In our previous article we defined generative AI (GenAI) as a form of artificial intelligence that not only analyzes existing data but also generates new content. GenAI thus takes on capabilities that, until very recently, were assumed to be unique to human beings. But as we explained, it cannot entirely replace human beings for a number of reasons.
GenAI cannot even pass the Turing Test, other than at a very superficial level. The Turing Test, proposed by British mathematician Alan Turing in 1950, asks whether a machine can exhibit intelligent behavior indistinguishable from a human’s behavior in conversation. If a human interrogator can’t reliably tell whether they’re talking to a machine or a person, the machine “passes” the test.
Many users of ChatGPT and other GenAI apps are surprised by how natural, fluent, and context-aware its responses seem. It really does feel as though you are interacting with a human being. But probe a little more deeply and cracks begin to show. First, even a robust GenAI program such as ChatGPT can have “hallucinations”, giving daft answers to apparently straightforward questions. Second, it lacks memory of past sessions. A human being can correct it one day, and the application will have forgotten that correction a day or two later. Third, as a machine system, GenAI application lacks consciousness. It can be trained to feign emotions, but these are superficial.
However, most tellingly, artificial intelligence lacks self-awareness, lived experience, or real-world grounding, it reveals itself as a machine quite quickly. Ask ChatGPT, “How did you feel the first time you fell in love?” and you’ll see what I mean.
GenAI in Modern Business Landscapes
The foregoing sets the scene for what GenAI can do in a modern business environment, and equally importantly, what it cannot do. It’s good to start with a clear understanding of what, in future, will be the business of artificial intelligence, and what will be the business of human intelligence. Let’s set the scene by briefly describing some use cases across business.
Financial Services
Whereas conventional AI has long been used for risk management, credit scoring and other predictive analytics, generative AI can be used to deliver reports on market trends for investors and analysts. Personalized financial recommendations generated by AI are becoming increasingly common, but bear in mind that these are not infallible! This is an area where hallucinations can occur due to lack of context, such as the financial swings that can happen in minutes rather than days.
In insurance, generative AI solutions can speed up claims processing, fraud detection and risk assessment. They can also be used to analyze policies, automate underwriting and improve customer interactions, though regulatory compliance remains a key consideration.
Creative Arts
GenAI tools can now generate high-quality graphic and video content, reducing production costs and enhancing creative possibilities. However, critics point out that the datasets used to train AI models often include copyrighted content, leading to unresolved questions about ownership. There are also concerns about the quality of output, much of which is clearly derivative. It will take time to resolve these issues.
Healthcare
Generative AI is making life sciences more efficient and responsive by assisting with medical documentation, diagnostics, patient engagement and drug discovery. In new drug creation, GenAI helps with the modeling of molecular structures, predicting the effectiveness of new compounds and accelerating the development of novel treatments. However, in these and other medical use cases there are also ethical issues around patient privacy and data bias. For example, if an AI system is trained on data from a specific demographic group, its accuracy may be lower for other groups, leading to misdiagnosis or inappropriate treatment recommendation.
Human Resources
Generative AI streamlines the recruitment, retainment and development of talent. For example, it can sift through thousands of CVs to identify a shortlist of the best-qualified candidates, generate structured annual review templates and create innovative staff training programs.
Legal and Compliance
GenAI relieves legal and compliance officers of much of their routine work, for example by generating customized contracts and producing detailed reports to regulators.
Education
Likewise, GenAI can be used to develop engaging and relevant educational content and teaching materials pitched at different age groups and tailored to local or special educational needs. In addition, it is increasingly used for automating administrative tasks. However, here too there are concerns, in particular around misinformation and academic integrity.
Enhancing Creativity & Innovation
There is a common misconception that, while GenAI can help with creative work such as art and design or music, it’s of limited use for other areas of innovation. In fact, the reverse is true. There are legitimate concerns about using GenAI for artistic purposes as the previous work it draws upon, by human artists, goes uncredited and unrewarded. But in the wider realms of business this is less of a concern.
Creativity and innovation are essential for growth and profitability in business. However, human beings often find it difficult to break out of conventional ways of doing things, “tried and tested” processes, management hierarchies, group-think and timidness. We’ve all sat through brainstorming sessions where, despite their avowed purpose and participants being told that no idea is too outlandish, everybody clams up for fear of being ridiculed. The more forceful personalities in the group — the extroverts — tend to dominate, and good ideas are often forgotten in pursuit of bad ones, a process known as “production blocking”. No wonder that research has shown that groups brainstorming together produce fewer ideas than individuals working separately.
This is where generative AI can help with ideation, precisely because it is impersonal and it can generate many ideas very quickly, so there is no production blocking. Computers don’t suffer from the biological restrictions that cause people to lose focus or run out of energy. Nor does AI suffer from the creative blocks that hold humans back. That said, human intervention is still needed to pose the right questions, evaluate the responses from GenAI, and refine the ideas with further enquiry.
Opportunities to innovate with generative AI augmentation include both client-facing applications, such as marketing and product visualization, and internal use cases, such as strategic planning and analysis.
Streamlining Operations
GenAI can significantly automate routine tasks and processes thanks to its ability to learn and generate new data, mimicking human capabilities to streamline workflows and reduce manual intervention. This automation can lead to increased efficiency, higher productivity, and faster turnaround times, freeing up human resources for more complex and strategic work.
Generative AI models like GPT are trained on vast amounts of text, code, images, or other data. During this training the AI learns patterns, relationships, and structures. For example, it learns how contracts are normally worded, how customer emails are typically handled, or how standard reports are formatted. It builds a statistical model of how language, processes, or data points fit together.
In short, GenAI can be trained on large datasets but then adapt as it absorbs new information, either from human prompts or from new data such as user interactions. This leads to greater process efficiency and streamlined operations, but it need not be impersonal. In fact, GenAI can be used to personalize customer experiences and create tailored content, leading to higher customer satisfaction and business value.
For example, an insurance company could fine-tune the deployment of artificial intelligence on its claims forms and policies so that it generates highly accurate draft claim responses.
Personalized Marketing and Customer Engagement
In particular, GenAI takes sales and marketing to the next level of hyper-personalization. Large language models (LLMs) can be used to output content for emails, blogs, social media posts, newsletters, websites etc., all aligned with the brand tone of voice and dynamically targeted at specific audiences. The content can automatically be localized for different regions and languages, maintaining brand consistency while reducing manual translation work.
Data Analysis and Decision Making
Even with recent advances in modern business intelligence, significant amounts of management time are spent on reviewing large quantities of data to reach decisions. By contrast, GenAI can scan huge datasets and summarize matters of concern and interest, such as trends, outliers and correlations, much faster than a human analyst. Decision-makers get executive summaries without needing to dig through thousands of rows of data or dozens of charts. For example, instead of manually reviewing monthly sales figures across regions, a Chief Revenue Officer could use artificial intelligence to generate a report highlighting regions whose performance requires attention or special measures.
Accessing insights from business data often requires technical skills, creating bottlenecks and delays in decision-making. GenAI allows managers and executives to query data in plain English, removing the need for specialist support. Instead of writing complex database queries or waiting for custom reports, users can ask direct questions such as “Which product lines delivered the highest margins last quarter?” and receive clear, accurate responses. For example, a Marketing Director could quickly obtain a ranked list of top-performing campaigns without needing to involve the analytics team.
Conventional forecasting relies heavily on static models and periodic human intervention to update assumptions. GenAI can analyze historical patterns and real-time data to deliver dynamic, continuously updated predictions, enabling decision-makers to anticipate future developments with greater confidence. For instance, a Chief Operating Officer could use artificial intelligence to forecast supply chain disruptions based on emerging trends, allowing time to identify alternative suppliers or reroute orders before problems escalate. GenAI can also quickly generate and evaluate multiple “what-if” scenarios, helping decision-makers assess risks and opportunities with minimal delay. A CFO considering a strategic acquisition could use artificial intelligence to simulate the financial impact under various market conditions, enabling a more informed and flexible negotiation strategy.
In addition to predictive analytics, GenAI can be harnessed to make recommendations, i.e., prescriptive analytics. GenAI analyzes the available information and proposes the next steps based on identified trends, risks, or opportunities. This approach helps decision-makers act faster and more effectively, particularly in high-pressure situations. For example, a Chief Customer Officer could receive AI-driven recommendations on which product improvements would most likely enhance customer satisfaction, based on recent feedback and usage data.
Cost Reduction and Resource Efficiency
We’ve seen some examples of how GenAI eliminates manual work to increase efficiency. But GenAI is also emerging as a powerful tool for energy and resource optimization, offering innovative solutions to some of the most pressing challenges in sustainability and operational efficiency.
In the energy generation sector, AI is revolutionizing power grid operations through real-time load balancing: AI models analyze consumption patterns and weather data to predict demand fluctuations, enabling utilities to optimize generation and distribution. By simulating various grid scenarios, AI can also identify potential failure points before they occur. AI helps manage the intermittency of solar and wind power by forecasting production and optimizing storage deployment.
In manufacturing and heavy industry, GenAI enables energy-efficient production planning. It can reduce energy consumption in factory processes by 30-50% compared to traditional methods. In addition, it can optimize raw material usage and reduce waste, especially in sectors such as chemicals and mining. Advanced monitoring of emissions throughout production processes can also help manufacturers to hit emission reduction targets. McKinsey estimates that generative AI could create $390−550 billion in additional value for energy and materials sectors through these optimizations.
In construction and urban planning, GenAI contributes to sustainable design through architectural optimization, generating building designs that maximize natural light and ventilation to reduce energy needs, and simulating different energy mix scenarios for cities and evaluating their environmental impact. Moreover, GenAI is accelerating the energy transition, for example through advanced renewable system design, such as more efficient wind turbine blades and high-capacity solar panels, as well as innovation in hydrogen production, carbon capture technologies and critical mineral management. Research by global technology company SLB has highlighted the importance of GenAI to the renewables sector.
However, the rise of generative AI also poses some difficult choices for society. Massive computational power is required to drive GenAI. Training GPT-4 required 50 times more electricity than GPT-3, according to the World Economic Forum. ChatGPT also requires about 500ml of water (a 16-ounce bottle) to generate 100 words of text. In addition, the GPUs required to handle AI workloads involves dirty mining processes and toxic chemicals.
New Product Development & Customization
Product designers now use generative AI throughout the product development lifecycle, from the initial concept to manufacturing and procurement, with feedback loops to integrate user and customer feedback for continuous improvement.
GenAI is used to generate multiple design variations (e.g., in engineering, fashion, or architecture), reducing R&D costs. AI models then simulate product performance, cutting physical prototyping expenses. Generative AI is revolutionizing product hyper-personalization by enabling mass customization at scale, creating products tailored to individual preferences, behaviors, and even biology.
A 2023 McKinsey study found hyper-personalization can increase revenue by 10-15% while reducing customer acquisition costs by 20%. The key is balancing AI’s creative potential with ethical constraints. When it is done right, it transforms customers from passive buyers to active co-creators.
We will take a deeper dive into NPD and customization in a separate article focused on GenAI and manufacturing.
Improving Customer Service & Support
We have all experienced AI chatbots and virtual agents when using a company’s online stores or resources. Until now, this has been the most common use case. They can engage in natural conversations, providing around-the-clock support and delivering context-aware responses. However, even the best have their limitations. Sometimes only a human voice will do, so they are best used in conjunction with human agents.
In technical support, if the generative AI is trained on millions of support tickets, it can learn common problems and typical solutions, enabling it to help users without needing a human to teach it or intervene case-by-case. A common objection, that such responses can come across as impersonal, can be overcome by fine-tuning each application to adapt to the tone and style of the organization. Such fine-tuning can be supervised (with humans correcting outputs) or unsupervised (where the AI absorbs patterns from company data automatically).
Future Prospects & Challenges
the opportunities presented by GenAI for business seem infinite. McKinsey estimates GenAI could add $4.4 trillion annually to global productivity, with sectors like retail, healthcare, and finance leading adoption. We have identified the main areas that offer further scope for businesses in many sectors: hyper-personalization and customer experience, operational efficiency, innovation and new product development etc.
However, there are many risks and challenges. These include ethical and regulatory issues, such as concerns about deepfakes and misinformation. The European Union’s AI Act and other global regulations may slow deployment in highly regulated industries. There are also significant implementation barriers, such as data quality and the talent shortage. Plus, huge costs (in terms of energy and resource usage) are incurred to train advanced GenAI systems.
Finally, there are cybersecurity risks from AI-powered attacks (e.g., phishing and deepfake scams). These require investment in AI-driven defense systems. Handling sensitive customer data with AI demands strict compliance with legal frameworks such as GDPR.
Conclusion
In future, GenAI will be present in much of our business activity and in our daily life as consumers. The scope is simply enormous whether it’s personalized product experiences, innovation and scientific breakthroughs or the energy transition. With the emergence of generative AI, industries will change radically as will our roles as leaders, managers and workers.
