Ramprakash Ramamoorthy, the Director of AI Research at ManageEngine, says as generative AI continues to evolve, it is poised to align consumer demands with enterprise objectives strategically, potentially reshaping the business landscape
In the past year, the emergence of large language models (LLMs) has significantly enriched the landscape of generative AI. These expansive models exhibit emergent capabilities, showcasing a more human-like interaction that sets them apart from their smaller counterparts. When considering their applications, we can broadly categorise their utility into three main areas: summarisation, paraphrasing, and the ability to have deep, nested conversations.
Despite the widespread impact that generative AI is predicted to have across various domains, it’s worth noting that its capabilities, at present, lean more towards consumer-oriented tasks rather than business-focused applications. Nonetheless, as generative AI continues to evolve, it is poised to align consumer demands with enterprise objectives strategically, potentially reshaping the business landscape.
Why, according to you, should companies leverage generative AI?
Companies today are increasingly recognising that AI is not merely a strategic differentiator, but an essential component for day-to-day operations. Generative AI offers distinct advantages in this context, such as the ability to maintain a consistent tone across various customer touchpoints—from marketing messaging to customer support. This consistency in communication can significantly enhance the customer experience and build brand loyalty.
Moreover, generative AI can bolster staff productivity by automating tasks and generating content, thereby allowing employees to focus on more complex tasks that require human intervention and expertise. Additionally, for businesses looking to expand internationally, generative AI can reduce the time and effort associated with content translation, enabling easier market penetration across borders. In essence, leveraging generative AI can enhance operational efficiency, improve customer engagement, and facilitate global expansion, making it a compelling addition to any company’s technology stack.
What are the challenges companies face in terms of adopting and using Gen AI and how can they be overcome?
The hesitancy around integrating generative AI into enterprise operations stems from a combination of factors, despite its increasingly common use on an individual basis. There are three main barriers to generative AI.
Firstly, legal regulations pose a significant challenge. There is a prevailing lack of clear global guidelines on how generative AI can be utilised in critical sectors such as healthcare and banking. The regulatory landscape remains a grey area, creating apprehension among companies fearing legal repercussions. Businesses must closely monitor evolving regulations and perhaps engage with policymakers to have a say in how these guidelines are developed.
Secondly, privacy concerns are significant deterrents, particularly when dealing with commercial LLMs. The uncertain implications surrounding the sharing of sensitive business or customer information raise red flags. Companies should consider robust data governance policies and consult privacy experts to manage these risks effectively.
Thirdly, the financial constraints are non-negligible. Building proprietary LLMs demands enormous computational power and large data sets, making it an impractical venture for most businesses. The soaring demand for GPUs further exacerbates the situation, and even third-party LLM services come at a steep price. Companies might look into cost-effective, scalable AI solutions and consider forming partnerships with AI service providers to mitigate this challenge.
Next year is likely to bring more clarity on these fronts as the technology and its accompanying regulations mature. Monitoring these trends closely will be crucial for companies aiming to effectively leverage generative AI while navigating its associated challenges.
Are companies aware of regional and global policies surrounding the use of Gen AI?
Policies relevant to generative AI significantly vary across companies and industries. Some sectors such as healthcare, finance, and defence tend to stay up to date with policy development whereas others are not fully aware of or find it challenging to navigate the rapidly evolving legal landscape.
AI regulations tend to be decentralised and have substantive differences from one jurisdiction to another, making it a complex challenge for companies that operate on a global scale to comply with a myriad of regional laws and regulations.
It’s indispensable for companies to be aware of these policies because ignorance can result in legal repercussions, damaging reputation and customer trust. As generative AI advances, standardised regulations may simplify compliance for companies.
To overcome these challenges and improve awareness, companies can:
- Engage with legal and regulatory consultants to help comply with the current laws affecting generative AI.
- Collaborate with industry forums and AI ethics committees to gain insights and face upcoming changes.
- Conduct compliance audits to ensure that the usage of generative AI remains within the purview of existing laws.
- Educate employees about the importance of the ethical considerations surrounding AI.
- Benefit from adaptable compliance frameworks for diverse regions in cross-border policy review.
These steps raise awareness and ensure compliance with regional and global policies on generative AI usage.
What factors do companies need to consider before adopting Gen AI such as having a centralised data strategy?
Adopting generative AI is a complex process that requires meticulous planning, including a centralised data strategy. This ensures consistent quality, access, privacy, and security across the company, all coming together for a successful AI implementation.
Companies need to invest in staff training or recruit experts to implement and maintain generative AI models, as they are highly specialised projects. The costs of setting up the required hardware, software, and personnel should be factored into the budget along with ongoing operational costs such as model updates, maintenance, and compute resources.
If a company chooses to partner with third-party AI vendors, then it should rigorously assess the reliability and customisation of solutions to meet specific business needs. This includes reviewing customer testimonials, evaluating service-level agreements, and ensuring that the vendor’s product aligns with the company’s long-term objectives.
Furthermore, aligning the generative AI initiatives with broader business goals is essential. Companies should identify key performance indicators (KPIs) to measure the effectiveness and ROI of their AI projects. A well-aligned strategy defines AI’s role in customer service, innovation, and operational efficiency. Scalability and adaptability are vital. The chosen architecture should handle growth and evolving needs in the fast-paced AI field. Also, companies must be aware of regional and global regulations and proactively address issues like data privacy and algorithmic bias.
Consider these factors for a successful generative AI adoption aligned with strategy, regulations, and future adaptability.
How can companies experiment with Gen AI to predict the future of strategic workforce planning?
Companies can leverage generative AI to enhance strategic workforce planning by first centralising their data strategy for robust, reliable insights. They should align AI initiatives with key performance indicators, such as employee retention and productivity, to gauge effectiveness. Experimentation could involve using AI to forecast skill set gaps, assess future staffing needs, or simulate various business scenarios for better contingency planning.
Ensuring legal compliance and ethical governance while doing so is crucial. The objective is to utilise generative AI as a predictive tool that aligns with broader business goals, offering a competitive edge in workforce optimisation and customer success.