In a 2023 report conducted by Deloitte, 32% of Australian employees surveyed admitted to already using generative AI in some form at work. However, only 1.4% of Australian businesses have officially adopted it. While this number looks relatively small right now, generative AI has become a key discussion among business leaders, given its potential to transform operations. While anyone can use these tools, your organisation should be cautious about adopting them without a defined strategy. The C-suite should know these key points before promoting adoption.
Defining generative AI
Generative AI is a branch of AI trained to generate text, images, music, and code. Text-based generative AI uses a Large Language Model (LLM) to understand human-like text and produce creative outputs based on the datasets used to train it. Do not confuse generative AI with traditional AI, which is typically limited to specific use cases, such as object detection in videos or sentiment analysis on text. Both types of AI have their place in business, so it is important to understand the distinction.
Business use cases for generative AI
When ChatGPT rose to prominence in early 2023, hitting 100 million users within two months of release, the question arose of whether it would replace some professional roles entirely. It is pertinent to note that generative AI is unlikely to fully replace peoples’ roles in the next two to three years. However, in most cases, it can be used to augment roles to some degree. In these instances, it should materially augment and improve your team’s output.
According to Gartner, the primary focus of Generative AI initiatives falls into four main categories: Customer Experience/Retention, Revenue Growth, Cost Optimisation and Business Continuity.
Here are a few use cases that are delivering measurable business value for our customers:
Document search and updating
Users can search for information in documents using natural language, and generative AI will retrieve the relevant documents or data. The AI will provide a summary of information spread across multiple documents. Users can also update documents using generative AI; others accessing the document can request a summary of these changes. These tools cut down time manually spent searching for information and checking document updates.
Secure chatbots
AI-powered chatbots can do more than respond to simple queries. Generative AI allows them to provide accurate and personalised responses using customer information. Chatbots can generate human-like responses based on context to improve customer service.
Complex pattern detection
Generative AI can identify complex patterns within data that are not immediately apparent to human analysts. For example, complex pattern detection identifies potential fraud by spotting anomalies in transactions, claims, or purchase patterns. Generative AI improves decision-making by providing deep insights and accurate predictions based on data trends to support informed strategies and risk management.
Student evaluation
Educators can use generative AI for student assessments and evaluations. Generative AI can create unique scenarios to test students’ problem-solving and critical-thinking skills. Educators can also use generative AI to tailor assessments for those requiring specific considerations, such as neurodivergent students. Using generative AI for student evaluations streamlines evaluation processes and helps educators remain adaptive and responsive to individual learning needs.
Real-time context analysis for voice
Generative AI takes voice input and interprets the intent and nuances. For example, it can analyse someone’s voice to identify customer sentiment and provide this information to a contact centre agent. Such advancements improve experiences for agents and customers alike by enabling more intuitive and personalised interactions.
Strategies for implementing generative AI
Your organisation cannot simply ‘switch on’ generative AI; adopting it requires careful planning to see a return on investment.
Understanding generative AI’s role
Before adopting generative AI, the C-suite must decide on its potential impact and applications. Leadership must go beyond merely providing access to AI tools; they should identify the departments where AI could add the most value and establish specific metrics for success. For example, which departments and job functions will leverage generative AI? How will it contribute to meeting the company’s goals?
By answering questions like these, the C-suite ensures that generative AI aligns with the business’s desired objectives and outcomes. This first step lays the groundwork for a successful AI strategy by focusing efforts where they can produce the most meaningful outcomes.
Leadership involvement
Successful generative AI adoption should start from the top down. It is a technology that has already changed the business landscape, and we will continue to see its impact in the future. The C-suite should increase their knowledge of generative AI and keep up with the latest developments to help their business stay cutting-edge. The C-suite can also gain a lot from using generative AI in their processes and should plan to use it at all levels of the business, not just the front line.
Training and upskilling
Your teams need the right training and skills for the entire organisation to experience the benefits of generative AI. As such, the C-suite should ensure that generative AI initiatives include programs to train and upskill relevant people.
Investing in education and skill development equips employees with the tools to innovate and improve processes, ensuring the organisation fully capitalises on AI’s potential. Moreover, fostering a culture that values continuous learning and proficiency in generative AI prepares the company to navigate challenges and opportunities.
Cautions for using generative AI
C-suite executives should be cautious about the following three areas when adopting generative AI within an organisation:
Data security and privacy
Generative AI accesses large amounts of data, including intellectual property or sensitive customer information. The AI platform could retain this information for future training, which gives way to privacy issues. Executives should ensure the AI adoption strategy includes data protection measures to prevent breaches or leaks.
Data quality
The success of generative AI heavily relies on the quality of data used. Executives should establish data management practices to enhance accuracy, completeness, and consistency. Regular audits and updates should be part of the data management strategy to ensure generative AI uses reliable, up-to-date information.
Skill gaps and workforce impact
Introducing generative AI may reveal skill gaps in the current workforce. The C-suite should clearly communicate how AI will augment rather than replace human teams and provide opportunities for upskilling and reskilling employees. Executives should consider the impact of AI on employees and manage the transition to avoid change resistance. They should also instil the importance of using generative AI responsibly and ethically.
Conclusion
Generative AI provides opportunities to augment workforces, improve processes, and drive a competitive edge. The C-suite should understand generative AI and stay abreast of developments as they change and transform the business landscape.
Before adopting any form of generative AI within an organisation, the C-suite must understand its relevance, drive company-wide adoption, and commit to training and upskilling the workforce. While generative AI offers numerous advantages, it also requires careful consideration of workforce skills, data security, and quality. Carefully planning adoption and addressing issues enables businesses to harness generative AI to enhance human efforts and open new pathways for innovation and growth.
addaxis.ai can help your organisation deploy generative AI correctly
We unlock the potential of advanced machine learning to address real-world business challenges. Our specialists work with a broad suite of AI tools and solutions to deliver business value backed by security and engineering. We integrate the advanced capabilities of proprietary models (like Open AI’s GPT-4 and Google’s Gemini) and open-source models (like LlaMA 2, Vicuna and Gemma) in your applications.
Our team have successfully developed applications to fine-tune LLMs for document search and updates, secure chatbots, complex pattern detection, student evaluation and real-time voice context analysis – to name a few. Visit our Services page to learn more about what we do and how we can support your business.
