Ali Shabdar, the MEA Regional Director at Zoho, says by analysing large amounts of data and providing insights, businesses can make more informed and data-driven decisions
Gen AI (Generative AI) is still in its infancy. However, various flavours of it, such as Chat-GPT, DALL-E, LLAMA, Claude, and Stable Difusion have been used to boost creativity, productivity, and efficiency in a myriad of scenarios. Some of the top applications so far are automation and personalization of customer service, creative content and media creation, content translation, code generation, as well as data analysis and visualization.
For example, a company could use Gen AI to generate personalised product recommendations for its customers or build chatbots, also known as conversational AI, that can answer customer queries in a natural and informative way. Gen AI can be used to accelerate new product development as well as improve operational efficiency in a number of ways. For example, a company could use the technology to optimise its production, or forecast demand.
Why according to you should companies leverage generative AI?
The main goal of any business is to maximize value and productivity while keeping efficiency up and costs down. Utilizing Generative AI helps with achieving these goals faster. By automating repetitive tasks and generating content, generative AI can free up employees’ time to focus on more strategic and creative initiatives. Secondly, generative AI can improve decision-making.
By analysing large amounts of data and providing insights, businesses can make more informed and data-driven decisions. Generative AI can enhance customer experiences. By leveraging AI-powered chatbots and virtual assistants, businesses can provide personalised and efficient customer support around the clock. This can lead to higher customer satisfaction and loyalty. Finally, generative AI can drive innovation and creativity. By generating new ideas and solutions, businesses can stay ahead of the competition and continuously improve their products and services.
What are the challenges companies face in terms of adopting and using Gen AI and how can they be overcome?
Companies are facing numerous challenges when it comes to adopting and using generative AI. The main, perhaps most common one, is data bias. The technology is still fairly new and under experiment, it cannot be relied upon to provide real-time outcomes that are neither discriminatory nor biased. On the other hand, businesses will incur costs of training and development of the technology, while there are still scarce skilled professionals in the market who can deploy and fully utilise its benefits.
Globally, companies also face ethical and legal restrictions limiting its usage due to privacy concerns and the absence of a gold-standard regulatory framework to govern its implementation and use. Learning the technology’s limitations and capabilities- which is still a work in progress- can help businesses make the most to maximise productivity and efficiency.
Are companies aware of regional and global policies surrounding the use of Gen AI?
Global and regional policies on the use of generative AI are still in the nascent stages of development. There is no comprehensive framework that individuals or organisations worldwide can adhere to when using or even setting their own implementation and usage policies.
However, it is crucial to adhere to existing regulations such as the protection of personal data within the context of AI. Sharing of company and customer data with a third-party AI might be in breach of existing regulations or policies. As we grasp the implications of this era, it is advisable to tread carefully and consult with providers and experts before letting the AI excitement potentially jeopardise businesses.
How can companies use their resources on using Gen AI to create a competitive advantage?
Companies now realise the technology’s potential usage and ability to enhance operations and the digital workflow, when properly utilised and implemented. As such, there needs to be investment in training and development of digital skills that will maximise the technology’s benefits. Gen AI can help companies across different sectors generate new product ideas, designs, and prototypes. In addition, companies can create personalised marketing campaigns, generate leads, and qualify sales prospects, optimise the supply chain, improve logistics and automate tasks. These benefits contribute to reducing operational costs and boosting efficiency.
What factors do companies need to consider before adopting Gen AI such as having a centralised data strategy?
Adopting General Artificial Intelligence (Gen AI) requires a robust centralized data strategy to ensure consistent data management, quality, and accessibility, which are crucial for the success of AI applications. Addressing data privacy and security is paramount, alongside ensuring compliance with regional and global data protection laws. The technology infrastructure should be scalable and capable of integrating AI technologies with existing systems.
It’s essential to have in-house expertise or reliable external vendors for developing and managing AI systems. Ethical considerations around bias, fairness, transparency, and explainability are critical to build trust with stakeholders. Cost analysis, including initial investments and ongoing maintenance, is necessary to understand the financial implications. Regulatory compliance with AI ethics guidelines and preparation for legal liabilities associated with AI applications are crucial.
Change management, encompassing organizational culture, user adoption, and training, plays a significant role in smoothly transitioning to a Gen AI-driven environment. Establishing clear ROI metrics, planning for long-term maintenance, and being cautious of vendor lock-in ensure a sustainable and flexible AI adoption strategy that aligns with the organization’s broader objectives.
How can companies experiment with Gen AI to predict the future of strategic workforce planning?
Companies looking to leverage General Artificial Intelligence (Gen AI) for strategic workforce planning should start by collecting comprehensive data on current workforce metrics, skills inventory, and recruitment trends. Utilizing Gen AI, they can develop predictive models to forecast workforce trends and create simulations for scenario planning, aiding in understanding different future workforce conditions.
It’s crucial to foster collaboration between AI experts, HR professionals, and business leaders to align AI-driven insights with organizational goals. Additionally, implementing machine learning algorithms will facilitate continuous learning from new data, refining predictions over time. Investing in a robust technology infrastructure is essential to support AI experimentation, ensuring data privacy and security.
Ethical considerations should be addressed to minimize bias and adhere to ethical guidelines for fair AI-driven workforce planning. Establishing clear performance metrics will help in evaluating the effectiveness of AI strategies, and educating stakeholders about the potential and limitations of AI is crucial for smooth adoption. External partnerships with AI and workforce planning experts can provide fresh perspectives and additional expertise. Through a structured and collaborative approach, companies can experiment with Gen AI to enhance strategic workforce planning, making it more data-driven and predictive.