mental abilities and specific abilities are good predictors of several work variables including job prestige, and income ( Lang and Kell 2020 ). Although assessments of IQ are useful in many contexts, having a high IQ does not protect against falling for common cognitive fallacies (e.g., blind spot bias, reactance, anecdotal reasoning), relying on biased and blatantly one-sided information sources, failing to consider information that does not conform to one’s preferred view of reality (confirmation bias), resisting pressure to think and act in a certain way, among others. This point was clearly articulated by Stanovich ( 2009, p. 3 ) when he stated that,” IQ tests measure only a small set of the thinking abilities that people need.”

3. Which Theories of Intelligence Are Relevant to the Question?

Most theories of intelligence do not directly address the question of whether people with high intelligence can successfully solve real world problems. For example, Grossmann et al. ( 2013 ) cite many studies in which IQ scores have not predicted well-being, including life satisfaction and longevity. Using a stratified random sample of Americans, these investigators found that wise reasoning is associated with life satisfaction, and that “there was no association between intelligence and well-being” (p. 944). (critical thinking [CT] is often referred to as “wise reasoning” or “rational thinking,”). Similar results were reported by Wirthwein and Rost ( 2011 ) who compared life satisfaction in several domains for gifted adults and adults of average intelligence. There were no differences in any of the measures of subjective well-being, except for leisure, which was significantly lower for the gifted adults. Additional research in a series of experiments by Stanovich and West ( 2008 ) found that participants with high cognitive ability were as likely as others to endorse positions that are consistent with their biases, and they were equally likely to prefer one-sided arguments over those that provided a balanced argument. There are several newer theories that directly address the question about solving real-world problems. Prominent among them is Sternberg’s adaptive intelligence with “adaptation to the environment” as the central premise, a construct that does not exist on standardized IQ tests (e.g., Sternberg 2019 ). Similarly, Stanovich and West ( 2014 ) argue that standardized tests of intelligence are not measures of rational thought—the sort of skill/ability that would be needed to address complex real-world problems. Halpern and Butler ( 2020 ) advocate for CT as a useful model of intelligence for addressing real-world problems because it was designed for this purpose. Although there is much overlap among these more recent theories, often using different terms for similar concepts, we use Halpern and Butler’s conceptualization to make our point: Yes, intelligence (i.e., CT) can be enhanced and used for solving a real-world problem like COVID-19.

4. Critical Thinking as an Applied Model for Intelligence

One definition of intelligence that directly addresses the question about intelligence and real-world problem solving comes from Nickerson ( 2020, p. 205 ): “the ability to learn, to reason well, to solve novel problems, and to deal effectively with novel problems—often unpredictable—that confront one in daily life.” Using this definition, the question of whether intelligent thinking can solve a world problem like the novel coronavirus is a resounding “yes” because solutions to real-world novel problems are part of his definition. This is a popular idea in the general public. For example, over 1000 business managers and hiring executives said that they want employees who can think critically based on the belief that CT skills will help them solve work-related problems ( Hart Research Associates 2018 ).

We define CT as the use of those cognitive skills or strategies that increase the probability of a desirable outcome. It is used to describe thinking that is purposeful, reasoned, and goal directed--the kind of thinking involved in solving problems, formulating inferences, calculating likelihoods, and making decisions, when the thinker is using skills that are thoughtful and effective for the particular context and type of thinking task. International surveys conducted by the OECD ( 2019, p. 16 ) established “key information-processing competencies” that are “highly transferable, in that they are relevant to many social contexts and work situations; and ‘learnable’ and therefore subject to the influence of policy.” One of these skills is problem solving, which is one subset of CT skills.

The CT model of intelligence is comprised of two components: (1) understanding information at a deep, meaningful level and (2) appropriate use of CT skills. The underlying idea is that CT skills can be identified, taught, and learned, and when they are recognized and applied in novel settings, the individual is demonstrating intelligent thought. CT skills include judging the credibility of an information source, making cost–benefit calculations, recognizing regression to the mean, understanding the limits of extrapolation, muting reactance responses, using analogical reasoning, rating the strength of reasons that support and fail to support a conclusion, and recognizing hindsight bias or confirmation bias, among others. Critical thinkers use these skills appropriately, without prompting, and usually with conscious intent in a variety of settings.

One of the key concepts in this model is that CT skills transfer in appropriate situations. Thus, assessments using situational judgments are needed to assess whether particular skills have transferred to a novel situation where it is appropriate. In an assessment created by the first author ( Halpern 2018 ), short paragraphs provide information about 20 different everyday scenarios (e.g., A speaker at the meeting of your local school board reported that when drug use rises, grades decline; so schools need to enforce a “war on drugs” to improve student grades); participants provide two response formats for every scenario: (a) constructed responses where they respond with short written responses, followed by (b) forced choice responses (e.g., multiple choice, rating or ranking of alternatives) for the same situations.

There is a large and growing empirical literature to support the assertion that CT skills can be learned and will transfer (when taught for transfer). See for example, Holmes et al. ( 2015 ), who wrote in the prestigious Proceedings of the National Academy of Sciences , that there was “significant and sustained improvement in students’ critical thinking behavior” (p. 11,199) for students who received CT instruction. Abrami et al. ( 2015, para. 1 ) concluded from a meta-analysis that “there are effective strategies for teaching CT skills, both generic and content specific, and CT dispositions, at all educational levels and across all disciplinary areas.” Abrami et al. ( 2008, para. 1 ), included 341 effect sizes in a meta-analysis. They wrote: “findings make it clear that improvement in students’ CT skills and dispositions cannot be a matter of implicit expectation.” A strong test of whether CT skills can be used for real-word problems comes from research by Butler et al. ( 2017 ). Community adults and college students (N = 244) completed several scales including an assessment of CT, an intelligence test, and an inventory of real-life events. Both CT scores and intelligence scores predicted individual outcomes on the inventory of real-life events, but CT was a stronger predictor.

Heijltjes et al. ( 2015, p. 487 ) randomly assigned participants to either a CT instruction group or one of six other control conditions. They found that “only participants assigned to CT instruction improved their reasoning skills.” Similarly, when Halpern et al. ( 2012 ) used random assignment of participants to either a learning group where they were taught scientific reasoning skills using a game format or a control condition (which also used computerized learning and was similar in length), participants in the scientific skills learning group showed higher proportional learning gains than students who did not play the game. As the body of additional supportive research is too large to report here, interested readers can find additional lists of CT skills and support for the assertion that these skills can be learned and will transfer in Halpern and Dunn ( Forthcoming ). There is a clear need for more high-quality research on the application and transfer of CT and its relationship to IQ.

5. Pandemics: COVID-19 as a Consequential Real-World Problem

A pandemic occurs when a disease runs rampant over an entire country or even the world. Pandemics have occurred throughout history: At the time of writing this article, COVID-19 is a world-wide pandemic whose actual death rate is unknown but estimated with projections of several million over the course of 2021 and beyond ( Mega 2020 ). Although vaccines are available, it will take some time to inoculate most or much of the world’s population. Since March 2020, national and international health agencies have created a list of actions that can slow and hopefully stop the spread of COVID (e.g., wearing face masks, practicing social distancing, avoiding group gatherings), yet many people in the United States and other countries have resisted their advice.

Could instruction in CT encourage more people to accept and comply with simple life-saving measures? There are many possible reasons to believe that by increasing citizens’ CT abilities, this problematic trend can be reversed for, at least, some unknown percentage of the population. We recognize the long history of social and cognitive research showing that changing attitudes and behaviors is difficult, and it would be unrealistic to expect that individuals with extreme beliefs supported by their social group and consistent with their political ideologies are likely to change. For example, an Iranian cleric and an orthodox rabbi both claimed (separately) that the COVID-19 vaccine can make people gay ( Marr 2021 ). These unfounded opinions are based on deeply held prejudicial beliefs that we expect to be resistant to CT. We are targeting those individuals who beliefs are less extreme and may be based on reasonable reservations, such as concern about the hasty development of the vaccine and the lack of long-term data on its effects. There should be some unknown proportion of individuals who can change their COVID-19-related beliefs and actions with appropriate instruction in CT. CT can be a (partial) antidote for the chaos of the modern world with armies of bots creating content on social media, political and other forces deliberately attempting to confuse issues, and almost all media labeled “fake news” by social influencers (i.e., people with followers that sometimes run to millions on various social media). Here, are some CT skills that could be helpful in getting more people to think more critically about pandemic-related issues.

Reasoning by Analogy and Judging the Credibility of the Source of Information

Early communications about the ability of masks to prevent the spread of COVID from national health agencies were not consistent. In many regions of the world, the benefits of wearing masks incited prolonged and acrimonious debates ( Tang 2020 ). However, after the initial confusion, virtually all of the global and national health organizations (e.g., WHO, National Health Service in the U. K., U. S. Centers for Disease Control and Prevention) endorse masks as a way to slow the spread of COVID ( Cheng et al. 2020 ; Chu et al. 2020 ). However, as we know, some people do not trust governmental agencies and often cite the conflicting information that was originally given as a reason for not wearing a mask. There are varied reasons for refusing to wear a mask, but the one most often cited is that it is against civil liberties ( Smith 2020 ). Reasoning by analogy is an appropriate CT skill for evaluating this belief (and a key skill in legal thinking). It might be useful to cite some of the many laws that already regulate our behavior such as, requiring health inspections for restaurants, setting speed limits, mandating seat belts when riding in a car, and establishing the age at which someone can consume alcohol. Individuals would be asked to consider how the mandate to wear a mask compares to these and other regulatory laws.

Another reason why some people resist the measures suggested by virtually every health agency concerns questions about whom to believe. Could training in CT change the beliefs and actions of even a small percentage of those opposed to wearing masks? Such training would include considering the following questions with practice across a wide domain of knowledge: (a) Does the source have sufficient expertise? (b) Is the expertise recent and relevant? (c) Is there a potential for gain by the information source, such as financial gain? (d) What would the ideal information source be and how close is the current source to the ideal? (e) Does the information source offer evidence that what they are recommending is likely to be correct? (f) Have you traced URLs to determine if the information in front of you really came from the alleged source?, etc. Of course, not everyone will respond in the same way to each question, so there is little likelihood that we would all think alike, but these questions provide a framework for evaluating credibility. Donovan et al. ( 2015 ) were successful using a similar approach to improve dynamic decision-making by asking participants to reflect on questions that relate to the decision. Imagine the effect of rigorous large-scale education in CT from elementary through secondary schools, as well as at the university-level. As stated above, empirical evidence has shown that people can become better thinkers with appropriate instruction in CT. With training, could we encourage some portion of the population to become more astute at judging the credibility of a source of information? It is an experiment worth trying.

6. Making Cost—Benefit Assessments for Actions That Would Slow the Spread of COVID-19

Historical records show that refusal to wear a mask during a pandemic is not a new reaction. The epidemic of 1918 also included mandates to wear masks, which drew public backlash. Then, as now, many people refused, even when they were told that it was a symbol of “wartime patriotism” because the 1918 pandemic occurred during World War I ( Lovelace 2020 ). CT instruction would include instruction in why and how to compute cost–benefit analyses. Estimates of “lives saved” by wearing a mask can be made meaningful with graphical displays that allow more people to understand large numbers. Gigerenzer ( 2020 ) found that people can understand risk ratios in medicine when the numbers are presented as frequencies instead of probabilities. If this information were used when presenting the likelihood of illness and death from COVID-19, could we increase the numbers of people who understand the severity of this disease? Small scale studies by Gigerenzer have shown that it is possible.

Analyzing Arguments to Determine Degree of Support for a Conclusion

The process of analyzing arguments requires that individuals rate the strength of support for and against a conclusion. By engaging in this practice, they must consider evidence and reasoning that may run counter to a preferred outcome. Kozyreva et al. ( 2020 ) call the deliberate failure to consider both supporting and conflicting data “deliberate ignorance”—avoiding or failing to consider information that could be useful in decision-making because it may collide with an existing belief. When applied to COVID-19, people would have to decide if the evidence for and against wearing a face mask is a reasonable way to stop the spread of this disease, and if they conclude that it is not, what are the costs and benefits of not wearing masks at a time when governmental health organizations are making them mandatory in public spaces? Again, we wonder if rigorous and systematic instruction in argument analysis would result in more positive attitudes and behaviors that relate to wearing a mask or other real-world problems. We believe that it is an experiment worth doing.

7. Conclusions

We believe that teaching CT is a worthwhile approach for educating the general public in order to improve reasoning and motivate actions to address, avert, or ameliorate real-world problems like the COVID-19 pandemic. Evidence suggests that CT can guide intelligent responses to societal and global problems. We are NOT claiming that CT skills will be a universal solution for the many real-world problems that we confront in contemporary society, or that everyone will substitute CT for other decision-making practices, but we do believe that systematic education in CT can help many people become better thinkers, and we believe that this is an important step toward creating a society that values and practices routine CT. The challenges are great, but the tools to tackle them are available, if we are willing to use them.

Author Contributions

Conceptualization, D.F.H. and D.S.D.; resources, D.F.H.; data curation, writing—original draft preparation, D.F.H.; writing—review and editing, D.F.H. and D.S.D. All authors have read and agreed to the published version of the manuscript.

This research received no external funding.

Institutional Review Board Statement

No IRB Review.

Informed Consent Statement

No Informed Consent.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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How Leaders Should Think Critically

• John Baldoni

If you want to succeed in 21st Century business you need to become a critical thinker. Roger Martin of the Rotman School of Management figured this out a decade ago and as dean, has been working to transform his school’s business curriculum with greater emphasis on critical thinking skills. As Lane Wallace explained in the […]

If you want to succeed in 21st Century business you need to become a critical thinker. Roger Martin of the Rotman School of Management figured this out a decade ago and as dean, has been working to transform his school’s business curriculum with greater emphasis on critical thinking skills. As Lane Wallace explained in the New York Times , what Martin and many others are seeking to do is approach learning and problem solving from a multicultural platform that borrows from academia, business, the arts and even history.

• John Baldoni is an internationally recognized executive coach and leadership educator. His most recent book is MOXIE: The Secret to Bold and Gutsy Leadership .

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The business of critical thinking

Dr Tom Chatfield, a philosopher and author, on how teams can frame the questions worth asking

Dr Tom Chatfield, a tech philosopher, is the founder of our course, Critical thinking : problem-solving and decision-making in a complex world. Over the two-week programme, he outlines the importance of stress-testing ideas and cultivating constructive doubt. Here, speaking to Economist Education, Chatfield explains why critical thinking is an increasingly vital skill in business. The text has been edited for length and clarity.

Economist Education: Why is critical thinking important?

Tom Chatfield: Critical thinking is an essential skill because it uses human cognitive strengths and addresses our weaknesses. In an age dominated by algorithms, we are surrounded by increasingly intelligent systems that provide us with answers and information. However, only people can generate and frame the questions worth asking. It is more important than ever that people use these faculties to resist manipulation, disinformation and background noise and focus on the questions that matter.

Economist Education: What is the danger of thinking uncritically?

Tom Chatfield: The danger is that we fail to develop plans and ideas that work in the real world. We may adopt an oversimplified model or set of assumptions and, when unexpected events occur, these assumptions prove inadequate or their premises turn out to be false.

Critical thinking involves recognising complexity and testing ideas. It is a vital skill in planning for disruption and change, and can unlock value and insight beyond the existing information we have.

Economist Education: How can critical-thinking skills help people become better business leaders?

Tom Chatfield: Critical thinking is an essential leadership skill. It involves knowing how to maximise both human and digital resources. This means asking meaningful questions of big-data systems, algorithms and artificial intelligence. It also means harnessing imagination, creativity and teamwork, rather than treating people like machines and limiting their potential. 

Economist Education: How can these skills help teams function more effectively?

Tom Chatfield: Critical thinking emphasises the value of constructive disagreement and evidence-based collaboration—it is a team effort. You need to capture the benefits of diverse perspectives rather than automatically favouring certain ideas or theories. You must engage rigorously with the complexities of human talent and vulnerability, examining a problem from various angles while seeking truth, rigour and evidence. 

Teams often fall into groupthink, with blind spots and assumptions leading them astray. By applying the techniques of critical thinking and prioritising reason and argument over emotion, we can avoid these pitfalls. The aim should be to maximise collective potential, rather than allowing certain feelings, individuals and narratives to dominate.

Economist Education: What are some of the key takeaways from this course?

Tom Chatfield: They include understanding that critical thinking is not about perfect insights or being perfectly rational. It is an ongoing negotiation with your limitations. It invites you to reflect on your strengths and weaknesses and, with others, identify biases and oversights.

This approach is not a guaranteed recipe for success but a way of working, thinking and collaborating based on a deeper understanding of human cognition, and applying that to the challenges of the 21st century.

If you’re interested in exploring Economist Education’s critical-thinking course, click here .

Find out more on this topic in our course...

Critical thinking and decision-making

In an algorithm-fuelled world, the ability to interrogate assumptions and reason with rigour is essential, both in business and in life. Designed by The Economist ’s journalists and leading critical-thinking experts, this two-week online course will equip you with the tools to avoid cognitive bias, reframe complex problems and capitalise on your human advantage. You’ll gain the practical techniques and skills to use data discerningly and strengthen decisions through self-reflection, with a case study from The Economist ’s newsroom.

10 Skills to Learn to Become a Business Intelligence Analyst

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In this era of advanced technology, the growth of businesses lies in accurately predicting and forecasting business patterns. Your ability to study data and rising trends to tell what's attainable in a company is a huge part of business intelligence.

A business intelligence (BI) analyst works with companies to examine competitors and understand market seasons through data analysis. So, it's a plus if you understand business patterns and have strong consulting abilities. If you want to scale through, here are ten skills to learn to be a business intelligence analyst.

1. Data Mining

A core responsibility of a business intelligence analyst is data visualization, which is only possible if you understand how to extract and mine data. A data mining skill would help you understand where to extract data relevant to the company you're working for.

Normally, data is always available in large data sets, but with data mining skills, you can extract useful information, clean it up and make it available. The three basic methods of data mining are classification, clustering, and association.

During data classification, you divide large data sets into categories that include public data, confidential data, local data, and internal-only data. The second part of data mining is data clustering. Say you extracted public data. You have to further divide them according to their similarities.

Finally, in data association, you detect the relationship between the data you extracted and what makes them so similar. That said, you realize why having data mining skills is important to business intelligence because it forms the bedrock of business analysis.

2. Data Preparation

Once you understand how to mine data, learning how to prepare this data is next. Even though data mining requires that you put similar data together, you still need to organize and structure the extracted data. You want to turn that raw data into analytics-ready data because you can only use organized and structured data in business intelligence.

You'll also need to be proficient in the data structures that all programmers should know because business intelligence requires some programming knowledge. As part of data preparation skills, you need to learn how to filter, clean, and transform data.

These processes will help you identify errors and retrieve and store data, giving you an edge in confirming data to make informed business decisions. It also saves your company from producing unreliable analytics results.

3. Business Knowledge

Having business knowledge and understanding of your company's business model gives direction on how to channel company resources in the right direction. Business intelligence analysis goes beyond gathering information. You'll need to understand how to use cleaned data to produce the results a company needs. These should be actionable information for making the right decisions.

With vast business knowledge, you can support a company's vision by providing consultancy on leveraging market trends to improve revenue. These skills also help you interpret data in a way that aligns with a company's vision and mission. So, you're not compiling chunks of data. You visualize them and arrange them in a way consistent with business growth.

4. Analytical and Critical Thinking

You need to have critical thinking incorporated into the responsibility of a business intelligence analyst. Also, collecting data, analyzing, and deducing conclusions that would be useful to your company places you on a pedestal for success as a business intelligence analyst.

Analytical and critical thinking ability helps you properly scrutinize data and build something useful from your gathered data. Critical thinking also helps you recognize the weaknesses of a company's system based on the data you've gathered.

For example, if you work for a food-service retailer like McDonald's and gather data on the influx of in-person sales and online orders. Critical thinking helps you decide whether to increase your personalized online sales strategy or improve your customers' eat-out experience. You want to also make decisions for current seasons based on the data you've gathered while maintaining the company's goals.

5. Programming Languages

Programming stands at the core of business intelligence analysis. You'll need basic knowledge of SQL programming to help you pre-aggregate, filter, and only select the data necessary for your analysis. With knowledge of SQL queries, you can create views (rows and columns of data) of a few thousand, lessening the amount of data your BI tool has to work with. This process makes analysis easier and faster.

Therefore, learning basic SQL commands and queries is an important skill for business intelligence analysts. SQL programming language is also useful for data extraction, data quality, data validation, and creating analytical reports. In addition to the SQL programming language, you also need basic knowledge of Python and R programming for data visualization.

6. Statistical Analysis

Through statistical analysis, you can discern market trends and forecast business patterns. It involves organizing and analyzing raw data to draw up reliable information. During statistical analysis, you remove all bias from the data set, leaving you with structured data.

A business intelligence analyst should have statistical analytical skills to describe patterns that can predict future changes in market demands, services, or prices, depending on what company you're working for. You'll need this skill to coin useful information from messy data, reducing uncertainty during decision-making.

7. Creation of Reports and Dashboard

Learning how to create reports and dashboards will help you organize and manage your company's data. It helps you store data in patterns that make it easy to calculate the growth and performance of your company. You can also project future company trends with the report you create to present to stakeholders willing to invest in the company.

In addition to choosing the ideal reporting tool , it's expedient for you to learn how to navigate reporting software and visualize data relevant to your company. You also want to create reports that are simple and easily understood. By developing skills in creating dashboards, representing data in an all-in-one platform becomes easy.

As a business intelligence analyst, conveying data in the simplest of ways is crucial. Thus, proficiency in creating reports and dashboards that simplify data is important.

8. Data Visualization

Data visualization skills include understanding data purpose, navigating data visualization tools, and telling a compelling story using graphs, web maps, charts, and tables. In addition, learning how to present data to a layperson using charts and graphs is essential. Combining statistical data with art and having strong visual design skills are pluses.

Once you can gather cleaned data and run them on different databases, upgrade your data visualization skills to make yourself a better business intelligence analyst. Also, learning when to use what chart type is important. You want to avoid using the wrong chart types, which may confuse people.

9. Organization and Time Management

Your ability to efficiently manage your time and interpret data promptly is important for your company's growth. If you work for a healthcare company looking to scale its medical services and get data on the number of individuals they've treated in the past, you'll need to sort these analyses in an organized manner.

A tiny error in the figures can result in a huge dent in your company's image. In addition to being meticulous, relaying information on time, especially when providing data for shareholders who want to examine some reports before making investments, is important. You help your company make relevant decisions on time by providing the required data on time.

10. Industry Knowledge

Finally, having industry knowledge about your company is necessary. If you work in the health and fitness or digital marketing niche, you have to be familiar with the latest trends and trajectories. Read the numbers and watch what other competitors are changing. If they were rethinking their strategy, they could have made their analysis.

Analyze your company's data and find ways to leverage that information to boost your company's resources. However, this is only possible if you understand your way around the industry and what drives it. Aside from making you an asset to your company, becoming knowledgeable in your industry also increases your worth as a business intelligence analyst.

Become a Top-Rated Business Intelligence Analyst

In addition to acquiring high-level skills as a business intelligence analyst, you'll also need the appropriate BI tool to help you boost your career and become top-rated. One of the best is Power BI. By using Power BI, you'll get accurate results in no time.

So, to become a top-rated business intelligence analyst, you can start by going through some online courses to help you master Power BI.

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How to turn critical thinking into a business superpower

Board & management papers, why do businesses need critical thinking.

In 2001, American entrepreneurs Jason Jennings and Laurence Haughton’s best-selling book, It’s Not the Big That Eat the Small… It’s the Fast That Eat the Slow , coined a new mantra for business leaders. In the digital age, economies of scale were no longer a trump card. To succeed, you’d need to move fast — no matter your organisation’s size.

Moving fast without thinking smartly, however, is a recipe for breaking things — as many corporate scandals and failures, from WeWork’s collapse to Boeing’s ongoing problems with the 737 MAX, have highlighted. In contrast, the world’s most enduringly successful companies have shown us that the secret to winning in the long term is great thinking — and lots of it. You can’t move fast, and in the right direction, if you don’t think first.

Critical thinking helps you spot risks and opportunities early. It helps you to understand problems properly before you start trying to solve them. It leads you to insights that might otherwise remain buried. It helps you build more robust plans, preventing costly missteps. And it relieves bottlenecks by giving leaders the confidence to empower decision-making closer to the front line.

In sum, deep thinking doesn’t slow you down, it speeds you up.

Take Amazon, for example. Deep and detailed thinking underpins the e-commerce giant’s ability to innovate and pivot at pace.

“At Amazon we spend a lot of time writing documents, because it helps you think things through and gives others confidence that you’ve done so”, a former colleague and current Amazonian told us. “That builds trust, which helps us move quickly — you don’t have to go up the chain on every decision. Deep thinking is a hugely important part of Amazon’s entrepreneurialism.”

Yet, for many businesses, this powerful combination of critical thinking and agility is elusive. Just 14% of the business leaders who took our Agility Friction Test think their organisation moves fast and finds it easy to get things done, and 88% say they don’t have the quality of thinking they need or would like throughout their organisation .

The good news is that critical thinking is a skill we’re all capable of, and opportunities to hone it are hiding in plain sight in every business.

What is critical thinking and where are businesses going wrong?

Albert Einstein was onto something when he (allegedly) uttered this now-famous line:

“If I had an hour to solve a problem and my life depended on the solution, I would spend the first 55 minutes working out the question at the heart of it. Once I knew the question, I’d only need five minutes to solve the problem.”

He recognised, as had Socrates before him , that questions are powerful. They are the spark and fuel of critical thinking. They unlock our ability to reason, understand, and create. They sharpen our decisions, plans, and ideas. And they’re as vital to businesses as they are to academics.

To succeed, businesses need a culture of questioning, which means everyone, at every level, asking and answering questions all the time. The problem is that we’re not as good at it as we think we are.

We all start off as prodigious questioners. Research from Harvard shows that children bombard their caregivers with up to 100 questions a day between the ages of two and five, as they try to make sense of the world around them. But as we get older, we ask less, in part because the habit is drilled out of us.

“There is what I call an answer orientation that permeates education, professional life and society at large,” explains Dr Lani Watson, a University of Oxford academic who researches questions and questioning.

And because we value answers, we lose our ability to ask questions, which in turn reduces our capacity for creativity and critical thinking.

But that doesn’t mean we should resign ourselves to a question-less life or pass the responsibility to the minority of adults who, somehow, retain their childhood inquisitiveness. We can get our questioning mojo back. And over the past 15 years, we’ve worked with thousands of executives and hundreds of organisations to develop a way to do just that: the QDI Principle, a methodology for high-quality thinking and writing . It outlines three critical thinking rules that can help individuals, teams, and organisations to think smarter and act faster.

How do you think critically in a business context?

When you next have some thinking to do — whether that’s building out a plan, reviewing performance, or submitting a request for resources — try following these three QDI Principle rules to help you identify the right questions to ask.

1. Tackle the tough questions

The questions that add most value to your thinking are the ones that cut through the noise and get you to the crux of what matters faster.

Unfortunately, they’re often the questions we’d rather shy away from — the tough ones that are difficult to answer, surface uncomfortable truths, or trigger more questions. Or they’re the questions that are so simple and blindingly obvious that we neglect to ask them, because we assume we already know the answer or because we don’t want to look stupid by admitting we don’t. Like “What happened?”, “Why?”, and “What other options have you considered?”

Don’t hide from these questions. Seek them out and tackle them head-on .

2. Leave no stone unturned

To avoid creating blind spots or making judgements built on flawed foundations, you need to find and plug the gaps in your thinking .

So, take a step back and cast your eye over the questions you plan to tackle. Map them out visually, write them down, or talk them through with someone.

Getting the questions out of your head makes it easier to understand how they fit together and what you might be missing. For example, you might have looked at the opportunities but forgotten to think about the risks. You might have thought about the internal implications of your plan but not considered the external context.

It’s only by holding your thinking up to inspection that you can identify and fill the gaps in it — and avoid costly missteps caused by half-baked logic and ill-conceived plans.

3. Charge thinking with insight

With so much information at our fingertips, finding the insight buried within it can feel like searching for a needle in a haystack. But find it we must. Because it’s insight, and not information, that acts as the foundation for great plans and ideas.

So, challenge yourself to draw meaning from the information in front of you by asking “So what?” and “What needs to be done differently as a result?”

Asking these two simple questions will help you draw this meaning out, so you can charge your thinking with actionable insight .

How do you turn critical thinking into a daily habit at work?

Now you know how to surface the right questions, you need to make asking them a habit — an easy yet unavoidable part of people’s working lives. Fortunately, our daily routines are full of opportunities to do this. They lie in the activities that every business is run on, like quarterly business reviews and annual planning cycles.

Most of the time, people start preparing for set-piece activities like these by jumping straight into the weeds, pulling every imaginable cut of data together and then attempting to explain what it all means in a long and detailed document.

But to get everyone thinking harder about the topic at hand, the prep work should start not with research or the data that’s available, but with the critical questions that everyone involved in the activity needs to grapple with. Those questions should be used to focus the prep work, organise the information (as headings or sub-headings in a report), and structure the discussion that follows (as the meeting agenda). This makes it hard for people to avoid engaging with those questions and, through repetition, it creates an expectation: “This is how we do things around here.”

Leaders can do some of this work in advance and package questions up into set plays for routine interactions, to help their teams identify the critical questions and cut to insight more quickly. We call them QDI plays.

Like a quarterback barking out a coded signal on the football field (“Green 19, Green 19, set hut!”), you select the right set of questions for the task, be it a monthly performance review or preparing a business case for your boss to sign off on, from a bank of preloaded “plays”. You then frame the meeting, email, report or slide deck, and the thinking that goes into it, around those questions.

For example, if you’re preparing for a regular performance update, you could use a QDI play like this:

• What are we trying to achieve?
• What’s gone well and what has not?
• What are the key risks and opportunities?
• Given all of this, what should we stop doing/start doing/do differently?
• Are we confident we’ll achieve our aims?

These can each lead to further questions, but starting with the most obvious and important ones gives your thinking a solid foundation and helpful guard rails. It helps you cover all the bases and tackle the tough questions head on and stimulates your creativity by asking you to think about doing things differently.

You can see a similar approach in a wide variety of organisations and contexts. Take the British Army, for example : since 2001, they’ve used a seven-question framework, the Combat Estimate, to help commanders rapidly formulate plans, even in the most difficult circumstances.

The specific questions you ask will vary with the situation, but the approach should always be to keep it simple and make it easy to use. Before long, asking the right questions becomes something that people do every day.

How do you turn critical thinking into an organisation-wide habit?

The key to getting an entire organisation to think well is to make it difficult for everyone not to do it well. The companies that do this best don’t rely on templates or training. They use technology to show people when they’ve missed the mark and help them to find their way back to it.

It’s similar to a Japanese concept called poka-yoke , or “mistake-proofing” — designing a product or process in a way that makes it really hard to screw up. The term was coined on Japan’s manufacturing floor in the 1960s, and applications of poka-yoke can today be found in most modern manufacturing processes as well as everyday life. Like automatic cars that won’t start if they’re not in “park” or “neutral”, so you don’t lurch forwards (or backwards). Or filing cabinets that won’t let you open more than one drawer at a time, to stop them falling over.

These days, modern software tools can be used in much the same way, to get you off to a solid start and then nudge and guide you back to the path when you stray off it. AI, in particular, can be a powerful aid; its immense capabilities to analyse text can nudge report writers in the right direction and act as a critical, always-on-call friend.

That’s why we built Lucia, an AI-powered thinking and writing guide . It can give instant feedback and real-time prompts that challenge what your team members are writing, as they’re writing it, nudging them in the right direction and continuously training them.

Have they considered the risks? Are they looking forwards as well as backwards? Are they sharing the bad news as well as the good? Lucia’s AI tools will ask these questions and suggest ways of fixing the problems it finds, so that, over time, razor-sharp critical thinking becomes a habit for everyone — and you can unlock your team’s collective intelligence to move faster.

Want to start building critical thinking habits in your business today?

If you want to start building your team’s critical thinking habits today, take a look at Lucia, our AI-powered management reporting software . It offers live feedback, real-time prompts, smart automatic editing tools — and much more.

Additional resources

If you’d like to learn more and find out how to apply these principles to specific papers, check out our best practice guides below.

• write a great CEO’s report
• write a great business case
• write a great management report
• create a business dashboard
• write better board packs with the QDI Principle
• build a business that can move quickly with the QDI Principle

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Critical thinking: a model of intelligence for solving real-world problems.

1. Introduction

2. the problem with using standardized iq measures for real-world problems, 3. which theories of intelligence are relevant to the question, 4. critical thinking as an applied model for intelligence, 5. pandemics: covid-19 as a consequential real-world problem, reasoning by analogy and judging the credibility of the source of information, 6. making cost—benefit assessments for actions that would slow the spread of covid-19, analyzing arguments to determine degree of support for a conclusion, 7. conclusions, author contributions, institutional review board statement, informed consent statement, conflicts of interest.

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Share and Cite

Halpern, D.F.; Dunn, D.S. Critical Thinking: A Model of Intelligence for Solving Real-World Problems. J. Intell. 2021 , 9 , 22. https://doi.org/10.3390/jintelligence9020022

Halpern DF, Dunn DS. Critical Thinking: A Model of Intelligence for Solving Real-World Problems. Journal of Intelligence . 2021; 9(2):22. https://doi.org/10.3390/jintelligence9020022

Halpern, Diane F., and Dana S. Dunn. 2021. "Critical Thinking: A Model of Intelligence for Solving Real-World Problems" Journal of Intelligence 9, no. 2: 22. https://doi.org/10.3390/jintelligence9020022

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The Path to Critical Thinking

by Stever Robbins

Can you write a refresher on critical thinking?

What's logic got to do with it? Nothing! We don't use logic to decide, or even to think. And a good thing, too, or the advertising industry would be dead in the water. Unfortunately, all of our decisions come from emotion. Emotional Intelligence guru Daniel Goleman explains that our brain's decision-making center is directly connected to emotions, then to logic. So, as any good salesman will tell you, we decide with emotion and justify (read: fool ourselves) with logic.

Purely emotional decision making is bad news. When insecurity, ego, and panic drive decisions, companies become toxic and may even die. Just look at all the corporate meltdowns over the last five years to quickly understand where emotional decision making can lead.

Critical thinking starts with logic. Logic is the unnatural act of knowing which facts you're putting together to reach your conclusions, and how. We're hard-wired to assume that if two things happen together, one causes the other. This lets us leap quickly to very wrong conclusions. Early studies showed that increasing light levels in factories increased productivity. Therefore, more light means more productivity? Wrong! The workers knew a study was being done, and they responded to any change by working harder, since they knew they were being measured—the Hawthorne Effect.

We also sloppily reverse cause and effect. We notice all our high performers have coffee at mid-morning, and conclude that coffee causes high performance. Maybe. Maybe not. Maybe high performers work so late and are so sleep deprived that they need coffee to wake up. Unless you want a hyper-wired workforce, it's worth figuring out what really causes what.

There are many excellent books on logic. One of my favorites is the most-excellent and most-expensive Minto Pyramid Principle by Barbara Minto. It's about logic in writing, but you can use it for any decision you want to think through in detail.

The trap of assuming You can think critically without knowing where the facts stop and your own neurotic assumptions begin. We aren't built to identify our own assumptions without lots of practice, yet the wrong assumptions are fatal.

When we don't know something, we assume. That's a fancy way of saying, "we make stuff up." And often, we don't realize we're doing it. When our best performers leave, our first (and perhaps only) response is to offer them more pay, without realizing that other motivations like job satisfaction or recognition for accomplishments might be more important.

Finding and busting "conventional wisdom" can be the key to an empire. For decades, the standard video rental store model assumed that people wanted instant gratification and, to get it, they were willing to drive to a store, pay a rental fee for a few days' access, and then drive back to the store in a few days to return the movie. Thousands of big and small video rental parlors popped up across the country using this model. But Reed Hastings challenged those assumptions. He calculated that people would trade instant gratification for delayed, and would pay a monthly fee if they could have movies mailed to them, which they could keep as long as they liked. The result? Netflix. Estimated 2005 revenue: $700 million.

Assumptions can also cripple us. A CEO confided that he never hires someone who backs into a parking space. His logic (and I use the term loosely): The person will use time at the start of the day so they can leave more quickly at the end of the day. He assumes face time equals results. In whose world? Many people tell me they get more done in an hour at home than in eight hours in an interruption-prone office. How many great employees will he miss because he's not examining his assumptions?

Some assumptions run so deep they're hard to question. Many managers can't imagine letting people work fewer hours for the same pay. "If they go home earlier, we have to pay them less." Why? "Hours = productivity" is true of assembly lines, but not knowledge work. Research shows that it's not how much you work, but the quality of the work time that drives results. 2 But in most workplaces, hours count as much as results.

Next time you're grappling with a problem, spend time brainstorming your assumptions. Get others involved—it's easier to uncover assumptions with an outside perspective. Then question the heck out of each one. You may find that one changed assumption is the difference between doing good and doing great.

The truth will set you free (statistics notwithstanding) Have you ever noticed how terrified we are of the truth? We're desperately afraid that the truth will reveal us as incompetent. Our situation really is hopeless. We really aren't as great as we pretend. So we cling to our beliefs no matter how hard the truth tries to break free.

Guess what, recording industry: Electronic downloads have changed the nature of your business. Start asking how you'll add value in a world where finding, packaging, and distributing sound is a commodity. Hey, ailing airlines: Oil's expensive, customers won't pay much, and you have huge capital costs. That hasn't stopped Southwest, Jet Blue, and others from making a fortune.

Nothing tells the truth like solid data and the guts to accept it. But it's difficult in practice. When was the last time you identified and collected data that contradicted your beliefs? If you found it, did you cheerfully change your belief, or did you explain away the data in a way that let you keep your comfortable pre-conceptions?

Here is a great exercise for your group or company. Have your general managers list your industry's Unquestioned Truths, which they then must prove with data. When a Fortune 500 CEO recently ran this exercise, Surprise! Some "absolute truths" were absolutely false. Now he can do business his competitors think is nuts. Analysts will say he's off his rocker, until his deeper knowledge of truth starts making a small fortune.

One caveat: Be picky about where you get your data. The Internet can be especially dangerous. The miracle of technology lets one bad piece of data spread far and wide, and eventually be accepted as truth.

Help! I've been framed! Not only may your data be disguised, but the whole problem itself may be disguised! It seems obvious: we're losing money, we need to cut costs. Not so fast! How you "frame" a situation—your explanation—has great power. Remember assumptions? Frames are big ol' collections of assumptions that you adopt lock, stock, and barrel. They become the map you use to explore a situation.

You're negotiating an acquisition. You're chomping at the bit. It's WAR!! Competition is all. The frame is combat!

Or, you're negotiating an acquisition. You're on a journey with the other party to find and split the value buried at the X. You still track your gains and gather intelligence, but the emphasis is on mutual outcomes, not "winning."

In a zero-sum one-time negotiation, a combat frame may be the best tool. But in a negotiation where you're free to develop creative solutions that can involve outside factors, the journey frame could work best. "Instead of $100K, why don't you pay $75K and let us share your booth at Comdex?"

Frames have great power! Presented with a potential solution to a problem and told, "This course of action has a 20 percent failure rate," few managers would approve. When that same solution is presented as having an 80 percent success rate, the same manager is going to consider it more deeply— even though a 20 percent failure rate means the same thing as an 80 percent success rate! The frame changes the decision.

Are you brave in the face of failure? Most people aren't. I recommend the responsibility frame: "What aren't we doing what we should?" The responsibility frame sends you searching for the elements of success.

The beauty is that no one frame is right, just different. The danger is when we adopt a frame without questioning it. You'll do best by trying several different frames for a situation and exploring each to extract the gems.

People are our greatest asset. Really Critical thinking isn't just about what happens in our own brains. When you're thinking critically in business, bring in other people! We don't consider the people impact in our decisions often enough. In fact, we pooh-pooh the "soft stuff." We feel safe with factors we can calculate on our HP-12B. But in truth, business is about people. Multibillion-dollar mergers fail due to culture clash.

Customers, suppliers, partners, employees. They're as much a part of your business as that sparkly new PC you use to play Solitaire. How will your decisions change their lives? Imagine being them and let your imagination change your decisions.

The Gallup organization estimates that 70 percent of America's workers are disengaged, and disengaged workers are dramatically less productive, creative, and committed than engaged workers. Yet few strategy meetings ask, "How can we engage our employees more?" It's as if we say people are our greatest asset—but we don't really believe it. If you want to improve your critical thinking, get other points of view.

A stitch in time saves nine Of course you know you should think about the consequences of your actions. But with information overload, quarterly earnings pressure, sixty-hour weeks…who has the time? We don't think much beyond the end of our nose.

But technology leverages the effects of our decisions throughout the organization and even across the globe. So good thinking demands that you consider consequences over many timeframes. Think out a month, a year, a decade, many decades. That tanning booth looks great when you consider how you'll look in a week, but is it worth looking like a leather overcoat ten years from now?

Long-term junkies like me are great at creating ten-year plans, but managing next month's cash flow? Not likely. Short-term junkies are more common; they're the ones who discount to make this quarter's numbers, while tanking the company in the process. You can do better by considering multiple timeframes.

I could go on, but there's plenty here to chew on. Think about a decision you're making, and pull in the rigor:

• Make sure you understand the logic behind your decision.
• Identify your assumptions and double-check them.
• Collect the data that will support or disprove your assumptions.
• Deliberately consider the situation from multiple frames.
• Remember the people!
• Think short and long term.

© 2005 by Stever Robbins. All rights reserved in all media.

Stever Robbins is founder and president of LeadershipDecisionworks, a consulting firm that helps companies develop leadership and organizational strategies to sustain growth and productivity over time. You can find more of his articles at http://LeadershipDecisionworks.com . He is the author of It Takes a Lot More than Attitude to Lead a Stellar Organization .

1. Yes, I know. I'm making a point. Congratulations; you got it. Color me subtle. Now go back and keep reading...

2. The Power of Full Engagement , by Tony Schwartz and Jim Loehr, New York: Simon & Schuster, 2003.

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Critical Thinking and Emotional Intelligence

More From Forbes

Curiosity unleashed: the missing ingredient in business education.

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Curiosity lies at the heart of all meaningful learning, particularly in business education. Educators witness firsthand how curiosity drives not only academic success but also the capacity to thrive in the constantly evolving world of business. Dr. Diane Hamilton , in her newly released book Curiosity Unleashed , explores the critical role that curiosity plays in shaping future business leaders.

Dr. Hamilton believes that in higher education, fostering curiosity is not just about encouraging students to ask questions. It is about empowering young minds to look beyond the obvious and confront the obstacles that hold organizations back. These insights resonate deeply in a world where the status quo often suppresses innovation. For leaders to emerge, they must possess the ability to question, explore, and challenge conventional thinking.

When engaging with students on the importance of curiosity, the focus is on asking the right questions—those that lead to deeper understanding and informed decision-making. Curiosity is not simply about gathering information; it is about delving into issues, examining them from various angles, and remaining open to new perspectives. This approach to thinking is directly tied to critical thinking, which Dr. Hamilton describes as a key benefit of cultivating curiosity in students. Ultimately, curiosity drives critical thinking and equips students to challenge the assumptions that often stifle innovation.

The connection between curiosity and critical thinking is clear. Critical thinking involves assessing situations, identifying opportunities, and solving problems in creative ways. These abilities are nurtured by a curious mindset. When students are curious, they become more engaged, which in turn fosters innovation. Dr. Hamilton discusses how business courses grounded in curiosity help students bridge the gap between theory and practice, enabling them to address real-world problems that organizations frequently struggle to solve.

In business education, fostering curiosity should be a top priority. Diane mentions that too often, the focus is placed on teaching the "what" and "how," while neglecting the importance of the "why." Students need to learn to question assumptions, remain open to new ideas, and explore possibilities beyond the obvious. This is not just about preparing them for exams; it is about equipping them to handle the complex challenges they will encounter in their careers. The true value of curiosity in the classroom lies in its ability to break down the barriers of fear and complacency, paving the way for bold, innovative thinking.

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To effectively cultivate curiosity, educators must be aware of the factors that inhibit it. Whether it is fear of failure, rigid thinking, or a lack of exposure to diverse ideas, these barriers can stifle the very curiosity that needs to be encouraged. Dr. Hamilton underscores the importance of creating environments where students feel safe to ask questions, make mistakes, and explore new ideas without fear of judgment. This kind of environment is essential for developing not only better students but also better leaders.

Curiosity in business education does more than enhance learning—it aligns with what organizations need most: critical thinkers who can break free from the status quo and drive meaningful change. Curiosity does not just improve the classroom experience; it cultivates the skills that are essential for success in the business world.

The value of curiosity in business education extends beyond the classroom. It is a lifelong skill that will benefit students in both their careers and personal lives. By fostering curiosity, educators are not only teaching students to think critically; they are also encouraging innovation, engagement, and success in whatever paths the students choose to pursue. This is the true value of curiosity, and it is why it should be at the center of everything done in business education.

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This high school is replacing teachers with ChatGPT and AI tools to personalize learning for some students

• David Game College will let some students learn with AI tools instead of teachers from next month.
• ChatGPT and LLMs will help 20 students prepare for exams in subjects like mathematics and biology.
• While some experts say AI can be a helpful learning tool, it cannot yet replace teachers.

A high school in London is replacing teachers with AI tools such as ChatGPT to help some students prepare for exams.

In the pilot scheme at David Game College starting in September, 20 students who are about 15 years of age will use AI tools for a year before taking their GCSE exams. The subjects will include English, mathematics, biology, chemistry, and computer science.

John Dalton, coprincipal of the fee-paying school, told Business Insider: "Students will benefit enormously from AI-powered adaptive learning, which allows every student to learn at their own pace rather than having to keep pace with a class, which often progresses too quickly for some students and too slowly for others."

The students will also be supported by three full-time learning coaches, and will receive personalized learning paths.

Dalton said AI-enabled learning will allow students to spend more time on a topic to master it, while also letting those who are ready to move on progress more quickly. It might also be helpful for students to ask the AI-powered learning assistant questions that they might not feel comfortable asking a teacher during class.

"We don't just want to teach core subjects as efficiently and effectively as possible, but to use the extra time this creates during the rest of the day to focus on areas such as self-awareness, critical thinking, active citizenship, digital literacy, artistic expression, public speaking, and entrepreneurship," he said.

Some educators hope AI can help to solve problems such as overworked teachers , ballooning class sizes, and a lack of one-on-one engagement with students. Others think it may have downsides.

Promising examples

"While AI can be a valuable supplement to live teachers, it cannot replace them entirely," Hadida Grabow, a director at the educational consultancy Higher Learning Group, told BI.

While there are some "promising examples" of tools such as Google's Socratic, an AI-powered learning app for students that offers explanations and resources, or the Khan Academy's AI teaching assistant, Grabow said: "We are not seeing anything that could replace a quality educator."

"Regrettably, the technology just isn't there yet — we've seen that with the high-profile failure of the Los Angeles Unified School District's AI chatbot," she said.

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The district introduced an AI chatbot called Ed in some schools last March to help with tracking assignments, grades, and student records. But about three months later, the chatbot was shelved after the company that created it collapsed.

Karl Knapp, dean of the business school at the University of Indianapolis, said AI systems can " hallucinate ," or make some things up, with students unlikely to "fact-check every utterance by the AI system."

Neither could AI systems judge tone of voice or facial expressions, which he said were "key indicators of student understanding when teaching."

'Humanized learning'

Dalton said that students participating in the pilot will spend their afternoons engaging in a "diverse support curriculum that includes learning how to debate, start a business, develop entrepreneurial skills, explore AI and financial literacy, and participate in creative activities such as cooking and painting." He added that the college has "humanized the AI learning process by creating a holistic and engaging educational experience." Students can still interact with teachers if they want to. "The system does not judge students. Instead, it allows them to learn at their own pace in a safe environment," Dalton said. "We also strongly believe that this approach will enhance student confidence as they achieve subject mastery, which in turn will improve their mental health."

Watch: What is ChatGPT, and should we be afraid of AI chatbots?

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Former Venture Highway Investor Aviral Bhatnagar Introduces AJVC to Support Early-Stage Indian Startups With a deep understanding of the enterprise software and artificial intelligence sectors, Bhatnagar's experience is set to drive AJVC's focus on pre-seed investments, filling a critical funding gap in India's startup ecosystem.

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Aviral Bhatnagar, a former lead investor at Venture Highway, has launched AJVC, a new venture capital (VC) firm with an INR 100 crore fund aimed at backing early-stage Indian startups.

With a deep understanding of the enterprise software and artificial intelligence sectors, Bhatnagar's experience is set to drive AJVC's focus on pre-seed investments, filling a critical funding gap in India's startup ecosystem.

Bhatnagar's decision to launch AJVC came after recognizing the significant challenges that early-stage founders face in securing initial funding. During his tenure at Venture Highway, Bhatnagar observed that many promising startups struggled to raise even INR 1 crore, often requiring pitches to over 20 investors. This arduous process frequently led to the abandonment of potentially successful ventures.

I aim to address this issue by creating a fast, approachable, and founder-friendly pre-seed investment firm," Bhatnagar shared in a LinkedIn post. AJVC's goal is to simplify the funding process and support startups in their earliest stages. Our first fund has already received SEBI approval and is committed on launch day. I intend to leverage my decade of experience as an investor to build AJVC.

The venture capital landscape in India is witnessing a significant shift as seasoned fund managers and partners depart established firms to launch their own ventures. This trend is exemplified by the recent merger of US-based General Catalyst with Delhi-based Venture Highway, a move designed to solidify General Catalyst's presence in India. The merger prompted the exit of Aviral Bhatnagar, a key investor at Venture Highway, shortly after the resignation of Samir Sood, a co-founder of the firm, who stepped down as a partner months earlier.

Venture Highway, founded in 2015 by Sood and Arora, has backed unicorns such as Meesho, CRED, and Moglix. Bhatnagar's departure signals a broader trend where experienced VC professionals are branching out to establish their own funds.

Atul Gupta, a former partner at Premji Invest, is another notable example of this trend. He recently launched Trident Growth Partners (India), a fund targeting investments across diverse sectors, including consumer goods, financial services, and healthcare. Similarly, Piyush Gupta, who exited Peak XV in April, has started a secondary-focused fund, highlighting the growing interest in niche investment areas.

Lightspeed India also experienced a reshuffle, with venture partners Vaibhav Agrawal and Abhishek Nag leaving the firm to explore new opportunities. Meanwhile, Sameer Brij Verma, who served as managing director at Nexus Venture Partners, has launched a multi-stage fund, further underscoring the momentum of this trend.

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AI adoption is ‘mission critical’ for tech leaders, but ‘internal rigidity’ is holding them back

While CIOs are sold on the benefits of automation, there is less enthusiasm in other business teams.

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The wider adoption of artificial intelligence and machine learning is “mission-critical” in businesses, according to a majority of CIOs responding to a survey by MIT Technology Review Insights . However, tech leaders say “internal rigidity” within their organisations threatens to stall progress.

The survey explored ways companies are bridging the gap between business intelligence and artificial intelligence, including a survey of 600 C-Suite executives working across 14 industries between May and June this year. All of the respondents worked in organisations with $500m or more in annual revenue.

The majority of those polled said their companies were in the process of scaling up the use of artificial intelligence through greater investment in data foundations and that multi-cloud and open standards were integral to any progress.

Just over half of those surveyed expect AI use to be widespread or critical to business functions by 2025, particularly to IT, finance, product development, marketing, and sales. In total 94% of senior leaders said their companies were already using AI in their line of business and would increase that use in the coming years.

One of the biggest barriers to the widescale adoption of AI within an organisation is “internal rigidity”, the survey found. This includes rigidity of organisational structures and processes, as well as budget constraints for new technologies that are needed to scale.

Despite the difficulties, most see scaling up the use of AI from current experiments and small-scale projects as a major priority, outlining its importance to data strategies, but explaining the issue of poor data management has to be addressed first. This will include investing in unifying data platforms that merge analytics and AI. Almost every leader said this was critical.

“Data issues are more likely than not to be the reason if companies fail to achieve their AI goals, according to more than two-thirds of the technology executives we surveyed,” says Francesca Fanshawe, editor of the report. “Improving processing speeds, governance, and quality of data, as well as its sufficiency for models, are the main data imperatives to ensure AI can be scaled.”

AI in business: good quality data is vital

Mike Maresca, global chief technology officer at pharmaceutical retailer Walgreens Boots Alliance, said data was firmly at the top of the list of AI challenges the company needs to address, even after having upgraded its data infrastructure.

“We now have the right data platform, the right quality tools, and the right governance in place,” Maresca said. “But ensuring the data quality remains high, while we enhance our algorithms over time to continue driving the right business outcomes, is a key challenge as we scale.”

The level of investment will vary by industry, with the most money expected to be spent within the financial services sector, dwarfing other areas of the economy. For example, spending on data governance will increase by 74% by 2025 in finance compared to 52% across all other sectors.

The other two sectors that will see growth rates above the average of 52% are retail and automotive.  Executives in these sectors were among the most ambitious about becoming AI-driven.

“Many companies don’t really know what they have in terms of their entire data estate or how they measure quality across it,” explained Jeremy Pee, chief digital and data officer at Marks & Spencer to the authors of the report.

“We’re addressing this in our new platform by putting cataloguing, searchability, data quality management, and other capabilities around every data element. We need to do all of this at the speed necessary to respond to customer and business needs.”

Chris D’Agostino, field CTO at cloud database vendor Databricks, which sponsored the report, said the insights are consistent with what the company is hearing from clients. He said that AI-ready data is no longer “nice to have” but critical to solving real-world problems and driving business outcomes.

Making AI work at scale will be dependent on multi-cloud and open standards, the leaders explained, with organisations turning to service providers to facilitate AI strategies – often using the services of two or more public cloud vendors.

“Having a multi-pronged, multi-cloud approach, and then incorporating APIs and microservices as part of our data architecture, are key for us,” says Rowena Yeo of Johnson & Johnson in an interview with the researchers.

In their conclusion, the researchers wrote that future success in innovating with AI will rely on the data, insights and tools they can source externally. “Data technology that favours open standards and open data formats is well placed to facilitate such collaboration.”

Read more: AI in call centres could save businesses $80bn

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