Suraj Kotipalli, APAC Business Leader – Data Platform & Solutions, Hitachi Vantara, offers five key actions that enterprises can take to accelerate GenAI adoption and achieve tangible business outcomes.
The possibilities of AI, especially through GenAI are limitless and it has revolutionised how organisations can enhance customer experience, bring new products quickly to market, streamline operations and enhance employee productivity.
Gone are the days when AI was a complex technology reserved for a select few. According to a McKinsey report, there has been an uptick in GenAI use across all regions, with the largest increase in APAC.
Businesses of all sizes can leverage the power of AI for tasks ranging from automating processes to generating valuable insights and driving innovation. Looking at specific industries, respondents working in energy and materials and in professional services report the largest increase in GenAI use. However, translating this potential into real-world benefits requires a strategic approach.
There are five key actions that enterprises can take to accelerate GenAI adoption and achieve tangible business outcomes. They are:
- Tailoring AI solutions for success at scale:
The road to successful GenAI implementation begins with a clear understanding of an organisation’s specific goals. Moving beyond generic applications, enterprises should focus on building customised solutions that directly address industry challenges. These solutions should be designed for scalability, ensuring they can grow alongside the business and deliver a continuous return on investment (ROI).
The ideal approach involves a comprehensive suite specifically designed for industrial use. This suite should empower businesses to automate tasks, streamline workflows and accelerate the generation of valuable insights.
However, the success of such solutions hinges on customisation as a ‘one-size-fits-all’ approach simply won’t cut it. The chosen GenAI suite should be adaptable, allowing for customisation to integrate seamlessly with existing workflows and infrastructure.
- Bridge the AI expertise gap:
While GenAI offers open-source models and tools, simply having access isn’t enough. Enterprises need not only the technology but also the expertise to utilise it effectively. This necessitates a new breed of technical guidance that bridges the gap between a business’s domain expertise and the intricacies of GenAI.
Savvy domain experts, such as financial analysts or manufacturing engineers, possess a deep understanding of their specific industry challenges and opportunities. However, they may lack the technical know-how required to leverage GenAI effectively. A study by the Asia Pacific Economic Cooperation revealed that 65% of business leaders in APAC identify the lack of skilled professionals as significant barrier to AI adoption, underscoring the need for bridging the expertise gap. This is where targeted technical guidance comes into play.
- Embracing ‘Data anywhere, AI everywhere’:
Data management is a critical, and often the most challenging, aspect of GenAI adoption. Enterprises have transitioned through various data storage models, from on-premises data centres to cloud environments and hybrid deployments. Today, the key lies in understanding where data resides, how to manage it effectively and how to leverage it for AI-driven insights.
The solution lies in utilising a suite of tools and technologies specifically designed to manage data effectively within a hybrid infrastructure. These tools should empower enterprises to discover, prepare, train and inference data efficiently for GenAI applications.
This includes tools for data cleansing, transformation and annotation, ensuring the data is high-quality and ready for use. Additionally, the ability to blend structured and unstructured data becomes crucial. GenAI can unlock valuable insights from a variety of data sources, including text, documents, images, videos and sensor data.
- Build a high-performance AI-ready infrastructure:
GenAI solutions are computationally intensive and training and running complex models requires significant processing power. To meet these demands, enterprises need robust platforms that can support cutting-edge hardware that can provide the horsepower necessary to train and run trillion-parameter models.
Beyond processing power, robust file systems and cost-effective object storage are crucial components of the GenAI infrastructure. File systems need to be exceptionally fast to handle the constant data movement involved in training and running GenAI models.
The ideal solution lies in an AI-ready infrastructure and tightly integrated that combines all these elements – high-performance computing, high speed networking, super-fast file systems, and cost-effective object storage – into a single, pre-configured package. This integrated solution should be engineered for load optimisation, ensuring efficient resource utilisation and cost savings.
- Choose your AI Partner for success:
The journey towards AI adoption is rarely a solitary endeavour as few enterprises possess the in-house resources and expertise to navigate the complexities of AI on their own. This is where partnering with an AI solution provider ecosystem that boasts vertical market expertise becomes crucial.
These partner ecosystems bring together a diverse range of players, including hardware vendors, software developers and domain-specific consultants. The ideal partner should have a deep understanding of the specific challenges and opportunities within each industry.
Beyond industry expertise, choosing a partner who actively utilises AI internally is a valuable indicator of their commitment to the technology. Such partners are more likely to have developed innovative AI solutions, like ‘copilots’ or AI-powered companions, that can streamline internal processes and optimise resource allocation. This also ensures the chosen AI partner has a strong understanding of the challenges associated with AI governance, data security and infrastructure management.
The arrival of Generative AI has democratised access to AI, empowering enterprises across industries to unlock its transformative potential. By adopting these five key actions, enterprises can accelerate GenAI adoption and achieve tangible business outcomes.
