The potential of Generative AI to drive transformative growth and revolutionize innovation across people, operations, data, and technology is undeniable. However, adopting any transformative technology comes with inherent risks, and Generative AI may present even more. Proper oversight is essential to accurately identify, assess, and manage these risks.
Many leaders, including CEOs, CTOs, CDOs, CFOs, and COOs, have the same question. So where do you start?
This article looks at multiple use cases and provides a simple framework to help leaders decide the starting point.
Automotive: Driving Efficiency and Innovation
To effectively harness generative AI, leaders must identify use cases that have the potential to make the most business impact. In my experience, there are three critical factors to consider when evaluating the potential of a use case for AI implementation:
Business Value: Assess the business value of the proposed use case. Can the benefits be quantified in terms of increased revenue, cost savings, risk reduction, or improved decision-making?
Data Viability (Barrier): AI models heavily rely on the quality and quantity of the data they are trained on. Evaluate the availability of relevant data for the specific use case. Insufficient or irrelevant data can lead to unreliable model performance. Essentially it is garbage in — garbage out.
Implementation Effort: Consider the technical feasibility and effort required to implement the AI solution. Factors such as the complexity of the model, integration with existing systems, and resource availability influence the overall effort involved.
AI Enabled People
Whether it’s customers seeking updates on their orders, employees inquiring about their vacation balances, or CEOs seeking consolidated sales figures, people require information. This flow of information plays a critical role in determining the efficiency and effectiveness of the work performed. Focusing on how people accomplish their work effectively would uncover Generative AI projects that could serve as a starting point.
Customer service (external & internal) to query information has become the top Generative AI priority for Organizations. Coupled with Conversation AI, many organizations use this as a strategic stepping stone to ensure a successful enterprise-wide implementation of Generative AI.
Content generation can be an overwhelming and burdensome task in many departments (marketing, sales, HR, finance) often leading to high-value strategic work being neglected as teams struggle to meet constant deadlines. With Generative AI, content creators are freed from the relentless pressure of churning out content.
AI Enabled Technology
In various sectors, including Banking, Financial Services, Insurance, Healthcare, Life Sciences, Automotive, and Retail, companies of all sizes, from startups to large enterprises, have come to rely on technology as the backbone of their operations. Generative AI can be a shot in the arm for this backbone.
Democratization of Software Development — In the past, coding was primarily confined to software engineers, but Generative AI is changing that. It empowers domain experts to contribute to software development by assisting in writing new code, testing and debugging existing code, and identifying security risks. This adoption by domain experts will spark innovation across various fields.
For software engineers, Generative AI enhances productivity by optimizing software delivery time. By leveraging DORA metrics, the impact of implementing Generative AI on the software delivery process can be precisely measured, allowing for continuous improvement.
Make trusted data the backbone of your organization — Generative AI has increased the value of data, leading to competition among companies to exploit its potential. Companies with abundant and trustworthy data tend to achieve higher ROI from their AI investments. Identify required data sets by working backward from the customer experience to determine what a Generative AI platform should offer.
Data mining unstructured data is crucial for developing a competitive advantage and differentiating platform value propositions. By extracting insights from unstructured data, businesses can gain a deeper understanding of customer needs, market trends, and operational patterns. This data can provide valuable insights that help in developing innovative products and services, improving customer experiences, optimizing operations, and making data-driven decisions.
Developing a superior customer experience is what sets many businesses apart. Generative AI unlocks the potential for hyper-personalization, transforming it from a distant aspiration into a tangible reality. By leveraging integrated data, businesses can construct comprehensive 360-degree customer profiles. These profiles enable tailored and highly relevant marketing strategies.
AI Enabled Processes
In various domains such as Network Operations, Security Operations, Data Operations, Application Maintenance, and Supply Chains, process automation has significantly enhanced business efficiency over the past few decades. Generative AI can further amplify these advancements by enabling quicker access to data that complies with standards and helps optimize decision-making.
Throughout the operations lifecycle, handling various documents such as KYC documents, contracts, purchase orders, and invoices is an integral part of operations. A considerable amount of time is spent extracting information from these documents. Generative AI can be leveraged to accurately extract information, thereby enhancing the speed and efficiency of the process.
Transform traditional dashboards into real-time queries powered by Large Language Models (LLMs). Integrate comprehensive operations metrics and transactional data into Generative AI models. Evaluate demand forecasting accuracy, assess potential improvements in cycle times, and determine the optimal working capital requirements.
In the operations lifecycle, forecasting is significant. It encompasses various facets such as sales forecasting, budget forecasting, and demand forecasting, all of which require substantial resources and effort. Generative AI enhances these forecasting processes by enabling faster, more accurate predictions, reducing human error, and allowing businesses to incorporate vast amounts of historical and real-time data. This can significantly reduce the effort and resources required for traditional forecasting methods, while improving overall accuracy.
By analyzing network traffic patterns, Generative AI can identify unusual activities that could indicate security breaches or infrastructure issues. For example, it can detect abnormal spikes in traffic, unauthorized access attempts, or unusual data flows, triggering alerts for further investigation.
Operations often require regular compliance checks and reporting (e.g., GDPR, HIPAA). Generative AI can automate the generation of compliance reports and ensure adherence to regulatory requirements, reducing the manual workload.
Strategy
In the last two years, Generative AI has revolutionized the workplace, surpassing initial expectations in terms of pace and scale of change. This transformation demands a rapid adaptation of essential skills, emphasizing creativity as a fundamental Generative AI skill.
People should be at the core of any Generative AI strategy and they should be empowered to experiment within a safe and secure environment.
To facilitate the establishment of Generative AI Transformation, I have devised a comprehensive strategy known as G-FORCE.
- Governance
- Financial Controls
- Objectives
- Robust Technology
- Culture
- Education
Define Objectives & Prioritize
- Define objectives of Generative AI transformation. What is the desired outcome of this transformation — say, 3 months, 6 months, 1 year, 2 years
- An Organization’s objective need not necessarily be to reduce the number of employees. Instead, it could be to empower individuals to use their time more efficiently and creatively, thereby fostering a more fulfilling and satisfying work environment.
- Prioritize your Generative AI investments, rather than spreading them thinly across your IT portfolio. This will largely be based on the objectives. For example, instead of spreading resources thinly across numerous applications, leaders should prioritize Generative AI solutions to specific projects selected intentionally.
Establish Core & Robust Generative AI Technology Ecosystem
- Assemble a cross-functional core technology team. Minimally this should include Architecture, Infrastructure, Security, Controls, Data, and Engineering disciplines.
- Task this team with defining the technology stack, including the selection of Large Language Models, Generative AI frameworks, databases, APIs, integration points, hosting/deployment strategy etc.
- Given the wide range of options available, ranging from commercial to open source, packaged tools to frameworks, SaaS, PaaS, Cloud, On Prem deployments, adopt an incremental design approach. Start small, gain knowledge and understanding, and scale over time.
- Understand the implications of using Large Language Models. What data is being used and how? Where will data be stored? This will impact the architecture of your solution.
- Consider the “Build vs. Buy” decision carefully. Evaluate available tools, frameworks, and platforms in the Generative AI space. Assess whether building a solution from scratch is necessary or if existing options can be leveraged and integrated. For example, there are tools available to write code that can integrate with an IDE.
- Prioritize security and compliance measures to ensure the protection of sensitive data and adherence to regulatory requirements. Implement robust access controls, encryption mechanisms, and audit trails.
Establish Generative AI Data Governance
- Generative AI, fueled by massive amounts of data from various sources — internal and external, raises concerns about trust.
- Implementing a comprehensive cross-domain end-to-end governance system throughout the AI lifecycle is crucial to avoid siloed governance.
- Create a team with Generative AI Data Governance as a mandate. This team should establish guardrails for the usage of data (both internal and external) and the responses generated by AI models.
- To establish trust, organizations should transparently communicate their processes for data selection, governance, analysis, and application.
Establish Financial Controls
- Define the financial controls around the Generative AI Transformation Program
- Consider creating a budget that includes costs around, data access & storage, LLM access, API access, infrastructure, hosting, 3rd party vendors etc.
- Implement alerts for cost thresholds to keep spending within approved limits.
- Measure, measure, measure!
- This is particularly important if multiple teams are eager to experiment with or adopt new technologies. Leaders should know at any given point, what’s their Generative AI spend?
- Compare the results of generative AI innovation with those of manual processes to demonstrate the value of generative AI.
Foster a Culture of Experimentation
- Every day, people are discovering new ways to use generative AI to automate tasks, make business decisions, or plan for future disruption.
- Organizations need to change their innovation plans to make the most of this AI by making innovation processes faster, bigger, and better.
- Foster a culture of experimentation among employees while simultaneously implementing robust safeguards to guarantee the protection of sensitive data and adherence to ethical principles
- Adapt incentive mechanisms and KPIs to encourage collaboration, innovation, and decision-making with generative AI across the enterprise.
- Reinvent employee experience with Generative AI by providing them with the requisite information and tools.
- Make Customer Trust central to every customer experience touch point. Create ethical journeys that build customer confidence.
- Generative AI will be a transformational shock for many. Find the friction and eliminate it.
Educate Everyone
- Educate the executive team and board, making Generative AI and Data Governance as regular agenda items.
- Educate employees on the usage of Generative AI tools. For example, the art and science of prompt engineering as most end users will be consumers and they should know how to leverage LLMs to get the right responses.
Summary
In my experience, transformation requires deliberate human intervention. Leaders must have a clear vision and plan when utilizing generative AI to maximize its potential. They should be specific and intentional about how they will integrate this technology and what outcomes they aim to achieve.
This clarity will help them harness Generative AI to enhance productivity, creativity, and overall effectiveness in the workplace. Leaders who use generative AI can improve decision-making, customer experience, and revenue growth.
By adopting this strategic approach, leaders can harness the power of generative AI to drive meaningful change and position their organizations for long-term success.
