Generative AI is advancing at an unprecedented pace, leaving CEOs to wonder whether it is a passing trend or a game-changing opportunity. With the rapid adoption of tools like ChatGPT, Bard, and Claude, generative AI has become more accessible than ever. In just two months, the public-facing version of ChatGPT reached 100 million users, making it the fastest-growing app in history. This democratisation of AI allows anyone who can ask questions to interact with it, highlighting its transformative potential.

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Understanding Foundation Models

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Foundation models are the backbone of generative AI. These expansive neural networks are trained on vast amounts of unstructured, unlabeled data, such as text, images, and audio. Unlike previous AI models that were limited to specific tasks, foundation models can perform a wide range of activities. This versatility makes them a powerful tool for various applications, from drafting marketing content to generating software code.

The term "foundation model" refers to their ability to serve as a base for multiple applications. For instance, a single foundation model could be used to draft marketing content, assist customer service representatives, and generate software code. This versatility is a double-edged sword, as it requires businesses to implement robust risk management practices to ensure the accuracy and reliability of the AI's outputs.

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The Business Value of Generative AI

Generative AI offers numerous advantages for businesses, including improving existing processes, unlocking new opportunities, and enhancing customer experiences.

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Improving Existing Processes

Generative AI can streamline and optimise various business processes. For example, in customer service, AI can handle routine inquiries, freeing up human representatives to focus on more complex issues. This not only improves response times but also enhances job satisfaction for employees.

In marketing, generative AI can automate content creation, generating personalised messages for different customer segments. This capability can significantly boost the effectiveness of marketing campaigns by ensuring that the right message reaches the right audience at the right time.

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Unlocking New Opportunities

Generative AI enables businesses to explore new opportunities that were previously out of reach. In product development, AI can analyse vast amounts of data to identify trends and predict customer preferences, leading to more innovative products.

In research and development, generative AI can accelerate the discovery process by generating hypotheses, designing experiments, and analysing results. This can be particularly valuable in industries such as pharmaceuticals, where the ability to quickly identify promising drug candidates can save significant time and resources.

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Enhancing Customer Experiences

One of the most exciting aspects of generative AI is its potential to transform customer experiences. By leveraging AI to understand customer behaviour and preferences, businesses can deliver highly personalised experiences that enhance customer satisfaction and loyalty.

For example, in retail, AI can provide personalised product recommendations based on a customer's browsing history and past purchases. In financial services, AI can offer personalised investment advice tailored to an individual's financial goals and risk tolerance.

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Practical Applications of Generative AI

Generative AI can be transformative in various industries. Here are some practical examples:

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1. Software Engineering

Software development is a labour-intensive process that often involves extensive trial and error. Generative AI can significantly speed up this process by assisting with code completion and debugging. Developers can describe what they want to achieve in natural language, and the AI can suggest several code blocks that meet the criteria. This not only enhances productivity but also improves the quality of the software by reducing the likelihood of errors.

Furthermore, generative AI can help in maintaining and updating legacy code. By understanding the context of existing code, AI can suggest optimisations and refactoring options, making the codebase more efficient and easier to manage. This is particularly beneficial for companies with large, complex software systems that require regular updates.

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2. Relationship Management

In industries such as finance and consulting, relationship managers (RMs) play a crucial role in maintaining client relationships. However, staying informed about clients' needs and market trends requires sifting through large amounts of data, such as annual reports and earnings call transcripts. Generative AI can assist RMs by quickly analysing these documents and providing synthesised insights.

For example, an AI tool can scan through a client's financial statements and highlight key performance indicators, potential risks, and growth opportunities. This allows RMs to spend more time on strategic discussions with clients rather than on data analysis. Additionally, AI can help in preparing personalised pitches and proposals, further enhancing the value delivered to clients.

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3. Customer Support

Customer support is an area where generative AI can have a significant impact. By fine-tuning AI models on customer interactions and sector-specific knowledge, businesses can deploy AI chatbots to handle routine inquiries. These chatbots can provide instant responses, improving customer satisfaction and reducing the workload on human representatives.

Moreover, generative AI can assist support representatives by suggesting responses during live interactions. For example, if a customer inquires about a product return policy, the AI can instantly pull up the relevant information and draft a response for the representative to review and send. This not only speeds up the resolution process but also ensures consistency in customer communication.

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4. Drug Discovery

The pharmaceutical industry is heavily reliant on research and development, and generative AI can accelerate this process. Drug discovery involves analysing vast amounts of data from clinical trials, research papers, and chemical databases. Generative AI can help researchers identify patterns and make predictions about the efficacy of new compounds.

For instance, AI can analyse microscopy images to identify cellular responses to different drug candidates. By predicting which compounds are likely to be effective, AI can help narrow down the list of candidates for further testing, saving time and resources. This accelerates the development of new drugs and can potentially bring life-saving treatments to market faster.

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Strategic Implementation for CEOs

For CEOs, the journey with generative AI should start with a clear strategy:

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1. Organising for Success

Successful implementation of generative AI requires a coordinated approach. CEOs should convene a cross-functional leadership team comprising representatives from data science, engineering, legal, cybersecurity, marketing, design, and other business functions. This team can identify and prioritise high-value use cases and ensure coordinated and safe implementation across the organisation.

2. Reimagining Business Domains

Generative AI has the potential to transform entire business domains. Rather than applying AI sporadically, companies should focus on domains where AI can have the most significant impact. For example, a retailer might focus on marketing and customer service, while a manufacturer might prioritise operations and supply chain management. By integrating generative AI with traditional AI applications, businesses can achieve maximum impact.

3. Enabling a Modern Tech Stack

A modern data and tech stack is crucial for the successful deployment of generative AI. Companies need robust computing resources, data systems, tools, and access to models. A scalable data architecture with strong governance and security procedures is essential. CEOs should work with their chief technology officers to assess and upgrade their tech stack as needed.

4. Building a 'Lighthouse'

To demonstrate the potential of generative AI, companies should start with proof-of-concept projects. These projects can showcase the benefits of AI and build momentum for broader adoption. Early wins can generate excitement and encourage other teams to explore AI applications. A 'lighthouse' project serves as a model for future initiatives and helps the organisation learn and refine its AI strategy.

5. Balancing Risk and Value

Implementing generative AI involves balancing value creation with risk management. Companies should establish ethical principles and guidelines for AI use, ensuring that AI applications align with the organisation's overall risk tolerance. Staying updated with regulatory developments is crucial to protect the business from liability issues. By addressing risks proactively, companies can harness the full potential of generative AI while maintaining compliance.

6. Fostering Partnerships and Talent

Building a balanced set of alliances with generative AI vendors and experts can accelerate implementation. Companies do not need to build all AI applications in-house; partnering with specialists can provide access to the latest technology and expertise. Additionally, companies should focus on upskilling their current workforce and hiring the right talent. Training employees on AI tools and techniques ensures that they can effectively leverage AI in their roles.

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The Role of CEOs in Generative AI Adoption

The CEO plays a crucial role in catalysing a company’s focus on generative AI. Here are some strategies that CEOs should keep in mind as they begin their journey:

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Organising for Generative AI

Many organisations began exploring the possibilities for traditional AI through siloed experiments. Generative AI requires a more deliberate and coordinated approach given its unique risk considerations and the ability of foundation models to underpin multiple use cases across an organisation. For example, a model fine-tuned using proprietary material to reflect the enterprise’s brand identity could be deployed across several use cases (for example, generating personalised marketing campaigns and product descriptions) and business functions, such as product development and marketing.

To that end, we recommend convening a cross-functional group of the company’s leaders (for example, representing data science, engineering, legal, cybersecurity, marketing, design, and other business functions). Such a group can not only help identify and prioritise the highest-value use cases but also enable coordinated and safe implementation across the organisation.

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Reimagining End-to-End Domains vs. Focusing on Use Cases

Generative AI is a powerful tool that can transform how organisations operate, with particular impact in certain business domains within the value chain (for example, marketing for a retailer or operations for a manufacturer). The ease of deploying generative AI can tempt organisations to apply it to sporadic use cases across the business. It is important to have a perspective on the family of use cases by domain that will have the most transformative potential across business functions. Organisations are reimagining the target state enabled by generative AI working in sync with other traditional AI applications, along with new ways of working that may not have been possible before.

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Enabling a Fully Loaded Technology Stack

A modern data and tech stack is key to nearly any successful approach to generative AI. CEOs should look to their chief technology officers to determine whether the company has the required technical capabilities in terms of computing resources, data systems, tools, and access to models (open source via model hubs or commercial via APIs).

For example, the lifeblood of generative AI is fluid access to data honed for a specific business context or problem. Companies that have not yet found ways to effectively harmonise and provide ready access to their data will be unable to fine-tune generative AI to unlock more of its potentially transformative uses. Equally important is to design a scalable data architecture that includes data governance and security procedures. Depending on the use case, the existing computing and tooling infrastructure (which can be sourced via a cloud provider or set up in-house) might also need upgrading. A clear data and infrastructure strategy anchored on the business value and competitive advantage derived from generative AI will be critical.

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Building a 'Lighthouse'

CEOs will want to avoid getting stuck in the planning stages. New models and applications are being developed and released rapidly. GPT-4, for example, was released in March 2023, following the release of ChatGPT (GPT-3.5) in November 2022 and GPT-3 in 2020. In the world of business, time is of the essence, and the fast-paced nature of generative AI technology demands that companies move quickly to take advantage of it. There are a few ways executives can keep moving at a steady clip.

Although generative AI is still in the early days, it’s important to showcase internally how it can affect a company’s operating model, perhaps through a “lighthouse approach.” For example, one way forward is building a “virtual expert” that enables frontline workers to tap proprietary sources of knowledge and offer the most relevant content to customers. This has the potential to increase productivity, create enthusiasm, and enable an organisation to test generative AI internally before scaling to customer-facing applications.

As with other waves of technical innovation, there will be proof-of-concept fatigue and many examples of companies stuck in “pilot purgatory.” But encouraging a proof of concept is still the best way to quickly test and refine a valuable business case before scaling to adjacent use cases. By focusing on early wins that deliver meaningful results, companies can build momentum and then scale out and up, leveraging the multipurpose nature of generative AI. This approach could enable companies to promote broader AI adoption and create the culture of innovation that is essential to maintaining a competitive edge. As outlined above, the cross-functional leadership team will want to make sure such proofs of concept are deliberate and coordinated.

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Balancing Risk and Value Creation

As our four detailed use cases demonstrate, business leaders must balance value creation opportunities with the risks involved in generative AI. According to our recent Global AI Survey, most organisations don’t mitigate most of the risks associated with traditional AI, even though more than half of organisations have already adopted the technology. Generative AI brings renewed attention to many of these same risks, such as the potential to perpetuate bias hidden in training data, while presenting new ones, such as its propensity to hallucinate.

As a result, the cross-functional leadership team will want to not only establish overarching ethical principles and guidelines for generative AI use but also develop a thorough understanding of the risks presented by each potential use case. It will be important to look for initial use cases that both align with the organisation’s overall risk tolerance and have structures in place to mitigate consequential risk. For example, a retail organisation might prioritise a use case that has slightly lower value but also lower risk—such as creating initial drafts of marketing content and other tasks that keep a human in the loop. At the same time, the company might set aside a higher-value, high-risk use case such as a tool that automatically drafts and sends hyper-personalised marketing emails. Such risk-forward practices can enable organisations to establish the controls necessary to properly manage generative AI and maintain compliance.

CEOs and their teams will also want to stay current with the latest developments in generative AI regulation, including rules related to consumer data protection and intellectual property rights, to protect the company from liability issues. Countries may take varying approaches to regulation, as they often already do with AI and data. Organisations may need to adapt their working approach to calibrate process management, culture, and talent management in a way that ensures they can handle the rapidly evolving regulatory environment and risks of generative AI at scale.

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Applying an Ecosystem Approach to Partnerships

Business leaders should focus on building and maintaining a balanced set of alliances. A company’s acquisitions and alliances strategy should continue to concentrate on building an ecosystem of partners tuned to different contexts and addressing what generative AI requires at all levels of the tech stack, while being careful to prevent vendor lock-in.

Partnering with the right companies can help accelerate execution. Organisations do not have to build out all applications or foundation models themselves. Instead, they can partner with generative AI vendors and experts to move more quickly. For instance, they can team up with model providers to customise models for a specific sector, or partner with infrastructure providers that offer support capabilities such as scalable cloud computing.

Companies can use the expertise of others and move quickly to take advantage of the latest generative AI technology. But generative AI models are just the tip of the spear: multiple additional elements are required for value creation.

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Focusing on Required Talent and Skills

To effectively apply generative AI for business value, companies need to build their technical capabilities and upskill their current workforce. This requires a concerted effort by leadership to identify the required capabilities based on the company’s prioritised use cases, which will likely extend beyond technical roles to include a talent mix across engineering, data, design, risk, product, and other business functions.

As demonstrated in the use cases highlighted above, technical and talent needs vary widely depending on the nature of a given implementation—from using off-the-shelf solutions to building a foundation model from scratch. For example, to build a generative model, a company may need PhD-level machine learning experts; on the other hand, to develop generative AI tools using existing models and SaaS offerings, a data engineer and a software engineer may be sufficient to lead the effort.

In addition to hiring the right talent, companies will want to train and educate their existing workforces. Prompt-based conversational user interfaces can make generative AI applications easy to use. But users still need to optimise their prompts, understand the technology’s limitations, and know where and when they can acceptably integrate the application into their workflows. Leadership should provide clear guidelines on the use of generative AI tools and offer ongoing education and training to keep employees apprised of their risks. Fostering a culture of self-driven research and experimentation can also encourage employees to innovate processes and products that effectively incorporate these tools.

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Conclusion: Embracing the Future with Generative AI

Generative AI represents a significant leap forward in AI capabilities, offering businesses new possibilities for innovation and efficiency. By strategically implementing generative AI, companies can transform their operations, enhance customer experiences, and gain a competitive edge. CEOs must act decisively and thoughtfully to harness the full potential of this transformative technology, ensuring their organisations are well-positioned to thrive in the future.

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