With the public unveiling of OpenAI’s ChatGPT last year, the realm of generative AI has undergone a remarkable transformation, shifting from obscurity to prominence. The capabilities of generative AI have surged to an astonishing level. It can now craft text responses resembling human composition, conjure lifelike images of events that never transpired, offer insights into software code, and even construct entire websites.
This evolution has triggered a heightened focus from political and regulatory quarters on the responsible utilization and advancement of AI technology. The tightrope walk between risk and reward in AI deployment has ventured into uncharted territory. As the legal landscape adapts to the ever-changing AI terrain, industries are experiencing a profound upheaval in their operational and creative paradigms.
Here our experts examine some of the big questions to address when exploring generative AI opportunities, especially its role in Global Business Services.
Generative AI, or “Generative Artificial intelligence” to give its full name, is a type of artificial intelligence technology that can produce various types of original content, including text, imagery, video, audio, and synthetic data.
Generative AI models use neural networks to identify patterns and structures within existing data to generate content that is new and entirely unique. It can do this without explicit instructions, unlike traditional AI which requires specific instructions to perform a task.
A neural network is a method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain. It is a type of machine learning process, called deep learning, that uses interconnected nodes or neurons in a layered structure that resembles the way the human brain works.
“That is the big question for GBS! Let’s remember that AI is “just” one of the latest tools in the end-to-end Intelligent Automation space. It does not replace the others, but adds additional, exciting capability for GBS practitioners. The question is what and how. Honestly, the potential opportunities and use cases are endless. But we also need to be careful how we approach this new technology”
This one I call the “old chestnut”. Generative AI enabled IDP leverages Optical Character Recognition (OCR), Natural Language Processing (NLP), and Computer Vision technologies to significantly improve the success from IDP plus also the opportunity to further leverage the data captured through IDP. It can learn from experience and from mistakes and is easily configurable and extendable for multiple uses. Indeed, many of the old broader “document processing tasks” like data entry, order booking, invoice processing, mailroom processing can benefit from Generative AI and Large Language Models (LLMs).
Intelligent Chatbots can enhance customer service in shared service centers by providing automated responses to frequently asked questions or common issues. These chatbots go beyond the limitations of traditional chatbots by leveraging AI, NLP, and machine learning technologies. They offer more sophisticated and human-like interactions, context awareness, personalization, and the ability to handle complex tasks, making them more effective in delivering seamless and engaging conversational experiences.
Generative AI can optimize supply chain and logistics operations in GBS by improving demand forecasting, inventory management, and route optimization. It can also contribute to more efficient warehousing and distribution processes. As an example, Walmart is harnessing Generative AI to develop efficient and sustainable supply chains. The retail giant employs algorithms for accurate demand forecasting, waste reduction, improved inventory management, and optimized transportation networks, resulting in lower costs and emissions.
In shared services/GBS that deal with customer-facing activities, generative AI can be used to personalize interactions and services based on customer preferences and historical data. For example, Amazon began testing a feature in its shopping app that uses AI to summarize reviews left by customers on some products. It provides a brief overview of what shoppers liked and disliked. The technology has helped Amazon to weed out bogus reviews and provide personalized recommendations to its customers.
In shared services that involve content creation, such as marketing or documentation, generative AI can assist in generating text, images, or even videos, thereby reducing the time and effort required by human content creators. Jasper, for example, a marketing-focused version of GPT-3, can produce blogs, social media posts, web copy, sales emails, ads, and other types of customer-facing content. At the cloud computing company VMWare, for example, writers use Jasper as they generate original content for marketing, from email to product campaigns to social media copy.
Generative AI can help in identifying anomalies and patterns that may indicate fraudulent activities. Shared services, especially those related to finance and accounting, can benefit from such capabilities to protect the organization from potential risks. For example, FIS Global uses Generative AI to improve its anti-money laundering capabilities. The provider now uses AI to vet risk when onboarding new vendors.
Generative AI powered language translation tools can assist businesses in GBS operations that require communication across different languages and cultures. This capability can enable seamless collaboration and expansion across global markets.
“Generative AI has a wide range of potential applications in shared services and GBS. But honestly so does RPA. P2P has been the “starting place” for many steps forward with shared services and automation, but this is not so much the case anymore – for RPA or AI, although can still sometimes be used as a “starting point.”
-In addition to HR, generative AI can be used in
Automated Responses: Generative AI-powered chatbots can handle customer inquiries and support requests, providing quick and accurate responses, even in complex scenarios.
Complaint Resolution: Generative AI can analyze customer complaints and feedback to generate appropriate resolutions, enabling better customer satisfaction.
Expense Reporting: Generative AI can automate the processing and categorization of expense reports, reducing manual effort and ensuring compliance with expense policies.
Invoice Management: Generative AI can help match invoices with purchase orders and receipts, streamlining the accounts payable process.
Demand Forecasting: Generative AI can analyze historical sales data and market trends to forecast demand accurately, helping optimize inventory levels and reduce stockouts.
Supplier Selection: Generative AI can assist in selecting the most suitable suppliers based on criteria like quality, price, and delivery time.
Data Visualization: Generative AI can automatically create visually appealing and informative data visualizations, simplifying the presentation of complex data.
Predictive Modeling: Generative AI can build predictive models to identify patterns and trends, aiding in forecasting sales, customer behavior, and market trends.
Contract Review: Generative AI can analyze contracts and legal documents, highlighting critical clauses and potential risks.
Regulatory Compliance: Generative AI can assist in monitoring regulatory changes and identifying areas of non-compliance within the organization.
Generative AI can help create marketing content, social media posts, and personalized email campaigns, saving time and resources.
A/B Testing: Generative AI can assist in conducting A/B tests to optimize marketing campaigns and improve conversion rates.
“In the realm of shared services, Generative AI emerges as a transformative ally. It has the capacity to revolutionize processes, augment decision-making, and enhance customer interactions. From streamlining operational workflows to personalizing user experiences, Generative AI holds the key to unlocking unprecedented efficiency and innovation within shared services, propelling organizations into a new era of excellence.”
Consider the current business objectives and challenges of the organization. Determine which department (Finance or HR) is facing more immediate pain points that could be addressed by generative AI. For example, if the finance department deals with complex data analysis and reporting requirements, applying generative AI in finance processes might be more beneficial.
Generative AI models require large amounts of high-quality data to learn patterns and generate accurate outputs. Assess whether the required data for finance or HR processes is readily available, trusted and suitable for training generative AI models.
Implementing generative AI requires technical expertise and resources. Evaluate whether your organization has the necessary talent and infrastructure to develop and maintain generative AI solutions for either finance or HR processes.
Depending on the industry and region, there may be specific regulatory and compliance requirements that impact the use of AI in finance or HR. Ensure that the chosen application aligns with the necessary regulations.
Consider the potential impact on customers or employees for both finance and HR processes. Choose the department where the application of generative AI can lead to a significant improvement in customer experience or employee satisfaction.
Evaluate the potential ROI for applying generative AI to finance or HR processes. Assess the expected cost savings, efficiency gains, and other benefits associated with each application. According to Mckinsey, 75% of the value could arise from just four areas: customer operations, marketing and sales, software engineering, and R&D.
Analyze the risks associated with applying generative AI to either finance or HR processes. Consider data privacy, security concerns, and the sensitivity of the data involved in each case.
Consider your organization’s long-term strategy and vision. Determine whether applying generative AI to finance or HR processes aligns with the broader strategic goals of the company.
“It is important to understand that Gen AI does not necessarily replace other AI tools but rather serves as a valuable addition to the AI toolkit within a GBS organization”.
Generative AI (Gen AI) can play a valuable role in a typical GBS (Global Business Services) solutions catalogue by offering a unique set of capabilities that complement and enhance other types of IA and AI tools.
Generative AI’s opportunities in GBS are manifold:
Gen AI provides capabilities that are not typically found in other AI tools, such as natural language generation, content creation, and simulation of scenarios. It can complement other AI tools like machine learning algorithms, chatbots, and predictive analytics by generating content, data, or simulations that can be used as inputs for other AI processes.
Gen AI’s ability to create personalized content and recommendations can significantly enhance customer experiences in GBS operations. It can be used in conjunction with other AI tools to provide more tailored solutions to customers, resulting in increased satisfaction and engagement.
Gen AI can automate content creation for marketing materials, reports, and other documents. By using Gen AI alongside other AI tools that handle data analysis or predictive modeling, businesses can streamline the content generation process and make data-driven decisions.
Gen AI’s capacity to simulate scenarios and generate alternative outcomes can be valuable in strategic decision-making and risk assessment. It can be combined with other AI tools like predictive modeling and data analytics to explore different possibilities and optimize business strategies.
Gen AI can enhance collaboration between humans and AI systems by generating ideas, suggestions, or draft content that human users can then refine and finalize. This collaboration between Gen AI and human expertise can lead to more innovative and effective solutions.
While Gen AI brings unique capabilities, it may not be suitable for all AI use cases. For specific tasks requiring complex data analysis, pattern recognition, or statistical modeling, other AI tools like machine learning algorithms may be more appropriate.
“Generative AI holds immense potential, but we must not overlook the critical risk factors associated with its unprecedented capabilities. As we harness its power to create, innovate, and transform, we must be vigilant in addressing ethical concerns, data biases, and the potential for misuse. Responsible development and deployment are paramount to ensuring a positive and sustainable future for generative AI.”
Research shows 67% of senior IT leaders are prioritizing generative AI for their business within the next 18 months, with one-third (33%) naming it as a top priority.
However potential risks of generative AI have been raised, especially copyright issues. One company has banned AI-generated content over liability concerns over copyright infringement. Several stock libraries have banned AI images at the requests of artists and photographers. Educators have raised risks of plagiarism from ChatGPT, with some cities banning chatbots from public schools.
In my opinion, there are some key risk factors of generative AI.
When answering a question, humans will often qualify with “I’m not sure, but…” or “This is just a guess…” depending on the level of certainty they have about their answer. By contrast, ChatGPT tends to provide an answer without equivocation.
A hallucination is when the model makes stuff up that either doesn’t make sense or doesn’t match the information it was given. In such cases, the model answers sound plausible but are incorrect.
The key challenge of identifying a “truth” for ChatGPT is that it does not have a clear information source. Unlike other AI assistants like Siri or Alexa that locates an answer on the Internet search engine, ChatGPT is trained to construct sentences by making a series of guesses on the statistically likely “token” that comes next. For this reason, Large Language Models (LLMs) are sometimes called “stochastic parrots.”
Where results are questionable, explainability enables the receiver of information to assess context and gain insight into the assumptions or logic applied.
When training on a large corpus of text data or image data, the model naturally replicates any representative biases in its source. While ChatGPT has content moderation guardrails in place to prevent sexual, hateful, violent, or harmful content, these filters have been found to be easy to bypass by rephrasing the prompts.
Much work still needs to be done to attempt to identify biases in training data sets and mitigate them – not only in generative AI, but for AI overall.
Generative AI can have significant environmental costs. Strubell et al. found that the training process for its large transformer model emitted 284 tonnes of CO2. For context, an average human is responsible for close to 5 tonnes of CO2 per year. The model training was found to emit as much carbon as five cars in their lifetimes.
While there are ideas proposed to limit the carbon footprint of AI systems, the potential environmental impact of generative AI should be considered as a part of the risk-benefit assessment in any use case.
Overall, the potential risks and ethical considerations should be fully considered with the rising hype of generative AI. There are exciting potential applications of these technologies as researchers make massive strides in launching new models. Balancing these strides with proportionate consideration of risk management alongside accountability and potential misuse will ease concerns and limit unforeseen negative impact.