Generative AI For Business

In my last article, I spoke about the basics of generative AI.

This article will give you a clear understanding of how GEN AI can be used in your business for different purposes.

This article covers the sections:

1. What Is Generative AI?
2. Understanding Economical Impacts
3. Practical Examples Of Generative AI In Business
4. Benefits Of Using Generative AI
5. Areas Of Massive Impact
6. Outro

The era of generative AI is just beginning. The excitement around the technology is palpable, and early pilots are compelling. However the full benefits of the technology will take time to realize, and business and civic leaders still face significant challenges. These include managing the risks inherent in generative AI, determining what new skills and capabilities the workforce will need, and rethinking core business processes such as retraining and developing new skills.

Nevertheless, Generative AI is poised to unleash the next wave of productivity in business.

GEN AI gained mainstream attention around November 2022, when OpenAI released ChatGPT, and its momentum has continued unabated: according to 麦肯锡 , it could add $2.6 to $4.4 trillion annually to the economy.

高盛 estimates that it could increase global GDP by 7% over the next decade, and a study by OpenAI and 美国宾夕法尼亚大学 estimates that it could impact 10% of the work or tasks performed daily by over 80% of workers in the United States.

The same study also estimates that there are 20% of workers whose jobs or tasks are more than 50% impacted by generative AI, and so studies like these lead to hope for tremendous productivity gains as well as concerns about job loss due to automation.

Some of the benefits organizations can realize with generative AI include:

• Increasing workforce productivity
• Personalizing the customer experience
• Accelerating R&D through generative design
• Creating new business models

What Is Generative AI?

This term refers to artificial intelligence systems that can produce high-quality content, especially text, images, and audio.

One of the best-known generative AI or GenAI systems is OpenAI's ChatGPT, which can follow instructions to perform tasks such as writing three captions for a social media post about a new line of sunglasses and generating a variety of creative output.

Many users are familiar with websites or direct-to-consumer applications that can generate text like this. Examples include Google's Bard and Microsoft's Bing Chat. But there are now many companies that offer user interfaces that let you type some text, called a prompt, and generate a response.

But beyond these consumer applications, there's another application of generative AI that I think could be even more impactful in the long term, and that's the use of generative AI as a developer tool - take GitHub Copilot as an example, which was built on OpenAI GPT-3.5.

AI is already pervasive in our lives, and many of us use it dozens of times a day or more without even thinking about it. Every time you do a web search on 谷歌 or Bing, that's AI. Every time you use your credit card, there's probably an AI checking to see if it's you using your credit cards or not.

Understanding Economical Impacts

Generative AI's impact on productivity could add trillions of dollars in value to the global economy. Recent research estimates that generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually across the 63 use cases analyzed - by comparison, the entire GDP of the United Kingdom in 2021 was $3.1 trillion. This would increase the impact of all artificial intelligence by 15 to 40 percent. This estimate would roughly double if we included the impact of embedding generative AI in software currently used for other tasks beyond these use cases.

About 75 percent of the value that generative AI use cases could deliver falls into four areas: Customer Operations, Marketing and Sales, Software Engineering, and R&D. Across 16 business functions, there are 63 use cases in which the technology can address specific business challenges in a way that produces one or more measurable outcomes. Examples include generative AI's ability to support customer interactions, generate creative content for marketing and sales, and design computer code based on natural language prompts, among many others.

Banking, high-tech, and life sciences are among the industries that could see the greatest impact from generative AI as a percentage of their revenue. Across the banking industry, for example, the technology could add between $200 billion and $340 billion in value annually if use cases are fully implemented. In retail and consumer packaged goods, the potential impact is also significant at $400 billion to $660 billion annually.

Current generative AI and other technologies have the potential to automate work activities that consume 60 to 70 percent of workers' time today. By contrast, it was previously estimated that technology has the potential to automate half of the time workers spend at work. The acceleration in the potential for technical automation is largely due to generative AI's increased ability to understand natural language, which is required for work activities that account for 25 percent of total work time. Thus, generative AI has a greater impact on knowledge work, which is associated with occupations that have higher wages and educational requirements, than on other types of work.

Generative AI can significantly increase labor productivity across the economy, but this will require investments to support workers as they shift work activities or change jobs. Generative AI could enable labor productivity growth of 0.1 to 0.6 percent annually through 2040, depending on the rate of technology adoption and the reallocation of workers' time to other activities. 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 make a substantial contribution to economic growth and support a more sustainable, inclusive world.

Practical Examples Of Generative AI In Business

Gen AI applications create content, but what content and in what business context?:

1. Image Generation and Speech Synthesis: Generative AI has shown enormous progress in creating realistic images and synthesizing human-like speech.
2. Customer-Facing Functions: Businesses are using generative AI for customer-facing functions such as sales, marketing, and customer service. This includes tasks like creating engaging marketing campaigns and enhancing the customer experience
3. IT Functions and Software Development: Generative AI is being used in IT functions, including software development, to automate various processes and tasks
4. Professional Services and Business Operations: Major consulting firms like Accenture are using generative AI to help clients create smarter business strategies, roadmaps, and operations. This includes enhanced search, document summarization, and automated communications
5. Productivity Boosts: Generative AI is being leveraged to streamline and automate processes, assist in managing task loads, and optimize user experiences in various software applications
6. Faster Product Development and Enhanced Customer Experience: Generative AI can lead to faster product development, improved customer experience, and enhanced employee productivity. It can be used to create realistic images, text, music, and videos, as well as to automate and optimize various tasks and processes

Benefits Of Using Generative AI:

Generative AI offers several benefits for businesses, including:

1. Faster Product Development: Generative AI can accelerate the product development process by creating and testing design alternatives, thereby reducing time-to-market
2. Enhanced Customer Experience: By personalizing interactions and automating routine tasks, generative AI can improve the overall customer experience, leading to increased satisfaction and loyalty
3. Improved Employee Productivity: Generative AI can streamline and automate various processes, allowing employees to focus on more complex and high-value tasks, thus boosting overall productivity
4. Creativity Amplification: Generative AI enables businesses to generate creative and engaging content at scale, such as advertising materials, music, and videos, thereby amplifying creativity and innovation
5. Optimized Decision-Making: Generative AI can help businesses generate alternative scenarios, test hypotheses, and make predictions, enabling more proactive decision-making and reducing uncertainty
6. Cost Reduction and Efficiency: Generative AI can help reduce costs by optimizing designs, materials, and processes, leading to improved operational efficiency and resource utilization
7. New Career Opportunities: Incorporating generative AI knowledge into employees' skill sets can open up new career opportunities and enhance their capabilities

Areas Of Massive Impact

Based on a wide variety of scenarios, there are 3 areas in which Generative AI will have a particularly strong influence.

Customer operations: Improving customer and agent experiences

The integration of generative AI stands to transform customer service operations comprehensively, enhancing both the user experience and the efficiency of agents. Digital self-service capabilities are bolstered, and the proficiency of agents is enriched through this technology. Its adoption in customer service sectors is increasing, thanks to its ability to automate customer interactions in natural language.

Here's how generative AI will upgrade specific operational aspects:

For customer self-service, AI-powered chatbots can deliver instant, tailored responses to intricate queries, transcending language or location barriers. By enhancing interaction quality through automation, it could take on a greater share of customer queries, freeing up human agents for more complex issues. Our findings indicate that almost half of the customer interactions for North American banks, telecoms, and utility firms are machine-managed, not solely by AI. Generative AI could potentially halve the load on human-operated services, contingent on the current automation level.

In terms of initial contact resolution, generative AI can swiftly access customer data, aiding representatives in resolving queries at first contact.

To reduce response times, this AI can support human representatives by providing real-time assistance and suggesting follow-up actions.

Regarding sales uplift, generative AI's rapid analysis of customer data and browsing habits can pinpoint tailored product recommendations and promotions. It also can improve quality assurance and training by analyzing customer dialogues, identifying improvement areas, and coaching agents accordingly.

Generative AI in customer service could boost productivity by 30 to 45 percent of the present operational costs.

Different assessment reflects the direct benefits generative AI could have on customer service efficiency. It does not include the secondary benefits on customer loyalty and satisfaction that stem from an enhanced experience, such as a deeper understanding of customer needs that enables agents to offer more personalized assistance and advice.

Marketing and sales: Boosting personalization, content creation, and sales productivity

This innovative technology crafts messages that resonate on a personal level, tapping into individual customer interests and behaviors, while streamlining content creation processes such as drafting initial versions of advertisements, headlines, slogans, and social media content.

Marketing Enhancements with Generative AI

Incorporating generative AI into marketing requires a nuanced strategy due to potential issues such as intellectual property concerns and data bias. These risks necessitate a human touch for oversight and strategic direction.

Here are the advantages generative AI can bring to the marketing table:

- Streamlined Content Production: Generative AI can condense the content development cycle, harmonizing branding elements across various mediums. It fosters collaboration, merging diverse ideas into unified narratives, thus heightening message personalization across customer segments and regions.

- Data Insight Utilization: Marketing efforts can be supercharged by generative AI’s knack for sifting through and making sense of disparate data streams. This leads to sharper marketing tactics, precise customer profiling, and more insightful market analysis.

- SEO Enhancement: Generative AI aids in optimizing digital content for search engines, ensuring content reaches its intended audience effectively.

- Personalized Product Discovery: By analyzing text, image, and speech inputs, alongside customer behavior, generative AI refines product search and discovery, tailoring e-commerce experiences to boost conversion rates.

Projections indicate a 5-15% boost in marketing efficiency, as a percentage of total marketing expenditure, thanks to generative AI.

Beyond direct productivity gains, generative AI can enrich data analysis, sparking innovative marketing strategies and refined customer targeting. This could reallocate resources toward producing premium content for proprietary channels, potentially reducing dependence on paid media and external agencies.

Sales Revolution with Generative AI

Generative AI is set to redefine sales approaches for both B2B and B2C entities through the following applications:

- Lead Prioritization: By assembling detailed consumer profiles, generative AI can guide sales teams to engage more effectively with potential clients, enhancing the likelihood of a sale.

- Lead Cultivation: Sales representatives can benefit from generative AI’s capacity to collate relevant product information and customer data, crafting conversation guides that aid in up-selling and cross-selling, and automating routine follow-ups.

Studies anticipate that generative AI could uplift sales productivity by about 3-5% of global sales outlay.

This forecast doesn't fully encompass the potential revenue generative AI might generate. By identifying and nurturing leads more efficiently, generative AI could significantly amplify revenue streams and empower sales representatives to foster more meaningful customer relationships, further boosting sales outcomes.

Software engineering: Speeding developer works as a coding assistant

As software engineering evolves, treating coding languages like any other language has unlocked novel avenues. Generative AI is now assisting software engineers in co-development scenarios, augmenting coding efforts, and training language models to craft code from simple natural-language instructions.

Software engineering is not only pivotal for tech giants but also for a multitude of companies incorporating software into diverse products and services. The added value in modern vehicles, for instance, often stems from their digital capabilities such as advanced driver-assistance systems and internet connectivity.

Research suggests that AI could enhance the productivity of software engineering by about 20 to 45 percent of the current spend on this function. This boost is primarily due to time savings in activities like drafting initial code, debugging, refactoring, and creating new system architectures. Generative AI is shifting the skill demand in software engineering towards more sophisticated design and architecture. For example, a study observed that developers using AI tools like GitHub Copilot were 56 percent more efficient than those who did not. Furthermore, a McKinsey study reported that teams trained in generative AI tools not only worked more swiftly but also enjoyed a heightened sense of job satisfaction.

This analysis does not factor in the potential improvements in application quality and the subsequent productivity gains that could stem from AI-enhanced coding or IT architecture enhancements, which can ripple across the entire IT landscape. It's important to note that the essence of IT architecture still relies heavily on the expertise of human software architects, despite the helpful initial coding that generative AI offers.

Leading tech corporations have already begun commercializing generative AI for software engineering, with tools like GitHub Copilot—now powered by OpenAI’s GPT-4—and Replit, which boasts a user base of over 20 million coders.

Product R&D: Reducing research and design time, improving simulation and testing

The transformative impact of generative AI in R&D may not be as widely recognized as in other business sectors, but it holds substantial promise. Our findings suggest that generative AI could potentially enhance R&D productivity, contributing a value between 10 to 15 percent of total R&D expenditures.

In sectors like life sciences and chemistry, generative AI is already making strides with what's termed generative design. These advanced models are being utilized to conjure up new molecular structures, significantly speeding up the creation of new drugs and materials. For instance, Entos, a biotech firm, has harnessed generative AI alongside automated synthetic development tools to innovate in small-molecule therapeutics. This principle extends beyond the pharmaceutical domain, with applications in designing an array of products, from large physical objects to intricate electrical circuits.

The advent of generative design has already begun to tap into AI's potential within R&D, but the costs and intense data demands of more conventional machine learning methods can be prohibitive. In contrast, generative AI's foundation models—especially those further refined—offer a wider scope of application, shortening the time to market and diversifying the product designs that generative methods can tackle. Yet, these models currently do not span the full spectrum of industrial product design.

Generative design not only speeds up the conceptual phase but can also refine the designs themselves. The operational enhancements from generative AI could include:

- Cost-Efficient Design: Generative AI aids designers in cutting expenses by better material selection and usage, as well as refining designs for easier manufacturing, potentially reducing logistics and production costs.

- Elevated Testing and Quality: Products of higher quality with greater market allure can emerge from using generative AI in design processes. It can streamline the testing of complex systems and hasten customer trial stages by quickly generating test scenarios and identifying suitable candidates.

Another emerging R&D application is in non-generative deep learning surrogates, which have seen increased use since our previous studies. When combined with generative AI, these surrogates could further expedite design testing, although this will necessitate bespoke solutions.

While we've quantified generative AI's direct potential in studies on R&D functions, its capacity to forge entirely new product categories—those that can radically shift company performance and economic expansion—remains an untapped reservoir of opportunity.

GEN AI offers a wide range of advantages to enterprises, including creativity amplification, improved productivity, enhanced customer experience, and optimized decision-making. The best way to get started in your own company is by analyzing your potential for AI-driven solutions based on your data.

I'm offering a free I offer business clients a free 30-minute consultation to understand how AI-based solutions can lead to more effectiveness, cost savings, and profit maximization in your industry.

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x Alexander Stahl