Digital technology perspectives: AIs roles in economics and decision-making

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The AIs are reshaping and revolutionising political economy’s patterns by driving efficiency, innovative decision-making, as well as facilitating new business models in growth sectors and industries. There are already apparent positive shifts in AIs deployment through such technologies as the Internet of Things (IoT), machine learning and big data analytics. Besides, new and exciting opportunities exist to increase efficiency, personalised customer experiences and reshaping industrial, corporate sectors and commerce. 

Background: shaping digital economy
Over the last decade, the rapid adoption of AI has transformed businesses and organisations that function as part of the newly formed digital and/or digitalised economy: i.e. from manufacturing, supply chain management and logistics to the financial sector, health, education, security, etc.
The core element of the digital economy is the wide spread and adoption of digital technologies – the so-called “digital-economy’s factor”: e.g. all the IT/ICT applications are already really producing functioning digital goods and services.
The true ‘digital economy’ – defined as “that part of economic output derived solely or primarily from digital technologies with a business model based on digital goods or services” – consists of the digital sector plus emerging digital and platform services. In the widest scope, the use of ICTs and other digital means in all economic fields are usually regarded as the “digitalised economy”.
One of the biggest challenges for businesses seeking to capitalise on AI is understanding the changes that need to accompany it. The Boston Consulting Group, BCG’s 10-20-70 rule advises CEOs to focus 10% of AI efforts on algorithms, 20% on tech and data, and 70% to supporting people and adapting business processes. It suggests that companies need to anticipate talent needs, attract best-in-class candidates and develop talent quickly.
Thus, artificial intelligence’s models act as the co-called pivotal drivers of the modern digital economy, transforming economic activities by increasing productivity, reshaping labor markets, and driving innovation across industries. As a general-purpose-technology (GPT), the AI models, alongside technologies like Big Data and the Internet of Things (IoT), are enhancing efficiency in manufacturing, logistics, and finance, with projections suggesting it could increase global GDP by 7% over the next 10 years.

Digital-productivity’s paradoxes
There are certain paradoxes in modern human existence: e.g. practical digital application’s tools using AIs are already surpassing human-level performance in numerous spheres and domains, leveraging rapid advances in other technologies and driving soaring stock prices.
However, productivity growth in the world has declined by half over the past decade; specifically, real income has stagnated since the late 1990s in most developed states. There are at least four feasible explanations for that: false hopes, mis-measurement and mis-management, redistribution, as well as implementation’s lags; some argue that the latter has been the biggest contributor to the mentioned paradox.
The most impressive capabilities of AI, particularly those based on machine learning, have not yet diffused widely: more than that, the full effects of the GenAI technologies would not be properly realized until various “complementary innovations are developed and implemented”. For example, “the adjustment costs, organizational changes and new skills needed for successful AI can be modeled as a kind of intangible capital”; some value of this intangible capital is already reflected in the companies’ capitalisation. However, note some experts, “national statistics could fail to measure the full benefits of the new technologies and some may even have the wrong sign”.
Reference and citations from: Erik Brynjolfsson, Daniel Rock, Chad Syverson. “Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics Get access Arrow”. 2019. In: https://doi.org/10.7208/chicago/9780226613475.003.0001

AI in economics
The economics of AI is concentrated on the AI models’ role as a general-purpose-technology (GPT) means that reduce the cost of prediction and cognitive tasks, potentially driving significant productivity growth. While accelerating corporate IT spending and offering 40–56% efficiency gains in specific tasks, AI’s overall economic impact is currently constrained by implementation costs, skill mismatches, and a focus on “easy-to-learn” tasks.
However, in thinking about this digital revolution, and the economic role of technology in general, there is a problem: economists in general don’t know enough about the technology to make an informed judgment; at the same time, digital technologists are not great at economics!
Source: https://paulkrugman.substack.com/p/how-should-we-think-about-the-economics

The global economy appears to be on the precipice of another disruptive surge of innovation: thus, the AIs are able to automate routine manual tasks in both the manufacturing and services sectors. However, while some jobs will be lost, new roles/jobs will be created that require greater problem-solving skills and creativity.
AI can analyze vast amounts of data quickly and accurately, providing valuable insights for decision-making. Businesses use AI to predict consumer behavior, optimize supply chains, and personalize marketing efforts. By processing and interpreting large datasets, AI systems can identify trends and patterns that may not be immediately apparent to human analysts, enabling more informed and strategic decision-making.

AI’s corporate and economic facets
Businesses and organisations can use these free systems to create their own advertising or promotional content, uploading and refreshing past content, saving both time and money from having to commission such services separately. They are also used to create online customer services which, according to the Boston Consulting Group (BCG), once implemented, can increase productivity in the margin of 30-50 percent.
Conversational AI bots can resolve frequently asked questions, collect customer data and user feedback, give personalised product recommendations, optimise multichannel customer communications, target advertising, qualify leads and reduce the number of abandoned shopping carts. Erica, the Bank of America’s AI-powered virtual financial assistant has helped more than 41 million clients and over two billion interactions since its launch in 2018.
More and more, generative AI is filtering through to other industries – from healthcare to Hollywood. Disney’s latest Indiana Jones instalment, for example, used cutting-edge face replacement machine learning (ML) technology to transform 79-year-old Harrison Ford to move like a 35-year-old.
Google DeepMind is using AI to predict the structure of proteins and molecules to help scientists at its sister company Isomorphic Labs develop drugs to target diseases more effectively. Similarly, Japan’s SoftBank Group has set up a joint venture with Tempus AI to analyse personal medical data to come up with personalised treatment recommendations.
AI also has a pivotal role to play in establishing highly efficient, connected and smart manufacturing operations. The global AI in manufacturing market was valued at $3.2 billion in 2023, but is forecast to rise to $20.8 billion by 2028.
For example, cobots (i.e. collaborative robots) can work safely alongside their human colleagues performing various operations, such as glueing, welding, picking and packaging, or detecting equipment damage and product defects. When integrated with sensors and wearable technology, they can also warn factory workers about any hazards on the shop floor.
The International Federation of Robotics (IFR) cites Carrier Transicold, a company in the home appliance industry with over $20 billion in revenue, as an example of best practice: i.e. it gained over 8,000 hours of productivity annually, after installing a compact 6-axis cobot.
The cobot’s ability to assist with labor-intensive tasks like attaching access doors on refrigeration units freed up human workers to focus on more complex and higher-value operations. The company’s adoption of radio frequency identification (RFID) has also improved inventory tracking reducing human error and boosting operational efficiency.
Reference to: https://online.kcl.ac.uk/blog/whats-the-impact-of-ai-on-the-digital-economy

AIs key economic aspects
= Microeconomics (Tasks & Firms): AI behaves as a “machine of the mind,” often automating tasks rather than whole jobs, increasing task complexity, and impacting productivity at the individual worker level.
= Macroeconomics (Growth & Labor): AI can increase GDP by raising productivity and creating new tasks. However, it risks worsening income inequality, promoting industrial concentration, and potentially causing labor market disruptions if not managed.
= Investment & Hype: AI is the fastest-growing item in corporate IT budgets, with significant investment driven by high expectations of future artificial general intelligence (AGI).
= Productivity Puzzle: Early evidence suggests substantial productivity gains, particularly in coding and writing, but broad macroeconomic benefits may take time due to “adjustment costs” and the need for organizational changes.
= Economic Impacts and Risks.
= Capitalizing on Gains: Research indicates that three-quarters of AI’s economic gains are being captured by only 20% of firms, specifically those focusing on business model reinvention rather than just efficiency.
= The “30% Rule”: Emerging adoption trends suggest AI works best by automating 70% of repetitive tasks while keeping humans for 30% oversight and creativity.
= Shift in Economic Goods: AI is altering the nature of goods, making “cognitive goods” cheaper and more abundant, which challenges traditional valuation and pricing models.
= Adoption Risks: Over-investment in unproven AI tools, driven by “hype,” could lead to poor capital allocation, making a deliberate, analytical approach necessary.

AIs roles in modern economics
Artificial Intelligence models have been transforming from mere digital technology tools into a fundamental driver of the global economic capital cycle, actively influencing risk pricing, capital allocation and rapid labor restructuring.
The following AI’s roles in economics are becoming vital:
= Boosting productivity and efficiency: AI optimizes production processes and supply chains, reducing costs and accelerating operational speeds. In manufacturing, collaborative robots (cobots) improve efficiency, with the sector’s market value projected to rise significantly by 2028.
= “Transforming” the business models: generative AI allows firms to create new content, improve customer service and personalize experiences, with AI bots capable of improving productivity by 30-50%. Companies that adopt AI technologies can gain a competitive edge by improving efficiency and offering superior products and services. This, in turn, can lead to increased market share and profitability. By staying at the forefront of AI adoption, businesses can differentiate themselves from competitors and capture new opportunities in emerging markets.
= Facilitating macro-economic growth: AI is expected to boost global GDP by around 0.5% annually between 2025 and 2030, with economic gains projected to outweigh the costs of increased energy demand.
= Innovative data analytics: AI aids in drug discovery (e.g., protein structure prediction) and enhances research and development (R&D) efficiency, fostering growth in new sectors. AI fosters innovation by enabling the development of new products and services. Businesses are using AI to create personalized experiences, develop smart devices, and explore new markets. By harnessing AI’s capabilities, companies can push the boundaries of what is possible, leading to breakthroughs that drive economic progress and enhance quality of life.
More in: https://www.linkedin.com/pulse/ais-economic-reality-markets-jobs-capital-2026-edosa-odaro-itp2e

Challenges and economic implications
The following factors shall be taken into consideration:
= Inequality and Digital Divide: AI readiness varies widely, with advanced economies better positioned to capture AI benefits (up to 20-25% net economic benefit) compared to developing countries (5-15%). High-income countries with strong STEM talent and infrastructure tend to benefit faster, risking a wider global economic divide.
= Energy and Sustainability: AI’s high energy consumption poses a challenge, with electricity demand for AI potentially tripling by 2030, requiring policies for green energy transition.
= Ethics and Regulation: Key challenges include ensuring data privacy, accountability, and preventing algorithmic bias or market concentration.

The future digital economy will be defined by “human-AI symbiosis,” where AI handles data-intensive analysis while humans focus on strategic and creative tasks. Continued development in AI is expected to move toward autonomous decision-making and better integration with other technologies like blockchain and IoT.
This projection reflects a shift from augmentation talk toward substitution potential, underscoring how quickly the narrative around AI’s role in work is evolving within industry leadership. This has sparked debate about workforce design, human-computer interaction and strategic priorities across enterprises.
Reference to: https://www.linkedin.com/pulse/ais-economic-reality-markets-jobs-capital-2026-edosa-odaro-itp2e

       Bottom line: the role of artificial intelligence in the economy is expected to grow, with AI becoming an integral part of many industries. As technology advances, AI will continue to drive economic change and present new opportunities for businesses and workers alike.
By addressing ethical challenges and embracing innovation, the governments can harness the full potential of AI to create a prosperous and inclusive economies.
More in: https://www.goldmansachs.com/insights/articles/how-will-ai-affect-the-global-workforce