Artificial intelligence (AI) has been one of the most popular buzzwords in the business world for some time now. However, the recent emergence of generative AI and technologies like ChatGPT is creating new opportunities for businesses to automate complex tasks and reimagine value streams.
Generative AI is a type of AI designed to create something new, rather than just recognising patterns or making predictions based on existing data. It employs neural networks to generate outputs based on given inputs, aiming to produce novel and creative results.
In this article, we explore the applications of generative AI across different value streams (see Figure 1). We will examine specific use cases, affected process steps, changes brought about, and the impact on people and benefits.
Figure 1: Blueprint with core value streams
Use Cases for Each Value Stream
Value Stream: Strategy to Execution (Business Change and Transformation)
Use Case: Project Planning & Change Management
- The Process Step: Project Initiation & Change Management
- The Change: Using generative AI to generate project initiation documentation based on inputs from Project Managers and utilising the technology to draft communications.
- The Impact on People: Reduced time spent on the administrative side of managing projects and more time on stakeholder management.
- The Benefit: Reduced time to mobilise new initiatives
Value Stream: Insight-to-Awareness (Marketing)
Use Case: Content Creation
- The Process Step: Content Marketing
- The Change: Using generative AI to create new and innovative content ideas based on customer data inputs.
- The Impact on People: Increased engagement and conversion rates.
- The Benefit: More effective and targeted content marketing, resulting in increased engagement and higher conversion rates.
Value Stream: Concept-to-Retire (Product Development)
Use Case: Product Development
- The Process Step: Design and Development
- The Change: Using generative AI to generate new product design ideas based on customer preferences and market trends.
- The Impact on People: More innovative and customer-focused products.
- The Benefit: Increased customer satisfaction, leading to higher sales and revenue.
Value Stream: Order-to-Cash (Order Management)
Use Case: AI-Generated Personalised Promotions
- The Process Step: Order Processing
- The Change: Using generative AI to create personalised promotional offers and discounts for customers based on their purchase history, preferences, and seasonal trends. The AI system analyses customer data and generates unique promotional codes or special deals tailored to individual customers.
- The Impact on People: Order management and marketing professionals can offer more targeted and appealing promotions to customers, increasing the likelihood of conversions.
- The Benefit: Enhanced customer satisfaction and loyalty due to personalised attention, leading to increased sales and stronger customer relationships.
Value Stream: Issue-to-Loyalty (Customer Services)
Use Case: AI-Generated Personalised Complaint Handling
- The Process Step: Resolving Customer Complaints
- The Change: Using generative AI to create customised responses to customer complaints based on their unique situation and history with the company.
- The Impact on People: More tailored support experiences that address the specific needs of each customer.
- The Benefit: Enhanced customer satisfaction, leading to increased loyalty and long-term business relationships.
Value Stream: Source-to-Pay (Procurement & Accounts Payable)
Use Case: Selecting Suppliers During Sourcing
- The Process Step: Supplier Selection
- The Change: Incorporating generative AI into a Procurement ChatBot which buyers can quickly access to conduct supplier research and swiftly identify suppliers with specific capabilities
- The Impact on People: Reduced time to conduct research during sourcing
- The Benefit: Better supplier performance, faster delivery times, and more innovative sourcing decisions.
Value Stream: Technology to Decommission (Technology Delivery and Management)
Use Case: Generative AI for Code Maintenance
- The Process Step: Software Maintenance
- The Change: Implementing generative AI to automatically create patches and updates for legacy code based on current coding standards and best practices.
- The Impact on People: Developers can focus on new projects and innovations, rather than spending time on maintaining outdated code.
- The Benefit: Improved software quality, security, and compliance, leading to a more robust and reliable technology infrastructure.
Value Stream: Hire-to-Retire (HR & People)
Use Case: AI-Generated Personalised Career Development Plans
- The Process Step: Employee Career Development
- The Change: Utilising generative AI to create customised career development plans for employees based on their skills, interests, and goals, as well as organisational needs and opportunities.
- The Impact on People: Employees receive personalised guidance to help them grow professionally and achieve their career aspirations within the organisation.
- The Benefit: Increased employee engagement.
Value Stream: Record-to-Report (Accounting)
Use Case: AI-Generated Financial Narratives
- The Process Step: Financial Reporting
- The Change: Utilising generative AI to create comprehensive and easily understandable financial narratives based on complex financial data.
- The Impact on People: Finance teams can more effectively communicate financial results and insights to stakeholders.
- The Benefit: Improved decision-making and financial planning, leading to increased profitability and reduced risk.
The Limitations and Pitfalls of Generative AI
In addition to the potential benefits of generative AI, there are also important limitations and pitfalls that businesses need to be aware of. As a relatively new technology, there are still many unknowns and risks involved in using these models.
One of the primary limitations of generative AI models is their potential for biased or incorrect outputs. Because these models are trained on existing data sets, they may perpetuate the biases and prejudices that exist in the original data. This can lead to unintended consequences, such as offensive or harmful content being generated by the AI.
To mitigate these risks, it is important to carefully select the initial data used to train the models and to avoid including toxic or biased content. Organisations can also consider using smaller, specialised models or customising a general model based on their own data to fit their needs and minimise biases. It is also important to keep a human in the loop to check the output of the generative AI model before it is published or used, and to avoid using these models for critical decisions involving significant resources or human welfare.
As generative AI becomes increasingly incorporated into business, society, and personal lives, it is important to stay informed about regulation and risk. Organisations should reckon with reputational and legal risks involved in unintentionally publishing biased, offensive, or copyrighted content. As such, it is crucial for businesses to keep a human in the loop and to avoid using generative AI models for critical decisions.
In Summary
In conclusion, while generative AI offers numerous potential benefits for businesses across different value streams, there are also important limitations and pitfalls that need to be taken into consideration. By understanding and mitigating these risks, organisations can leverage generative AI to gain a competitive advantage and drive growth while avoiding potential negative consequences.
