GenAI Value Creation in Your Business
Unlocking the Value of Generative AI: The Flywheel Approach
The latest advancements in generative AI (GenAI) are causing a stir across businesses, and with good reason. PwC's CEO survey highlights the widespread expectation that GenAI will revolutionize how companies operate and generate value. A structured approach, inspired by the concept of a flywheel, can help organizations maximize their return on investment in this transformative technology.
While GenAI's potential is vast, the specific benefits and challenges might differ across industries. Our research indicates that sectors like software development stand to see significant profit margin gains by integrating GenAI into their current workflows. Even industries with more modest projected benefits should investigate how GenAI could enhance their competitive edge.
These figures, focusing on immediate productivity improvements, might underestimate GenAI's long-term disruptive power. As market expectations adjust, companies that successfully leverage GenAI are well positioned to lead the charge in innovation, reshaping how value is created.
The flywheel model offers a helpful framework for guiding GenAI implementation. Remember, a flywheel takes initial effort to get moving. However, once in motion, it gains momentum making it easier to accelerate and conserve valuable resources. Similarly, a well-planned GenAI strategy builds momentum, increasing efficiency and paving the way for transformative change within your organization.
Crucially, companies must address the ethical implications of GenAI from the outset. A responsible AI approach must be woven into every stage of GenAI integration to ensure its positive impact.
STAGE 1 | Crafting a Compelling Value Hypothesis
Start by evaluating both the potential gains and the challenges associated with various GenAI use cases in your organization. Your mission statement, financial position, operational structure, industry landscape, and regulatory environment will all inform this initial assessment. Compare this to industry-wide projections as a benchmark for progress.
GenAI promises tangible short-term efficiency boosts. Studies indicate that a significant amount of time spent on meetings and emails could be optimized. However, keep your sights set on the bigger picture – complete reinvention of processes and business models, a more ambitious goal that GenAI makes attainable.
STAGE 2 | Prioritizing the Right GenAI Uses
With your value hypothesis outlining potential gains, the next step is to pinpoint GenAI use cases that have the highest potential to contribute across your entire value chain. Focusing on these high-impact applications delivers a stronger return on investment and builds early support for future initiatives.
We've analyzed hundreds of GenAI use cases in various sectors, and each organization may have its own niche needs. However, our research shows that the top five GenAI applications within most industries can unlock 50-80% of the total value attainable via this technology. Let's look at some examples:.
Personalized Experiences at Scale: Consider the luxury retail sector, where personalizing the customer experience is crucial. GenAI can analyze customer history and preferences to deliver hyper-customized marketing, significantly improving customer service and boosting sales.
GenAI Coding Assistants Boost Productivity: Software developers are already reaping productivity benefits from GenAI coding assistants. These tools handle the bulk of coding tasks, freeing up developers to focus on quality control and strategic direction.
Maximizing Business Value with GenAI
The most beneficial use cases will vary even within a single industry. Analyze seven key considerations to identify those with the highest value potential for your business:
Data Availability and Quality: Does sufficient data exist, and is it structured well enough for GenAI?
Technical Feasibility: Is the technology mature enough to execute the use case reliably?
Business Impact: What tangible benefits would it offer (cost reduction, revenue increase, etc.)?
Speed to Value: How quickly could the application be deployed and generate results?
Regulatory and Ethical Alignment: Does it comply with relevant regulations and ethical AI principles?
Potential for Competitive Advantage: Could it set your company apart from the competition?
Scaling Potential: Can it be easily adapted or expanded within your organization?
STAGE 3 | Harnessing the Power of Patterns
GenAI solutions often need customization for maximum impact. While off-the-shelf models might work for some tasks, adding your company's unique data and tailored functionality often leads to more meaningful results. The beauty of these adaptations is that they can often be replicated and repurposed, revealing patterns that help scale the technology across your organization.
These patterns allow you to think creatively about how to apply learnings from one GenAI deployment to other related areas. Research indicates that the most significant GenAI gains will come from net-new creation and process augmentation. These may require longer development cycles and more upfront resources, but the potential payoff is substantial. By contrast, simple tasks like summarization and dialogue, for which GenAI is already known, offer more modest returns.
Let's illustrate this with an example. A communications provider sought to leverage GenAI's ability to analyze vast amounts of legal documents. The initial focus was on improving contract creation, where the value was easily recognizable. Once the company-specific GenAI contract analysis tool was developed, it could be adapted for procurement, real estate, and even HR contracts, extracting maximum value for a relatively minimal additional investment.
STAGE 4 | Selecting the Right GenAI Foundation
Once you've identified high-value use cases and potential scaling patterns, it's time to evaluate which foundational GenAI technologies best serve your needs. Remember, the goal here is to balance the technology's power with adaptability and avoid future "tech debt" that might arise from short-sighted solutions.
Start by considering the appropriate GenAI foundation models, supporting technologies, and cloud platforms, along with potential service providers and partners. Here's a breakdown of some options:
Public GenAI Models: Tools like OpenAI's ChatGPT, Google's Gemini, or Anthropic's Claude might be sufficient for basic use cases. However, sensitive business data might require a private, more secure version hosted within your own environment.
Specialized GenAI Needs: Applications like drafting RFPs based on your company's unique capabilities or providing personalized services with private data won't work with standard models. Here, you might need to apply specialized knowledge through retrieval-augmented generation (RAG) or by fine-tuning existing models. For highly niche applications in industries like pharmaceuticals, you might need to train your own bespoke GenAI models from scratch.
STAGE 5 | Maximizing Value with Tailored Solutions
The next step is to decide how you will augment your foundational tools to create highly specific solutions that maximize value. For many, the key to competitive advantage will not lie in the tools themselves but in how they use their proprietary data. This approach involves additional governance and risk management challenges, potentially raising the costs of development and deployment.
For maximum value with minimal effort, think laterally across applications via the patterns identified earlier. For instance, a GenAI chatbot initially developed for customer service could, with some modifications, assist your workforce in person-to-person interactions. From there, it's not a huge leap to adapt it further for internal use in sales training or product development. Each additional use case might individually have a modest impact, but the cumulative effect builds powerful momentum.
STAGE 6 | Costs and Environmental Impact
Once you have a potential roadmap for GenAI integration, carefully assess the associated costs – financial, environmental, and even reputational. Productivity gains should be an early focus, with an eye toward improving margins. Assessing the longer-term potential of net-new revenue generation enabled by GenAI is trickier but crucial.
While automation through GenAI can reduce labor costs, blindly replacing humans with AI could damage your brand and reduce human oversight, potentially increasing risk exposure.
Don't underestimate GenAI's environmental impact. Routine usage can be energy-intensive, but it's a complex picture. Analyzing GenAI from a carbon emissions perspective must consider both the energy consumed and the potential reduction of emissions it can enable in other areas through broader efficiency gains.
STAGE 7 | Deploy, Test, Iterate
With a technology as rapidly evolving as GenAI, testing becomes crucial to learning. Each deployment, equipped with success metrics and controls, should be viewed as an opportunity to gather insights. These learnings inform how you capture value, measure future wins, and adjust your risk and governance strategy before scaling solutions across the organization.
This iterative approach is exemplified by PwC, which spent the last year as "client zero" for GenAI. Our partnership with the startup Harvey focused on tax, law, and HR GenAI solutions built upon the OpenAI's ChatGPT foundation. The initial trial phase quickly showcased the power of the tool's summarization and retrieval abilities and highlighted effective ways of working. As a result, the deployment evolved from a mere time-saver into a continuous experiment in reinventing established processes.
STAGE 8 | Scaling for Success
The final phase of the flywheel leverages the knowledge and experience gained through careful assessment, development, and testing. Now is the time to make the adjustments needed to scale successful GenAI applications across the organization. Early deployments offer proof of concept and valuable lessons that should fast-track buy-in, ease the adoption process, and accelerate the time it takes to see results as you build institutional knowledge.
Consider a global beverage company that applied this adaptive approach. Starting with GenAI-enabled predictive maintenance in its factories, the company identified patterns that could be applied to its transportation and logistics operations. The next iteration took it even further, enhancing crop yield efficiency through precision agriculture powered by the same underlying GenAI principles.
This holistic, system-wide view allowed the company to develop end-to-end supply chain planning capabilities using similar predictive analytics tools. This level of integration would traditionally take years, but the flywheel approach helped them achieve it with greater speed and efficiency by building upon each successful project.
Seizing the GenAI Opportunity
While the flywheel framework guides value discovery, the ease of capturing that value will vary. The bottom line is that GenAI, when thoughtfully implemented, can kickstart an organization-wide journey of continuous learning and cumulative value creation.
Of course, numerous factors like your technology stack, company culture, regulations, and competitor landscape influence the likelihood of success. One of the most critical factors (which we'll explore in future work) is the industry in which you operate.
We've already seen advanced implementation and transformative change in some sectors with high disruptive potential and minimal barriers to GenAI adoption. Others with less disruption risk might still find significant value in GenAI, potentially gaining a crucial edge over less tech-forward competitors.
Regardless of your starting point, the flywheel model accelerates the process of identifying the most impactful GenAI applications in your business. Its focus on building momentum through learning and adaptation gives companies an advantage in this ever-changing landscape.
The Importance of Responsible AI
With GenAI's powerful capabilities comes the need to proactively address potential risks and ethical concerns. Here are some key areas of focus:
Bias and Fairness: Be mindful of inherited biases in the data that trains your GenAI models. Regular auditing, bias mitigation efforts, and ongoing monitoring are essential for fair outputs.
Transparency and Explainability: Many GenAI models are "black boxes" with difficult-to-understand decision-making processes. Focus on explainability to maintain trust and accountability.
Safety and Security: Without safeguards, GenAI can be misused for generating harmful content or exploited by malicious actors. Robust security measures are critical.
Privacy and Data Protection: Respect privacy and adhere to data protection regulations when working with potentially sensitive personal information.
Ethical concerns extend beyond mere legal compliance. A responsible AI approach builds trust among customers and employees, leading to long-term success.
The Leadership Factor
Top-down support is vital for GenAI success. Leaders should:
Clearly articulate the vision: Define how GenAI will transform your company and generate value, aligning teams around this shared goal.
Enable experimentation: Employees need freedom to explore, learn from failures, and iterate solutions.
Invest in skills development: Equip your workforce with GenAI skills needed to execute your strategy.
Balance innovation with governance: Set responsible AI guidelines while fostering creative exploration.
Leaders play an active role in shaping their organization's approach to GenAI, ultimately determining the impact this technology will have.
Getting Started with GenAI
GenAI is still a relatively new technology, and companies are bound to have varying degrees of experience. If you're just beginning your GenAI journey, consider these initial steps to lay a strong foundation. Start by educating yourself and key decision-makers about GenAI fundamentals, its benefits, and its associated risks. Explore use cases and industry-specific examples to spark ideas and identify how GenAI could potentially transform your operations. Additionally, initiate small-scale pilot projects focusing on low-risk, high-potential applications. These early experimentations deliver valuable insights and build momentum for larger implementations later on.
An often overlooked but equally critical step involves assessing your data readiness. Evaluate the quality of your existing data, pinpoint any gaps, and determine how much effort will be required to make your data GenAI-ready. Data preparation often represents a large portion of the time and resources invested. Finally, choose the right partners. Seek out seasoned GenAI vendors or consultants for guidance, access to advanced tools, and implementation expertise. Evaluate potential partners carefully, ensuring their commitment to security and ethical practices aligns with your own.
Remember, this technology is evolving rapidly. As a result, companies need to stay adaptable to maintain a competitive edge. Leaders must embrace flexibility and be willing to adjust course when needed, ensuring the organization remains well-positioned within the dynamic GenAI landscape.
Conclusion
Generative AI offers vast potential for transforming businesses across industries. Companies taking calculated risks and proactively implementing GenAI stand to reap significant benefits in operational efficiency, innovation, and long-term value creation. However, it's vital to approach GenAI with a strong sense of responsibility, mitigating risks and ensuring ethical implementation to secure sustainable growth and positive impact. Those who strike this balance and harness the flywheel effect to accelerate GenAI adoption will be well-positioned to thrive in this new era of technological disruption.
