GenAI Maturity: From Productivity To Effectiveness

As I work with more organisations using Generative AI (GenAI) tools like OpenAI’s ChatGPT and Microsoft’s Copilot, I see an interesting dilemma – many organisations seem satisfied with settling for productivity improvements and “taking costs out of the operations” (reminds me as to why companies went to the cloud). And while GenAI tools can indeed “take costs out of the operations,” these organisations are missing the bigger prize – to drive the creativity and innovation that can uncover and re-engineer new sources of customer, product, service, and operational value.

There is an inflexion point between using GenAI to reduce costs (Productivity phase) versus using GenAI to power creativity (Innovation phase).

• The “Productivity” phase seeks to reduce costs by elevating the average worker’s productivity to the level of the best practitioners. For instance, GenAI can enhance the productivity of the average nurse to that of the most experienced nurse by streamlining documentation processes, providing easy access to up-to-date medical information, and quickly analysing patient data. Similarly, in education, GenAI can elevate the productivity of the average teacher to that of the most experienced educators by alerting teachers to student performance issues, tailoring lesson plans to individual student needs, quickly evaluating assignments, and providing personalised feedback.

• However, the “Innovation” phase provides an even more significant GenAI opportunity. During this phase, the focus shifts to empowering significant, innovative advancements and re-engineering business processes and operational models. For instance, accomplished nurses can utilise AI to analyse complex patient data, identify new treatment strategies, evaluate their effectiveness, and continuously learn and adapt to drive more effective treatment outcomes. Similarly, AI can empower exceptional teachers to explore innovative teaching methods, develop individualised student lesson plans, create advanced learning materials, evaluate their effectiveness, and adapt the teaching methods and training materials accordingly.

The “Innovation” phase goes beyond simply improving productivity. It enables the most skilled workers to explore new innovative approaches, develop more effective techniques, and implement more relevant actions and treatments that lead to better, more accurate, and less risky outcomes (See Figure 1).

Figure 1: Phases of Generative AI Business Model Maturity

Levels of GenAI-enabled Innovation Maturity

How effectively is your organisation at leveraging Generative AI to drive innovation into your business and operational models?

The different levels of GenAI-enabled Innovation Maturity are:

1. Business Optimisation involves improving existing processes and operations to boost efficiency, cut waste, and add value. It leverages AI to streamline workflows, allocate resources, and enhance decision-making processes. For instance, in supply chain optimisation, AI can predict demand and adjust inventory levels accordingly, minimising stockouts and excess inventory based on real-world needs. In energy management, AI can monitor energy consumption in real-time, pinpointing inefficiencies and optimising usage to reduce costs and environmental impact. Moreover, AI can streamline the sales process by identifying the most effective tactics and channels, leading to increased conversion rates with fewer resources.

2. Process Re-engineering involves fundamentally redesigning business processes by leveraging AI to transform processes, improve efficiency, and scale effectiveness. For example, manufacturing process re-engineering can be achieved by implementing AI-driven robotics and automation, resulting in higher production rates, improved quality, and reduced labour costs. In healthcare, process redesign can utilise telemedicine and AI-driven diagnostics to streamline patient intake and diagnosis processes, improving patient outcomes and reducing wait times.

3. Market Domination involves identifying, entering, and dominating new markets to expand the Total Addressable Market (TAM) while increasing market share in existing markets. It includes using AI to analyse market trends, customer needs, and competitive landscapes in near real-time to discover and capitalise on new business and operational opportunities. For example, expanding into new geographic markets involves using AI to identify regions with high potential for product demand and developing a strategy to enter those markets. Product diversification can be achieved by using AI to uncover customer, product, and service insights to create new product lines that appeal to previously untapped customer segments.

4. Cultural Empowerment aims to foster a culture of innovation, collaboration, and continuous improvement within the organisation. It involves empowering employees with the necessary AI tools, analytical skills, and growth mindset needed to drive innovation and embrace change. Establishing innovation labs and collaborative workspaces allows employees to experiment with new ideas and technologies. Employee empowerment initiatives encourage participation in decision-making processes, unleashing the untapped organisational tribal knowledge and contributing innovative ideas through structured feedback systems and idea competitions.

With the aid of this maturity model as a guide, organisations now develop a systematic prompt engineering strategy to accelerate our advancements in the GenAI Innovation phase. This strategy will enable us to harness the power of GenAI for greater levels of innovation and creative exploration.

Prompt Engineering to Drive Innovation

The prompts created in the Productivity stage primarily focus on automating routine tasks, improving productivity, and reducing operational costs. These prompts aim to streamline daily activities, ensuring that basic functions are carried out quickly and accurately without requiring extensive human intervention. For example, prompts in this stage often include tasks like:

• “Write me a hiring letter based on the following criteria…”.

• “Find me the legal cases matching the following criteria…”.

• “Generate a weekly report summarising sales data…”.

• “Schedule my meetings and set reminders for the following tasks…”.

However, the Innovation phase allows employees to focus on more complex and creative aspects of their roles, paving the way for significant efficiency improvements. Employees can spend more time on areas that drive greater strategic value and innovation within the organisation, which impacts the types of prompt engineering we want to teach all of your employees.

Table 1 shows an example of a narrative conversation with ChatGPT across the different levels of GenAI Innovation maturity, focused on the healthcare operational initiative of improving patient treatment outcomes effectiveness. The conversation is structured to build upon each level, creating a cohesive narrative.

And this is only the beginning.  I hope you can appreciate that the depth and open-ended nature of the discussion in Table 1 is only limited by your preconceptions and imagination. In other words, you are in control of your own destiny.

Summary: Economic Ramifications of the GenAI Phases

The economic impact of the GenAI “Productivity” phase is impressive as it enhances output and consistency across the board. Organisations can achieve significant cost savings, improve service quality, and reduce outcome variability by bringing everyone up to a higher standard. However, the true economic potential is unlocked in the GenAI ” Innovation” phase, which focuses on leveraging GenAI to drive transformative changes. This phase has the potential to re-engineer existing operational processes, disrupt existing industries, reach new customers, create new markets, and generate unprecedented value through innovation.

The differences in prompt engineering between these two phases are substantial. In the “Productivity ” phase, prompts provide standard, reliable, and accurate information that aligns with best practices. These prompts aim to ensure consistency and adherence to established protocols. In contrast, the “Innovation” phase requires prompts that encourage creative thinking, exploration of novel ideas, and the synthesis of complex information. Prompts in this phase are designed to push the boundaries of what is possible, fostering an environment where innovation can thrive.

Originally published on Data Science Central on June 29, 2024. Republished here with permission of the author Bill Schmarzo.

Bill Schmarzo is a pragmatic leader with extensive experience in building and empowering Data Science and Value Engineering teams to unlock and monetise business value from data.

He created the Value Engineering methodology, which drives collaboration in identifying and prioritising key business use cases for data and analytics. Known as the “Dean of Big Data,” Bill specialises in data monetisation, integrating Design Thinking with Data Science to rapidly test and monetise insights.

An industry leader, Bill drives innovation, teaches the “Big Data MBA” course, and has authored four books and over 350 articles on Big Data, Data Science, and digital transformation.