Generative AI in Your Business Strategy: From Concept to Reality | by Baker Nanduru | Jun, 2023

Generative AI is poised to bring about a significant transformation in the enterprise sector. According to a study by McKinsey, the application of generative AI use cases across various industries could generate an astounding $2.6 trillion to $4.4 trillion in annual economic value.

The encouraging news is that most enterprises have already embarked on their artificial intelligence journey over the past decade years. Many have a well-defined AI strategy and have made considerable progress. However, the emergence of generative AI presents an opportunity for these enterprises to revisit their AI strategies and plans.

Generative AI has the potential to create economic impact within sales, marketing, software engineering & IT, customer operations, and R&D functions across various verticals. Industries such as high tech, banking, pharmaceuticals and medical products, education and telecommunications, healthcare, and insurance stand to gain immensely.

The onus is on executives and functional leaders actively driving AI strategies to identify new use cases, quantify opportunities, and refine their AI strategic plans to maximize business results.

Over 59% of US-based enterprises have already utilized at least one generative AI tool, with hundreds of millions of professionals enhancing their productivity through these innovative, accessible technologies. For enterprises that view artificial intelligence as a cornerstone of their business strategy, the time to double down on generative AI adoption is now. The risk of falling behind is real.

If you’re an executive who has invested millions in AI with mixed results, it’s time to refocus and explore the expansive opportunities generative AI offers with a fresh perspective. If your AI journey has already been successful, then it’s time to harness the power of new tools for an even broader impact.

Remember that we are still in the nascent stages of generative AI technology. Over the next two years, the technology will mature, becoming safer, more compliant, and transparent. Every week, AI creators launch thousands of new applications while AI vigilantes identify and address risks, particularly in areas such as bias, data privacy, transparency, safety, and regulatory compliance. There are concerns among professionals, and AI thought leaders about potential job displacement or loss.

However, as generative AI matures, consolidates, and thoughtful regulation is implemented, the benefits will likely outweigh the risks. Therefore, it’s time for enterprises to take the plunge and begin transforming their productivity landscape with the latest generative AI tools. The future of enterprise productivity is here, and generative AI powers it.

A Five-Step Methodology to Incorporate Generative AI into Business Strategy

Developers, technologists, and innovators across enterprises are already using the new tools to boost their individual productivity at work and at home. If you are an executive at an enterprise, here is a five-step process to formally incorporate generative AI into your overall AI business strategy:

While every function can benefit from generative AI, as an executive, you should consider at least the following functions.

Customer operations: Generative AI will improve customer experience and agent productivity through digital self-service and augment agent skills. It can automate customer interactions using natural language, increasing issue resolution and reducing time spent.

Marketing and sales: Generative AI creates personalized communications and experiences for prospects and customers. It can help generate content for brand advertising, social media posts, product descriptions, and other campaigns. It helps marketers achieve higher conversion, lower acquisition costs, and help sales improve opportunity conversion, customer retention, and upsell and cross-sell opportunities.

Software Engineering: Generative AI drafts computer code based on natural language prompts, which reduces the time required for coding and debugging. It can help in automating and quality assurance processes.

Research and Development: Generative AI helps to generate new ideas, analyze data and create models, which can significantly speed up the R&D process.

Assign your functional leaders to identify work in their functions and map it to productivity zones.

The key goal of this step is to identify work that can benefit from generative AI. Functional leaders can use a simple productivity zone framework to classify work within a function (refer to Figure 1).

This productivity zone framework has two dimensions:

Task complexity: Refers to the degree of difficulty involved in performing a task. It can be influenced by several factors, like the number of steps involved, the level of skills or knowledge required, the amount of information that needs to be processed, and the degree of uncertainty.

Low-complexity tasks include processing payroll for employees, posting preapproved content on social media channels, entering invoices into the accounting system, tracking inventory levels for a product, etc.

High-complexity tasks include developing a comprehensive management strategy, conducting a market segmentation study, conducting a financial risk assessment, optimizing a supply chain network, etc.

Human Interaction: This refers to the degree to which a task or role requires communication, collaboration, or engagement with other people. This includes interactions with colleagues, customers, suppliers, or other stakeholders.

Low-human interaction tasks include updating employee records in an HR system, analyzing marketing data to access campaign performance, preparing financial reports, and monitoring production systems, etc.

High-human interaction tasks include conducting employee performance reviews, focus groups, discussing budget forecasts, coordinating with partners, etc.

By using Task complexity (low, high) and human interaction (low, high), we can create a 2×2, which shows four productivity zones of each function. Generative AI creates unique business benefits for each of the productivity zones.

Productivity Zone 1 — Low Task Complexity / Low Human Interaction:

Generative AI can automate simple tasks in this quadrant, increasing efficiency and cost savings. Key metrics include time saved, task completion rate, and cost savings from automation.

Productivity Zone 2 — High Task Complexity / Low Human Interaction:

Generative AI can significantly improve efficiency and accuracy in this quadrant, leading to cost savings. Key metrics include time saved, error reduction, and cost savings from reduced need for human labor.

Productivity Zone 3 — Low Task Complexity / High Human Interaction:

In this quadrant, generative AI can enhance the quality and efficiency of human interactions, leading to improved customer satisfaction and potentially increased revenue. Key metrics include customer satisfaction scores, conversion rates, and revenue per customer.

Productivity Zone 4 — High Task Complexity / High Human Interaction: In this quadrant, generative AI can enhance the quality of human interactions and decision-making, leading to improved customer satisfaction, better decision outcomes, and potentially increased revenue. Key metrics include customer satisfaction scores, decision accuracy, and revenue growth.

Functional leaders should engage in an activity to better understand how generative AI can be applied in their respective areas. This involves identifying all tasks within their function and grouping them accordingly. Once these tasks are grouped, they should be placed into the appropriate productivity zones.

These zones are determined based on two factors: task complexity, which can be high, medium, or low, and the level of human interaction required, which can also be high, medium, or low. This exercise will help leaders visualize where generative AI can most effectively implement within their function.

For example: In a sales function, lead generation might fall into the high complexity/low human interaction zone, while customer communication might fall into the high complexity/high human interaction zone.

After mapping the work to the productivity zones, the next step is identifying and documenting the use cases that could contribute the most value to your business. This involves deep diving into each work group within the productivity zones and understanding how generative AI can enhance their operations.

Start by analyzing each work group and identifying tasks that are repetitive, time-consuming, or require high accuracy. These are the tasks that are most likely to benefit from the application of generative AI. For instance, in the customer operations function, tasks such as responding to customer inquiries or complaints could be automated using generative AI, leading to faster response times and improved customer satisfaction. For instance, automating lead generation could increase the number of leads generated, while personalizing customer communication could improve conversion rates. Refer to Figure 2 for generative AI’s business objectives and potential benefits.

Next, quantify the potential impact of implementing generative AI for these tasks. This could be in terms of time saved, cost reduction, improved accuracy, or increased revenue. For example, automating the coding process in software engineering could significantly reduce the time and cost associated with coding and debugging, leading to faster product development and lower operational costs.

Finally, prioritize the use cases based on their potential impact and feasibility. Use cases that offer high impact and are relatively easy to implement should be prioritized. This step will provide a clear roadmap for implementing generative AI in your organization, ensuring you focus on the areas where it can deliver the most value.

Once you’ve identified and prioritized the use cases, the next step is to conduct a feasibility study. This involves assessing generative AI’s technical and operational feasibility for the identified use cases.

On the technical side, you need to evaluate whether your organization’s current infrastructure and capabilities can support the implementation of generative AI. This includes assessing your data infrastructure, computational resources, and the availability of technical skills within your team. You should invest in new technologies or upskill your team to implement generative AI successfully.

On the operational side, you need to consider how the implementation of generative AI will impact your existing processes and workflows. This includes assessing the potential changes to job roles, the need for new processes or protocols, and potential employee resistance. It’s important to plan for these changes and manage them effectively to ensure a smooth transition.

The feasibility study will help you identify the solutions required for your use cases and provide a clear understanding of the resources and changes needed to implement generative AI successfully. It will also help you identify potential roadblocks early on, allowing you to address them proactively.

After conducting a feasibility study and identifying the necessary solutions, the next step is to initiate a pilot project within a single function. A function that has strong leadership commitment, large benefits with generative ai, a culture that fosters innovation, has required technical skills and resources and has a diligent measurement & learning culture is preferred.

The pilot project aims to test the effectiveness of generative AI in a controlled environment before rolling it out across the organization. It allows you to identify any potential issues or challenges and address them before full-scale implementation.

Choose a set of use cases within the selected function that is highly feasible, has near-term potential, and can have a significant impact. Implement generative AI for these use cases and closely monitor the results over a quarter. Measure the impact using the key metrics identified in step 3, such as time saved, cost reduction, improved accuracy, or increased revenue.

Based on the pilot project results, create a revised plan for the next 1–2 years. This plan should include a roadmap for scaling the implementation of generative AI across multiple functions of your organization. The learnings from the pilot project will be invaluable in guiding this plan and ensuring the successful implementation of generative AI across your organization.

After the pilot project, the next step is to iterate on the plan based on the results and learnings from the pilot. Look for early wins in the target function and use these successes to build momentum for the wider implementation of generative AI.

Continuous monitoring and evaluating generative AI’s performance is important during this phase. Use the key metrics identified in Step 3 to measure the impact and adjust your strategy as needed. This iterative process allows you to continuously improve and optimize the use of generative AI in your organization.

The successful functions that realize maximum business benefits have leadership with tenacity, great change management, and communication flows, strong accountability, and a good support network.

Once you’ve achieved early success in the target function, you can expand the use of generative AI to other functions. Define clear goals for each function and integrate these into your overarching corporate AI strategy. This ensures that the implementation of generative AI is aligned with your overall business objectives and allows you to maximize the benefits of generative AI across your organization.

Remember, implementing generative AI is not a one-time project but an ongoing learning, iterating, and integrating process. By following this approach, you can ensure your organization continually evolves and stays at the forefront of AI innovation.

As we stand on the brink of a transformative era powered by generative AI, organizations must strategically incorporate this technology into their business strategies. Organizations can harness the immense potential of generative AI by identifying key beneficiary functions, mapping work to productivity zones, sizing opportunities, conducting feasibility studies, and initiating pilots. While challenges exist, they can be navigated with thoughtful planning, stakeholder engagement, and a commitment to continuous learning and adaptation.

The journey towards generative AI is not just about technological change but also a paradigm shift in how we work and deliver value. Embracing this change can unlock unprecedented levels of productivity and efficiency, driving significant economic value and redefining the future of work.