With the generative AI wave, we are seeing innovation and a lot of agility has come into the ecosystem.

With the generative AI wave, Kyndryl is seeing innovation and a lot of agility has come into the ecosystem. (Image by Shutterstock).

Kyndryl: solving AI challenges for businesses

  • Businesses are increasing their spending on AI. 
  • However, many are still challenged on AI adoption. 
  • Kyndryl is hoping to help businesses in their AI journey. 

Businesses around the world continue to make big plans for AI adoption. While some companies have limited the use of certain AI applications at work due to cybersecurity concerns, the reality is that most businesses are still finding ways to implement more AI into their day-to-day processes.

According to a report by Gartner, worldwide IT spending is projected to total US$5.1 trillion in 2024, an increase of 8% from 2023. While generative AI has not yet had a material impact on IT spending, investment in AI more broadly is supporting overall IT spending growth.

In Asia Pacific, IDC reports that spending on AI will grow to US$78.4 billion in 2027 at a compound annual growth rate of 25.5%. The increase in AI spending reflects a shift toward leveraging cutting-edge technology to reimagine operations, improve customer experiences, and maintain a competitive edge in a rapidly changing market.

While those figures look promising, reality paints a different picture. Most businesses, especially those in Southeast Asia, are still facing several challenges in their AI adoption. To understand more about these issues and how they can be solved, Tech Wire Asia caught up with Andrew Lim, managing director in ASEAN for Kyndryl.

What are the challenges when it comes to AI adoption?

Andrew Lim, managing director in ASEAN for Kyndryl.

Andrew Lim, managing director in ASEAN for Kyndryl.

As with any revolutionary trend, generative AI not only promises immense potential for enterprises in broadening their sphere of services but also brings with it certain pitfalls. Enterprises must recognize certain specific challenges such as scaling generative AI solutions to drive business impact, ethical concerns, the threat of data breach, and inadequate supervision, among others. While these challenges are present with traditional AI, they are even more nuanced for generative AI use cases.

For example, scaling AI for business impact can only be done with the right data foundation (data strategy that aligns with your business strategy, data governance, data quality, data architecture, etc.). Your business insights are only as strong as the data your generative AI is fed, so your data quality = your AI/generative AI solution quality.

Three serious ethical implications of generative AI – and AI generally – in business are:

  • Potential bias: If not properly addressed, AI systems can inadvertently perpetuate bias present in training data, leading to discriminatory outcomes. At Kyndryl, we recognize this concern and are committed to ethical AI deployment. Since a machine is only as strong as the data it’s fed, Kyndryl helps customers build a strong data foundation. This includes employing stringent data curation processes to minimize bias in our AI models and continually evaluating and refining our algorithms to ensure fairness and equity.
  • Transparency: Businesses must be able to explain how AI arrives at specific decisions, especially when these decisions impact individuals’ lives or opportunities. AI models must be designed not to just provide answers but also the rationale behind those answers. This will require an industry-wide effort but can help foster transparency and foster better understanding of AI decision-making processes.
  • Security and privacy: In many countries around the world, customer and employee information is protected by clear data privacy laws and regulations, so any generative AI solution must adhere to the highest privacy standards, with robust data encryption and access controls to protect sensitive information.

While AI is tremendously appealing and well-intentioned, it’s a power technology that must be properly guided and managed. Because of this, appropriate guardrails and governance must be set from the start for responsible AI deployment and for the machine to function as a trusted companion in any business IT infrastructure. Furthermore, these guardrails must appropriately strike the balance between managing risks and enabling sustained innovation and growth.

Despite the challenges, the growth of AI is inevitable, as its benefits, like accelerated innovation, increased productivity and efficiency, etc outweigh the concerns. However, the right guardrails are crucial to ensure the deployment of responsible AI solutions and that the ethical concerns of all stakeholders are met while ensuring judicious and sustainable use of resources.

Kyndryl has assisted several organizations in the region in their AI and digital transformation journey.

Kyndryl has assisted several organizations in the region in their AI and digital transformation journey. (Image by Shutterstock).

How is Kyndryl helping businesses in their AI adoption journey in Southeast Asia?

Based on an organization’s maturity, we offer them tailor-made options that fit their purpose, be it a solution or architecture. In the case of both enterprise and generative AI, that foundation is data. Enterprises lacking the in-house skills to manage their data will need trusted partners who are experts in data governance, business processes and the appropriate applications for their industries.

More and more, businesses are coming to us for support and guidance on the adoption and implementation of AI which is why we are continuously expanding our network of partners to curate relevant solutions for our customers.

With the generative AI wave, we are seeing innovation and a lot of agility has come into the ecosystem. A task that would earlier take months to be done is now crunched to a few weeks. The turnaround time is rapid. We are currently focused on a few aspects of generative AI. We are working with our customers by helping them prioritize what use cases can be looked at from a quick-win standpoint.

For instance, we have recently collaborated with Microsoft to enable the adoption of enterprise-grade generative AI solutions for businesses on the Microsoft cloud. Together, we are also looking to continuously rapidly design, develop, and drive new generative AI innovations and solutions across their enterprises.

We’ve also signed a multi-year strategic collaboration agreement with Amazon Web Services (AWS) to accelerate its generative AI solutions adoption and provide its customers with the necessary end-to-end services to incorporate these solutions to drive business impact.

Separately, our AI-readiness program allows us to collaborate with customers through the identification of generative AI use cases and co-creation, implementation, execution, and management of solutions that help them reach their business goals. For example, we’re aware that banks are eager to adopt generative AI to improve fraud detection, increase internal productivity, and further transform the customer experience. To help them, we recommend they keep three strategies in mind:

  • Look to emerging AI standards for guidance: governments and organizations around the world have introduced AI frameworks and public sector proposals to help guide the development and use of AI such as the World Economic Forum’s AI Governance Toolkit, G20 AI principles, and so on.
  • Pay attention to the source and integrity of the data: the most common mistake in designing and building the AI system is failing to source reliable, high quality data from which trustworthy and unbiased models and outcomes can be built.
  • Begin any generative AI journey with a use case: one of the most effective approaches to successfully using generative AI is customer support. This lets you build a trustworthy model that offers customers reliable truth. Over time, human intervention will be needed less frequently as the AI system becomes more reliable.

It is through these collaborations with hyperscalers and programs that we have implemented that we have the ability to work with our customers and help them prioritize what use cases can be looked at for quick, secure, and effective AI implementation and adoption.

The skills shortage poses a significant challenge not only for AI adoption but also for businesses investing in various technologies.

The skills shortage poses a significant challenge not only for AI adoption but also for businesses investing in various technologies. (Image by Shutterstock).

Skills shortage is a major problem not just for AI adoption but for any businesses looking to invest in more technologies. How is Kyndryl helping with this?

ASEAN is a diverse market and an early adopter of new technologies, applications, and solutions – including the demand for hybrid cloud, modernized apps, digital services, networking, and security that enabled the rapid rise of remote work not just for the tech sector but across all verticals. Consequently, this phenomenon is continuing to foster new growth opportunities within the region and is putting pressure on non-tech industries to mirror developments in tech which might not have enough technical skills to execute the program.

Therefore, the skills shortage poses a significant challenge not only for AI adoption but also for businesses investing in various technologies. In response, Kyndryl has been focused on upskilling its workforce including in critical areas such as security and compliance, application and infrastructure architecture, software integration, data analytics, and data migration.

Internally, we invest in getting our teams accredited and focus on advanced automation, which allows us to redeploy our upskilled workforce into areas our customers are focused on. By doing this, we are aiming to keep trusted people, that our customers value, inside the business.

At the same time, we aim to cultivate the same culture with our customers and their organizations – to allow our IT strategies and solutions to guide them in restructuring their talents into different divisions that would best benefit their productivity. This would help bolster their efficiencies and in turn, support them in producing the best output that fosters growth in their respective sectors.

In the second part of the article, Lim talks about other emerging technologies businesses should look into as well as the impact of AI regulations on business decisions. He also shares Kyndryl’s goals for 2024.