From OUR EDITORIAL STAFF we restart the course with a very interesting topic that is on everyone’s lips, in the minds of business leaders, as well as politicians who have to define political action programs, in addition to consultants, professors and especially very dedicated lately any student who is taking a postgraduate course. It is obvious that we are referring to AI (Artificial Intelligence). As is our style, we have chosen for today some very outstanding contributions from authors and also companies and/or consultants that always mark the direction of where the actions of organizations and institutions should go, at a time when technological disruption gives no respite.
AI revolution: rise of productivity and beyond
This contribution corresponds to the Barclays economic research team, which we detail below:
– Christian Keller, who is Director of Economic Research at Barclays and leads a global team that covers developed and emerging markets. He is based in London and joined Barclays in 2007 from the International Monetary Fund (IMF), where he had worked on programmes with emerging market economies in Europe, Latin America and Asia. He graduated with a PhD in Economics from the University of Cologne, Germany, and holds a joint Masters in Economics and Finance from the University of Cologne and HEC, Paris.
– Mark Cus Babic, is an economist in Barclays’ European Economic Research team, based in London. His areas of expertise are the euro area real business cycle and macroeconomic coverage of Germany. He joined the bank in 2020. Mark holds an MSc with honours in Economics from the University of Edinburgh.
– Akash Utsav, who is also an economist in Barclays’ Global Economics Research team. He joined the bank in 2017 and contributes to thematic notes on global macroeconomics as well as quarterly publications. He holds a Masters in Economics from University College London (UCL), London, UK, and completed his Bachelors at Loyola College, Chennai, India.
The world operates in a digitalised economy, but information technology (IT) has not provided the long-term boost that many had hoped for.
In fact, productivity growth has been in decline since the 2000s. In partnership with the IBM Institute for Business Value, Barclays Research is exploring the potential of artificial intelligence to drive a productivity boom that will power economies in the future.
There has been growing enthusiasm for “generative” artificial intelligence
which uses powerful computer models to produce high-quality text, images and other content, based on the data they were trained on.
Many are wondering whether this technology could mark a turning point in labour productivity, similar to the inventions of the steam engine, electricity and the personal computer.
Historically, positive effects on productivity have lagged behind the invention of new technologies, but our research analysts, working in collaboration with analysts at the IBM Institute for Business Value, see good reasons for optimism when it comes to GenAI’s potential to drive growth.
On the one hand, the basic technology is accessible to a very broad audience on an infrastructure that already exists
A user can give instructions to a tool like ChatGPT, the bot developed by OpenAI and launched in November 2022, without having to learn any special programming language.
On the other hand, these tools are not limited to any particular task, function, problem, or sector. This makes them usable across different disciplines.
Once a large language model is trained on a body of text, for example, it can summarize a legal document just as well as a medical document or an insurance document. Few occupations are likely to remain unaffected. As Stanford scholar Jerry Kaplan said several years ago: “automation doesn’t see the color of your collar.”
Providing a genuine boost to the production of goods and services.
Those two basic attributes – accessibility and versatility – suggest that a broad implementation of GenAI could encounter fewer obstacles than previous technological advances and thus provide a genuine boost to the production of goods and services.
However, to ensure that we unleash the full potential of AI technology and limit any of its negative effects, the right mix of policies will need to be put in place, both from a regulatory and business point of view.
Two of the biggest challenges for the global economy in the coming decades are, on the one hand, ageing populations in advanced economies and, on the other, low productivity in developing economies. AI could help on both counts.
In countries like Japan, Germany and Italy, for example, labour forces are shrinking fast enough
Of course, this is going to require huge leaps in labour productivity, just to maintain the levels of GDP growth that prevailed before the pandemic.
But such advances are possible. According to our analysts’ estimates, most countries would need to achieve similar levels of labor productivity growth to those they achieved between 1990 and 1994 to regain pre-COVID average GDP growth rates by 2033.
In the emerging world, the picture is different: working-age populations are still expanding, overall, and in some cases very rapidly. But skill and education levels tend to be limited, on average, compared with advanced economies, manifesting in low GDP per hour worked.
In addition, economists are observing what some have described as “premature deindustrialization”
in which developing nations no longer experience the industrialization that typically resulted in large productivity and real income gains, as workers shifted from agriculture to manufacturing.
With AI, however, that productivity-boosting effect may now be possible, if those workers move into AI-assisted service industries. “Service-ization” could take over the role that industrialization played in the past.
Balancing AI-driven productivity potential with societal needs and security
Policies adopted by businesses, industries, and regulators will have a major influence on whether the promised benefits of AI are delivered and how those benefits are distributed. Cost will also be a critical factor, as acquiring data and computing power (and the energy it requires) is not cheap.
Our analysts argue that two guiding principles are especially crucial in the development of AI. The first is that technology be used as a complement to labor, rather than a substitute for it.
A recent survey by the IBM Institute for Business Value indicated that the primary goal of companies is to enable a greater focus on uniquely human talents such as creativity, social and interpersonal skills, and empathy. In that context, critical skills include time management, the ability to prioritize, and an affinity for working in teams.
Second, it is vital that policies encourage the spread of AI across the economy
Regulation around access to data to train or deploy specialist technology is likely to play a critical role, and issues around security, privacy and ethics need to be addressed.
Our analysts are encouraged by initiatives such as the AI Alliance, a global network of technology companies, universities, non-profits and government groups that have come together to, in their words, “responsibly maximise the benefits for people and society everywhere.”
New business models and workflows
The opportunities offered by AI appear broad. However, fully realising its benefits is likely to require a very human response – a collaborative effort by industries and regulators, and a complete reimagining of business models and workflows.
How AI can boost productivity and fuel growth
This contribution is from JPMorgan Bank’s Economics and Markets research section. The authors are Joe Seydl, Senior Markets Economist and Jonathan Linden Executive Director, Senior U.S. Equity Strategist
Optimists say AI is a revolution. Skeptics respond that it is a bubble.
We believe that the impact of AI could be truly transformative, as we discuss in our Mid-Year Outlook for 2024. The path ahead is uncertain, but there are powerful forces that could drive it forward.
In this article, we apply an economist’s perspective to a question at the heart of the debate between optimists and skeptics: how might AI affect the broader economy?
Among the questions we address:
– How much productivity gains could AI bring? And how quickly could it come?
– Which jobs could be displaced, and how might policymakers respond?
– Will AI be inflationary or disinflationary?
– What will be the pace of corporate adoption?
Along with a macro perspective on the potential of AI, and generative AI in particular, we also consider the prospects for investing in this powerful trend.
Although AI stock valuations have performed well, we see no signs of a bubble.
And while AI-related companies now account for a relatively high proportion of the overall US market, we don’t think market concentration is necessarily a cause for concern.
In short, you can find a wide range of AI-related investment opportunities to consider now and in the years ahead.
Productivity and growth: how soon, how fast?
When assessing the economic prospects for AI, productivity is a key metric. Faster productivity growth allows the economy to grow faster and living standards to rise more quickly, without generating excessive inflationary pressures. The U.S. economy has not seen sustained productivity gains since the 1990s. A repeat of 1990s-style productivity gains could usher in a new era of economic growth.
To understand how AI might (or might not) transform the economy, we consider historical precedents.
In his latest letter to shareholders, our Chairman and CEO Jamie Dimon compared AI to the steam engine, electricity, and the personal computer. If we look at those episodes of technological innovation, productivity gains don’t appear overnight.
It took more than 60 years for the steam engine to deliver any observable productivity benefit to the entire economy
With each subsequent technological innovation, productivity gains occurred more quickly.
If the trend holds (and we think it will), by the late 2020s, U.S. economic data could show evidence of productivity gains thanks to AI. Think of it this way: It took 15 years for the personal computer to boost the productivity of the economy. AI could do it in seven.
The time between innovation and productivity growth has been shortening
Chart depicts years from innovation to productivity growth for innovations such as the steam engine, electricity, PCs/Internet, and AI
Is AI the 21st-century equivalent of the steam engine or electricity, each of which radically changed the economy? Probably not. But we believe AI has the potential to be as transformative as the web and the personal computer, with the potential to deliver even more economic value over the next 20 years.
The Internet provided a powerful boost to productivity, but AI will likely outpace it
As for the percentage increase in labor productivity relative to the baseline without technological advances, the bar chart depicts the percentage increase in macro productivity relative to the baseline without technological advances
Quantifying job displacement
To quantify the potential impact of AI, we modify an International Monetary Fund (IMF) framework. We conclude that the impact of AI could be much larger than the productivity assumptions built into projections by government agencies such as the Congressional Budget Office.
The IMF identifies which jobs could potentially be displaced by AI. We assume that half of vulnerable jobs in the United States will be automated over the next 20 years. The cumulative productivity gain would be about 17.5% or $7 trillion more than the Congressional Budget Office’s current projection for GDP.
What if half of the vulnerable jobs in the United States are automated in 20 years?
The Congressional Budget Office (CBO) calculated this using the projection of real GDP divided by the number of hours worked. The AI-induced upside potential is estimated by assuming that 15% of jobs will be replaced by AI in the next 20 years, which is half of what the IMF suggested in the research.
It’s important to remember that technological innovation tends to boost economic productivity
But it generally does so only when it changes the amount of labor and capital needed to create a given service or product in the economy. So, for example, Uber didn’t change the labor and capital inputs (it’s still one driver, one car). But driverless cars, if they eventually appear on our roads, could.
What jobs might be most at risk of being replaced by AI? Not surprisingly, white-collar professional service jobs, such as budget analysis and technical writing, appear more vulnerable than child care work or pipe-laying.7
We also expect to see an education gap
A Pew Research Center study finds that workers with bachelor’s degrees or higher are more than twice as likely to be in jobs exposed to AI than those with only high school diplomas. The chart below is illustrative:
Across the global economy, AI will likely disrupt some economies more than others
The IMF has found that workers in advanced economies are more vulnerable to AI displacement than those in emerging market economies. For example, the IMF estimates that 30% of US jobs could be displaced by AI, versus less than 13% in India.
All estimates and projections, including our own, should be taken with a grain of salt. No one can accurately predict the economic trajectory of AI, and estimates of its economic impact vary widely. One specific uncertainty relates to the cost of implementing AI technologies in the workplace.
We are already seeing the infrastructure costs related to building AI computing platforms skyrocket.9 Just because a particular job can be automated using AI technologies doesn’t mean it will be if it is not profitable.
Among optimistic forecasters, Goldman Sachs projects a 15% increase in GDP thanks to AI over the next 10 years. Our estimates are a bit more moderate: we see an 8% to 9% increase in GDP over the next decade. MIT economics professor Daron Acemoğlu takes a much more circumspect view of the potential macroeconomic impact of AI, projecting only a 1% to 1.5% increase in GDP over the same period.10
Economists make varying estimates of how AI will impact growth over the next decade
The chart outlines the potential impact of AI on growth over the next decade and the cumulative percentage increase over baseline real GDP by 2034.
We should also remember that economies evolve in ways that can be best understood in hindsight. According to a study by economists at MIT, more than 60% of current job occupations in the United States did not even exist in 1940.
New technologies explain much of that change. Through each successive technological transition, aggregate demand increased and the economy created jobs that did not exist before.
History tells us that technology continually creates new jobs as old ones disappear
Policymakers will need to respond to the societal challenges posed by AI
Public policies focused on job training and transitioning vulnerable workers will likely be needed to minimize disruption from an increased pace of job displacement.
Challenges to corporate adoption
Next, we shift our perspective from the broader economy to sectors and companies. The economic impact of AI will depend on whether (and how) CEOs and management teams make AI a core part of their business strategies and operations. Right now, it’s early days.
While most U.S. companies are considering how they might use AI, so far only about 4% of companies have actually adopted the new technology.
We believe adoption rates need to increase to 50% or more before AI-driven productivity begins to impact the broader economy.
Adoption faces many hurdles. These include: concerns around the supply of advanced semiconductors, legal and regulatory issues, potentially limited power and electricity resources for data centers, and the ability of companies to optimize potential use cases.
Still, the technology makes possible a wide range of potential use cases across various industry sectors. That’s what gives AI the potential to have broad macro implications for the U.S. economy.
How organizations use AI to maximize revenue
This contribution corresponds to Datarails, which defines itself on its website as “Leading with experience.” It adds that “Datarails is on a mission to radically change the way analytics are used within the finance function. Our executive team has spent several decades in the corporate world and is very familiar with the pain points faced by finance professionals.
That is why our FP&A platform was designed to empower finance professionals. Finally, the data-driven trend that is changing the way companies across all industries manage their business has reached the financial sector.
Specifically, the author of the article on this portal that we reference today is Kelly Kennedy, who has a degree in business and is a university professor of accounting and finance, is also an entrepreneur and writes about finance and business.
By now, you may already know how to use AI to generate content, optimize operations, or even improve customer experiences. But have you considered the immense potential of artificial intelligence to increase revenue and maximize the performance of your organization? If not, now is your chance!
Consider these statistical predictions from December 2023:
“AI technology can increase revenue by more than $15 trillion over the next decade (PwC). Some estimates suggest that AI technology could generate $15.7 trillion in revenue by 2030, increasing the GDP of local economies by an additional 26%.”
If your organization wants to reap those benefits and stay ahead of the curve, it’s time to incorporate AI strategies into your business plan.
11 Ways Organizations Use AI to Maximize Revenue
Today, we’ll dive deeper into how to do exactly that when we share 11 ways organizations use AI to maximize revenue and how you can, too.
- Predictive Forecasting
AI-powered predictive analytics involves using advanced algorithms to analyze historical data and market trends. In this way, more accurate revenue forecasts can be obtained. Your team can go beyond traditional forecasting methods, which often rely on limited historical data and human intuition.
With AI, you gain access to sophisticated models that can spot subtle patterns and correlations that humans might miss. This translates into the ability to make data-driven decisions with a higher degree of accuracy.
These insights allow you to allocate resources more efficiently, whether it’s:
– Optimizing inventory levels
– Adjusting production schedules
– Fine-tuning marketing budgets
- Optimized pricing strategies
AI really shines when it comes to processing large data sets, including market demand, competitor pricing, and customer behavior in real time. This allows for dynamic price adjustments, moving away from fixed pricing models. AI constantly analyzes data, adapting prices to maximize revenue during demand spikes and stay competitive during low demand periods.
Strike a balance between increasing sales and profit margins, while also performing A/B testing to adjust prices. AI tracks competitor prices, personalizes customer prices, and also optimizes discounts and promotions. This makes your pricing strategy agile, data-driven, and responsive to market changes.
- Customer Segmentation
AI can analyze extensive customer data to effectively segment your audience. Beyond basic demographics, AI can discern intricate patterns in customer behavior, preferences, and purchasing habits. Intelligently segmenting your customer base allows you to tailor your marketing strategies and product offerings to meet the unique needs and preferences of each group. This personalized approach significantly increases sales and improves customer retention as customers feel more understood and valued.
- Fraud Detection
Implementing AI-powered fraud detection systems is crucial to minimizing revenue loss due to fraudulent activities. These systems employ machine learning algorithms to analyze transaction patterns in real-time, instantly flagging suspicious activities. This proactive approach protects your revenue, your brand reputation, and builds trust with your customers.
Some of the most sophisticated fraud detection systems use artificial intelligence and data analytics to identify complex purchasing patterns. For example, account takeovers, social engineering scams, and fake reviews.
- Expense Management
AI-powered expense management tools take a deep dive into your organization’s spending patterns. By analyzing historical data and recommending optimizations, they help identify cost-saving opportunities. This leads to a more efficient and agile operation, ultimately contributing to increased revenue. AI can also provide insights into areas where investments can yield the most significant returns, guiding your financial decisions.
Are you a financial leader hoping to learn more about the applications of AI, including increasing revenue with its help? Consider enrolling in one of these courses in 2024.
- Cash Flow Optimization
AI provides valuable insights into cash flow patterns, enabling your organization to make informed decisions about investments and liquidity management. This optimization ensures that your available cash is working for you, helping you maximize revenue generation while mitigating financial risks.
AI can also analyze data from multiple channels, such as social media, online reviews, and customer feedback, to better understand customer behavior and purchasing preferences. This information helps you tailor your products or services accurately. You can then better meet the needs of your target audience, ultimately leading to increased revenue. If you are looking to expand your business, AI also helps you identify new target markets and demographics, allowing you to tap into previously untapped revenue streams.
- Market Expansion
Your organization can identify new markets or untapped opportunities by leveraging AI to analyze market data and consumer behavior. This strategic perspective allows you to expand and diversify your revenue streams, reducing reliance on a single market and improving long-term financial stability.
This point is important. Let’s dive deeper into this topic by exploring some specific ways AI can support revenue growth through market expansion.
Deeper Research
With AI’s ability to process and analyze large amounts of data in a fraction of the time it would take a human, your organization can conduct more thorough research into potential markets. This includes analyzing the cultural, economic, and social factors that affect consumer behavior in different regions.
With a deeper understanding of these nuances, you can tailor your products or services to better resonate with new target demographics. You can then expect to capture greater market share and increase revenue.
Identifying New Trends
AI algorithms can also identify emerging trends in consumer behavior or product demand, helping your organization stay ahead of the competition. By predicting these changes, you can adjust your marketing and sales strategies accordingly, allowing you to better meet customer needs and preferences. This proactive approach tends to lead to increased revenue and a competitive advantage in the marketplace.
Personalization at Scale
AI-powered personalization allows organizations to tailor their offerings to individual customers at scale. This improves the customer experience and increases the likelihood of repeat business and customer loyalty.
Because it analyzes data such as purchase history, browsing behavior, and social media activity, AI can create personalized recommendations and targeted marketing campaigns that speak directly to each customer’s needs and interests. This level of personalization can drive revenue growth by increasing conversion rates and fostering customer satisfaction.
- Reduced Customer Churn
AI-powered customer sentiment analysis can identify at-risk customers by detecting subtle changes in their behavior or communication. You can reduce customer churn and maintain a steady revenue stream by proactively addressing their concerns or needs. Retaining existing customers is almost always more cost-effective than finding new ones.
Furthermore, statistics show that the success rate of selling to an existing customer is 60-70%. In contrast, the success rate of selling to a new customer is only 5-20%. This highlights the importance of keeping your current customers happy. Fortunately, AI can help you achieve this by identifying and addressing potential churn risks.
- Credit Risk Assessment
AI’s advanced ability to assess the credit risk associated with customers, partners, or suppliers is truly invaluable. Leveraging cutting-edge algorithms and machine learning enables your organization to minimize bad debt, identify potential risks, and make informed credit decisions.
For starters, this protects your revenue and financial stability. But at the same time, it improves the company’s overall efficiency and profitability. With AI as your trusted ally, you can confidently navigate the complex credit management landscape and ensure long-term success.
- Real-Time Financial Reporting
Implementing AI-powered reporting tools gives your organization real-time insights into financial performance. This allows you to quickly react to changing market conditions, identify trends, and seize revenue opportunities as they arise. Real-time reporting fosters agility and adaptability in financial decision-making.
Datarails helps with this – with it, you can embrace automation to speed up month-end closing. In turn, it reduces time spent on manual data collection and allows your finance team’s expertise to shine.
- Scenario Planning
AI can simulate various business scenarios by analyzing historical and real-time data. This capability allows your team to anticipate potential challenges and devise strategies to maximize revenue in different situations. Whether it’s planning for economic downturns, supply chain disruptions, or market fluctuations, AI-powered scenario planning equips your organization with the tools to thrive in a volatile business landscape.
Considerations for Using AI to Increase Revenue
As powerful and beneficial as AI is proving to be, this doesn’t mean you should blindly jump in and implement it without careful consideration.
Below are some key considerations to keep in mind when using AI to increase revenue:
– Data Quality and Governance
– The effectiveness of AI depends on the quality, quantity, and accuracy of the data available for analysis. Again, this means you need to have proper data governance practices in place so that your data is clean, organized, and trustworthy. This involves establishing transparent processes for data collection, storage, and maintenance.
Privacy and Security
With the rise in AI use comes concerns about how you handle sensitive data. Make sure your organization has strong privacy policies in place to protect customer information from misuse or unauthorized access. A variety of security measures, including restricted access where appropriate, protect your data from cyber threats.
Ethical Considerations
AI algorithms are only as unbiased and ethical as the data they are trained on. It is critical to be aware of any potential biases in your data that could lead to discriminatory outcomes. Regularly reviewing and updating AI models with diverse and inclusive data sets can mitigate these risks.
Human Oversight and Collaboration
AI should not be seen as a replacement for human decision-making, but rather as a tool to augment and support it. Human oversight is necessary to develop, deploy, and monitor AI technologies. Collaboration with employees from different departments also provides valuable insights to optimize AI solutions.
Return on Investment (ROI)
Before investing in any AI technology, evaluate its potential ROI. Consider the costs of implementation, training, and maintenance in relation to the benefits it can bring to your organization. Evaluating ROI helps you determine whether AI is a worthwhile investment for your business.
5 Ways AI Can Help Boost Your Revenue
This post is by Glenn Gow, a regular opinion columnist for Forbes magazine, CEO coach, featured speaker on AI, and board member (glenngow.com). This article was written in 2023 and is still fully relevant.
According to McKinsey, organizations that are going “all in” on AI attributed at least 20 percent of their 2022 EBIT to using the technology.
One in five dollars of pre-tax profits came from AI
The survey was conducted with 1,684 organizations around the world, representing a variety of industries and company sizes. Not all had adopted AI. And not all that had adopted it could claim such impressive results. But in those companies that are characterized by high AI performance, one-fifth of their gross profits already come from AI.
It’s only 2023, and AI is already rapidly disrupting the competition.
I advise, give talks, and write about the growing impact that artificial intelligence will have on productivity (see article “What to do with all the productivity?”). But I have to admit that this finding even blew me away. After all, ChatGPT, arguably the most well-known and immediately accessible form of AI, was only released to the public in November 2022. Generative AI is unlikely to have played a major role in those 2022 results.
It’s pretty clear what that predicts for 2023 results: organizations that have really gone all-in on AI are already outperforming their competitors that haven’t, in terms of profits. How are they doing it?
High-performing companies are using AI to increase revenue, not just cut costs
Unsurprisingly, AI is increasing profits simply by enabling greater efficiency and fewer staff, for example in:
– sales and marketing functions for design, content writing, customer and trend analysis, and personalization
– customer service, in the form of advanced chatbots and sentiment prediction
– HR, for recruiting, performance management, and optimizing workforce deployment.
But the real takeaway from the McKinsey survey was that AI is increasingly contributing to revenue growth, not just profits. High-performing companies that embrace AI are using the technology primarily to create new business ideas and new revenue streams, not to cut costs. In fact, only 19% of high-performing companies, compared with 33% of all other respondents, are using AI to cut costs.
Instead, top-performing companies are using technology to:
– create new business units or other revenue streams (23%)
– increase revenue from the core business (27%)
– increase the value of current AI offerings (30%)
Here are some ideas on how you can increase revenue with AI
- Identify niche markets:
One way AI can support revenue is by identifying consumer demographics and preferences to uncover market opportunities. Frito-Lay, for example, used AI to identify that one in six residents of Frisco, Texas, was ethnically Indian. AI then recommended that the company offer a spicy snack in Frisco that it previously sold only in India, allowing them to gain a huge market advantage in Frisco. (See the article “How AI Can Help in a Recession”)
- Support adoption:
Whether for new or existing products and services, AI can also help you improve your offering and increase market share by analyzing customer sentiment. AI-powered customer service platforms like IrisAgent and Intercom use broad language model AI to identify product bugs, pain points, and customer desires among logged support tickets. That provides product managers with clear, real-time feedback on how products can be improved and which features can meet market demand.
- Demand forecasting:
In 2022, a building materials company heeded warnings from AI that predicted an increase in the frequency and intensity of hurricanes in the Deep South. The company moved much of its inventory to warehouses in Florida. When Hurricane Ian hit weeks later, it was the only company with enough local stock to meet demand. The company didn’t have to change anything in its product line or marketing — it just had to be prepared. (Given the frequency of severe weather events like Hurricane Idalia in 2023, this type of forecasting could also be used by insurance companies, government aid agencies, and others.)
- Create new products:
AI can even add new revenue streams by creating entirely new products. For example, in the pharmaceutical world, INS018_055 is a new drug against pulmonary fibrosis currently in clinical trials, which was discovered and designed by Hong Kong-based InSilico Medicine using entirely AI. But you don’t have to work in the pharmaceutical industry to create new products with AI. AI is being used to design and redesign products from industrial components to golf clubs. Large language models can even be used to analyze trends on the internet and suggest products that meet industry demand.
- Optimize prices:
For more than a decade, economists and marketers have understood that raising price by just 1% without losing sales results in a nearly 9% increase in operating profit. The problem has been, until now, determining which products could withstand a price increase and how much they could withstand. Now AI is being used to analyze market data and determine the ideal price to set for each product being sold, in each market where it is sold. In retail, for example, AI can incorporate data as diverse as zip code, competitor prices, season, day of the week, time of day, and even the weather.
AI is already critical to staying competitive.
If you are still only using AI to reduce headcount and costs, you may already be falling behind. Your competitors are using AI to develop new products, monitor the market, optimize pricing, forecast inventory demand, upsell, cross-sell, and more.
The game is on, and companies are working hard to corner markets, create a flywheel effect, and establish strong competitive barriers to entry, using AI. (See the article “Winner Takes All.”)
If you’re interested in how AI determines winners and losers in business, how you can leverage AI to benefit your organization, and how you can manage AI risk, I encourage you to stay tuned. I write and speak about how senior executives, board members, and other business leaders can use AI effectively. You can read past articles and receive notifications of new articles by clicking the «Follow» button here.
The economic potential of generative AI: The next productivity frontier
This contribution is from the McKinsey & Company report –The economic potential of generative AI: The next productivity frontier
Generative AI is poised to unleash the next wave of productivity. We take a first look at where business value could accrue and the potential impacts on the workforce.
Key concepts
- Where business value lies
Where business value lies
- Industry impacts
Industry impacts
- Implications for work and productivity
- Considerations for business and society
The economic potential of generative AI: The next productivity frontier
AI has permeated our lives incrementally, through everything from the technology that powers our smartphones to self-driving features in cars to the tools retailers use to surprise and delight consumers.
As a result, its progress has been almost imperceptible.
Clear milestones — like when AlphaGo, an AI-based program developed by DeepMind, defeated a world Go champion in 2016 — were celebrated, but then quickly faded from the public consciousness.
Generative AI apps like ChatGPT, GitHub Copilot, Stable Diffusion, and others have captured the imagination of people around the world in a way that AlphaGo didn’t, thanks to their broad utility (almost anyone can use them to communicate and create) and their preternatural ability to hold a conversation with a user.
The latest generative AI apps can perform a variety of routine tasks, such as reorganizing and sorting data. But it’s their ability to write text, compose music, and create digital art that has won headlines and persuaded consumers and households to experiment on their own.
As a result, a broader set of stakeholders are grappling with the impact of generative AI on business and society, but without much context to help them understand it.
About the authors
The speed at which generative AI technology is developing doesn’t make this task any easier. ChatGPT launched in November 2022. Four months later, OpenAI released a new large language model, or LLM, called GPT-4 with significantly improved capabilities. Similarly, in May 2023, Anthropic’s generative AI, Claude, was able to process 100,000 text tokens—equivalent to about 75,000 words in one minute (the length of an average novel)—up from about 9,000 tokens when it was introduced in March 2023. 2 And in May 2023, Google announced several new features powered by generative AI, including Search Generative Experience and a new LLM called PaLM 2 that will power its chatbot Bard, among other Google products. 3
To understand what lies ahead, it’s necessary to understand the advancements that have enabled the rise of generative AI, which have been in the making for decades. For the purposes of this report, we define generative AI as applications that are typically built using basic models.
These models contain expansive artificial neural networks inspired by the billions of connected neurons in the human brain. Baseline models are part of what is called deep learning, a term that alludes to the many deep layers within neural networks.
Deep learning has driven many of the recent advances in AI, but the base models that power generative AI applications are a radical evolution within deep learning. Unlike previous deep learning models, they can process extremely large and varied sets of unstructured data and perform more than one task.
Baseline models have enabled new capabilities and greatly improved existing ones across a wide range of modalities, including images, video, audio, and computer code. AI trained on these models can perform multiple functions: it can classify, edit, summarize, answer questions, and compose new content, among other tasks.
We are all at the beginning of a journey to understand the power, scope and capabilities of generative AI
This research is the latest in our initiatives to assess the impact of this new era of AI. It suggests that generative AI is set to transform roles and improve performance in functions such as sales and marketing, customer operations and software development. In the process, it could unlock trillions of dollars in value in sectors ranging from banking to life sciences. The following sections share our initial findings.
The impact of generative AI on productivity could add trillions of dollars in value to the global economy. Our latest research estimates that generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually across the 63 use cases we analysed – by comparison, the UK’s total GDP in 2021 was $3.1 trillion. This would increase the impact of all AI by between 15 and 40 per cent. This estimate would roughly double if we include the impact of incorporating generative AI into software currently used for other tasks beyond those use cases.
About 75 percent of the value that generative AI use cases could bring falls into four areas:
– customer operations.
– marketing and sales.
– software engineering.
– R&D.
Across 16 business functions, we examined 63 use cases where the technology can address specific business challenges in ways that produce one or more measurable outcomes.
Examples include generative AI’s ability to support customer interactions, generate creative content for marketing and sales, and write computer code based on natural language prompts, among many other tasks.
The State of AI in Early 2024: Generative AI Adoption Ramps Up and Begins to Deliver Value
Generative AI will have a significant impact across all industry sectors. Banking, high-tech, and life sciences are among the industries that could see the biggest impact as a percentage of their revenue from generative AI. In the banking industry, for example, the technology could generate an additional $200 billion to $340 billion in value annually if use cases were fully implemented. In retail and consumer packaged goods, the potential impact is also significant, at $400 billion to $660 billion annually.
Generative AI has the potential to change the anatomy of work
Augmenting the capabilities of individual workers by automating some of their individual activities.
Today’s generative AI and other technologies have the potential to automate work activities that take up 60 to 70 percent of employees’ time today. In contrast, we had previously estimated that the technology has the potential to automate half of the time employees spend working.
The acceleration of technical automation potential is largely due to generative AI’s increased ability to understand natural language, which is necessary for work activities that account for 25 percent of total work time.
Generative AI therefore has a greater impact on knowledge work associated with occupations that have higher wages and educational requirements than other types of work.
The pace of workforce transformation is likely to accelerate
Given the increased technical automation potential. Our updated adoption scenarios, which include technology development, economic feasibility, and diffusion timelines, lead to estimates that half of current work activities could be automated between 2030 and 2060, with a midpoint in 2045, or about a decade earlier than in our previous estimates.
Generative AI can substantially increase labor productivity across the economy, but that will require investments to support workers as they change work activities or jobs. Generative AI could enable labor productivity growth of 0.1 to 0.6 percent annually through 2040, depending on the rate of adoption of the technology and the reallocation of workers’ time to other activities.
When combining generative AI with all other technologies, work automation could add 0.5 to 3.4 percentage points annually to productivity growth. However, workers will need support to learn new skills, and some will change occupations. If worker transitions and other risks can be managed, generative AI could contribute substantially to economic growth and support a more sustainable and inclusive world.
The era of generative AI is just beginning
The enthusiasm for this technology is palpable, and early pilots are compelling. But fully realizing the technology’s benefits will take time, and business and society leaders still have considerable challenges to address. These include managing the risks inherent in generative AI, determining the new skills and capabilities the workforce will need, and rethinking core business processes such as training and developing new skills.
Using AI to Increase Revenue and Reduce Costs
This contribution is from the Interactive Advertising Bureau of Southeast Asia and India (IAB SEA+India) portal that enables media and marketing industries to thrive in the digital economy. The author is Shabana Badami from Google and a member of the IAB SEA+India Regional Innovation and AdTech Council.
Artificial Intelligence (AI) has recently come into the spotlight, and businesses and consumers alike are just beginning to understand this new technology. There is a huge platform shift taking place, as AI is considered the third major shift in technology, after the advent of the internet and then the transition to mobile devices. Both of these shifts brought about tremendous growth and opportunity, and so will AI. Simply put, AI is the most profound technology that humanity is working on today.
Given all the talk about AI, it’s helpful to start with a basic understanding of what it is. In its simplest definition, AI is a non-human program or model that can complete sophisticated tasks, solve problems, and make decisions. AI systems are trained on large amounts of data and can learn to perform tasks that would normally require human intelligence, such as recognizing objects in images, translating languages, and writing different types of creative content.
Generative AI is opening up a world of creativity
This generative capability (where an AI is given a prompt and creates something new based on it) is the one that has been in the spotlight most recently, with the popularity of ChatGPT and Google Bard.
Example: You can ask the AI tool to give you six more ideas for high-protein vegan recipes, or it can help you get started on that novel you’ve been meaning to write. You can ask it to write a packing list for this weekend’s camping trip, or suggest the best ways to introduce your daughter to scuba diving.
Generative AI can help you boost your productivity, speed up your ideas, and fuel your curiosity – and it’s already opening up a world of creativity.
Analytical and predictive AI is optimising the world around us
The other thing AI does incredibly well is analyse and make predictions – it can find patterns and connections in vast amounts of information that would be impossible for humans to identify.
It does this in the blink of an eye – it analyses data and produces predictions in real time.
Examples of this can be seen everywhere – from suggesting the greenest route, based on traffic, to improving the impact of advertising campaigns.
As AI continues to develop, it is likely to have an even greater impact on our lives.
Legal and ethical issues
But can we trust AI? As AI systems begin to be widely deployed in our economy, ethical and legal issues are magnified. Large language models essentially reflect an approximation of the world and social stereotypes – bias, discrimination, misinformation, fraud, veracity and copyright, among others.
It is essential to understand that AI, as a still-emerging technology, presents various complexities and evolving risks. Therefore, it is imperative to implement AI responsibly, which requires a collective effort. Researchers, developers, implementers, academics, civil society, governments and users, including individuals, businesses and other organizations, must work together to get AI working properly.
Unlocking entirely new possibilities to reach your full potential
Ultimately, AI’s greatest potential is in helping people, businesses, and communities unlock entirely new possibilities to reach their full potential, and we can see that AI is transforming marketing as well.
AI is a marketing multiplier
Your marketing expertise multiplied by AI can drive profitable growth and results, help reach customers where they are, no matter how unpredictable their behaviors, and increase creativity to create new value and greater impact.
McKinsey analyzed the effects of AI on marketing and found that 70% of companies that have adopted AI experienced increased revenue and nearly a third experienced cost reductions. And remember, this is very early, but we are already seeing a huge impact.
Here are some ways AI can help marketers:
– AI can help identify new growth opportunities by analyzing data and identifying trends.
– AI can help personalize marketing messages and campaigns to reach target audiences more effectively.
– AI can help automate tasks and processes, freeing up time to focus on more strategic initiatives.
– AI can help measure and track the results of marketing campaigns, so you can see what’s working and what’s not.
But the key part of this equation is you. Ultimately, AI learns from your work, and you learn from AI.
One of the biggest barriers to using AI effectively at work is legacy thinking, such as equating manual controls with the perception of greater control.
Today, it’s nearly impossible to manually change course quickly enough to keep pace with consumers and the market. AI-powered products are easier to adapt to business goals. They seamlessly connect data sources and deliver the results needed.
Using AI isn’t like swapping a slower car for a faster one
It’s like driving on a day when all the traffic lights turn green as you approach. And that doesn’t mean replacing the human – you’re still the driver – but working with AI to reach your goals faster and more easily.
What can AI do for your business?
First, AI can help you drive next-level demand and growth. Take McDonald’s for example: they used their app’s e-commerce data and AI to predict their audience’s in-app behaviors, identifying those most likely to buy. Armed with these valuable signals, they were able to find new customers much more effectively, increasing conversions by 550% and reducing cost per action.
Second, AI can help you unleash your productivity. GetYourGuide, for example, used AI in their creative production process to personalize and scale creative messaging across all of their digital channels. The results: Production time was reduced by 94% and 4.1 million creative messages were generated, driving click-through rates to double.
And finally, this technology will be truly transformative: it will enable businesses to make not just incremental improvements, but giant leaps. Imagine how a simple sketch can be turned into a high-resolution image in seconds, or turned into a fully rendered 3D model and placed directly into your design software. Different types of media and inputs can be used as AI prompts and stimuli to open the door to incredible opportunities in design and personalization.
Harnessing the Full Potential of AI’s Analytic, Predictive, and Generative Capabilities
And finally, let’s talk about what’s next…
Bold ways of working – those that allow us to harness the full potential of AI’s analytical, predictive, and generative capabilities – are just beginning to emerge. With these seemingly magical new powers, we have an open field of possibilities ahead of us.
- The first starts with getting the foundation right.
It all starts with data.
High-quality data (especially first-party and consented data generated from direct customer relationships) combined with value-focused measurement is the foundation for powering AI and steering it toward optimizing conversions that truly drive business results.
As third-party cookies are phased out, preparing for the future and creating a privacy-focused measurement strategy also ensures that you get a complete and accurate picture of performance to make confident optimization decisions.
And for deeper insights into your websites and apps, we recommend a robust analytics tool. It can prepare you for the future of measurement because it can offer predictive and modeling capabilities.
Once you have your database in place, activate it using AI-powered campaigns to find incremental and untapped conversion opportunities across a wide range of channels and inventory.
- Then, activate the full funnel to find your most valuable customers
To make the most of those customer connections across channels, make sure you’re showing relevant ads in as many placements and formats as possible by providing a wide and diverse range of creative assets.
Attracting the customers who will bring the most conversion value to a business is a marketer’s priority, especially when profit pressure mounts. So predicting who your most valuable customers are and finding them is key.
However, to get the most out of AI-powered bidding strategy, your marketing expertise is essential. First, you’ll need to define values for the conversions that are most worthwhile for your business, to steer AI in the right direction and optimize for them.
In particular, you should choose conversion goals that translate into real value, such as profit or customer lifetime value, rather than intermediate metrics like sales volume.
AI-powered bidding strategy intelligently analyzes and predicts the value of a potential conversion every time a user searches for products or services you advertise or interacts with your ads across multiple channels. It then automatically adjusts your bids to maximize your return on them.
- The last group focuses on mindset changes
AI requires a lot of change management with advertisers and agencies. It fundamentally changes the way they are used to managing campaigns. But the results and growth they can achieve are worth it.
As for the third point, the shift to AI may seem like a risk, but it can be approached gradually. Implementing a culture of experimentation is one of the most important actions we can take to move organizations forward incrementally and benefit from the full power of AI.
Staying agile throughout the process is also essential to breaking down common data, budget, and channel silos that can limit AI’s ability to optimize and maximize performance. When it comes to budget and channel agility, specifically, staying agile allows us to follow consumer demand, however and whenever it may be, and we can capture incremental performance opportunities as they arise. And all of this is made possible by directly aligning marketing KPIs with business goals and partnering with your leadership team to quantify marketing’s impact on key financial metrics.
When you think of marketing as a profitable growth driver for your business, it allows you to take a more agile approach to your budget to unlock its full growth potential. Analyze each additional dollar spent in terms of the ROI it brings to your company, and think about how you can continue to use that next dollar to drive more growth.
In conclusion, where are we?
Well, AI is already here and two things are certain: you are probably already using it and there is likely more we can do with it.
The technology has the potential to give businesses a powerful competitive advantage, but it needs guidance, and that guidance comes from you and your experience. With valuable input like these, AI systems can learn and improve over time, meaning the sooner you lean into them, the better performance you will get.
AI optimization will become a discipline in its own right and businesses should consider creating a center of excellence within their organization to help share and scale learnings.
Consumers are using AI and their expectations are rapidly increasing. They want quicker rather than instant answers and they want a wide range of options. Once they have made a decision, they want more of what they have chosen. AI can help businesses keep pace with this change and connect with customers faster and at scale.
Finally, the proof of success: this summary of the industry discussion was written with the help of AI.
Now is the time for businesses of all sizes to start thinking about their AI-powered marketing strategy. We are excited about what is possible when we combine our knowledge and experience with the power of AI.
This information has been prepared by OUR EDITORIAL STAFF