Business

The View From Koo: Does Your Business Really Need an AI Strategist? The Surprising Answer

Explore the benefits of hiring a Data Strategist over an AI Strategist for comprehensive data-driven business transformation.

Published

7 months ago

July 2, 2024

Koo Ping Shung (Guest Contributor)

TL;DR:

Not all businesses require an AI Strategist; an established data management process and optimised reporting are prerequisites.
AI is just one tool in the data toolkit, and not all business problems require AI solutions.
A Data Strategist with expertise in data management, analytics, and machine learning might be a better fit for most companies.

Thoughts from Data Scientist expert, Koo Ping Shung

Hi, I’m Koo. I’ve been working with Data and Artificial Intelligence for over 20 years. I’ve done all sorts of things like collecting data, managing it, and making sure it’s used properly. I also help to find useful information from data and put it into machine learning models. Every now and then, I notice what’s happening and what’s difficult in this field and I write about it.

Lately, I’ve seen a lot of people using the title “AI Strategist”. I also get asked about this a lot at my company Data Science Rex (DSR). So I thought it was time to share my thoughts.

So… grab a coffee and settle down as we unpack whether your business really needs an ‘AI Strategist’. Here’s a humble view from a Data Scientist:

The Prerequisites of AI Adoption

Firstly, there are certain conditions that need to be met in your business before you start thinking of hiring an AI strategist.

1. Data

We all know that AI works better when there are good quality data, followed by availability of relevant data. So, you need to assess whether your business has an established data management process first. What is an established data management process then? It should have the following:

Infrastructure:

Are there established data storage and retrieval infrastructure? It needs to be something like a data warehouse, a SINGLE place where all the datasets go to be stored and managed.

Data Management Processes:

Are there data management process that accompanies the infrastructure? In order to ensure your data is of good quality, it needs to be managed well. This is not forgetting that there should be processes in place to ensure high security and privacy level. These are processes that deals with Identity & Access Management or IAM in short.

Data Quality Measurement:

Are you measuring data quality? Data quality needs to be measured to build up confidence in using data. Having established data quality metrics helps in showcasing to your stakeholders that the reports or derivative products from data such as insights, visualisations and analysis can be trusted.

2. Reporting

Have your organization optimised your reporting process using data? Why is business reporting important before coming to AI?

We need to actively understand the nuances of data by examining how and when the data is collected and determining what each column in the dataset represents.

Only through a good understanding of these nuances, that are honed through multiple reporting, can we have a good understanding on what are the limitations from internal data.

Establishing AI through your business requires change management extensively. This means that throughout your business, there is a need for everyone to understand how data and AI works, followed by everyone feels that they have benefitted from using data.

An optimised reporting process throughout the company is a good signpost to say that your business and its employees have benefitted from data for their own tasks.

3. But… Do You Really Need AI for your digital transformation?

Once these conditions have been met, there’s a good chance that if there are AI use cases in your organisation, you may be successful. The reasons are it has tackled two of the biggest roadblock to adopting Artificial Intelligence in business, Data and Change Management.

AI is just part of the data toolkit. You can see AI as, besides the ubiquitous tools that you find in the toolkit like hammer, screwdrivers, etc, that smart shiny drill that can auto-change the drill bit based on your needs.

From this analogy, you will realise that not all your business challenges are about “drilling” i.e. AI solves certain type of problems, not all problems. What does this translate? Not all the business problems you have needs AI, and followed by do you have enough AI use cases to justify hiring a full-time AI strategist?

Do not forget that AI use cases like any IT projects has costs to it, short-term being the proof-of-concept, design & planning, data management, training of the AI models, and long-term being the maintenance, constant monitoring and validation of AI models, cloud computing subscription, ensuring the essential skills stays within the organization.

Switching Perspectives

Now put yourself in the shoes of an AI strategist, let us forget about his/her background, experience and suitability for the role for the moment.

The AI Strategist main role is to identify areas in the business where AI can be used, proposed the necessary changes that needs to be made, and how AI should be integrated into the business process, keeping in mind the alignment to strategic goals of the business.

Have you ever heard of the saying, “To a hammer, everything is a nail.” To an AI strategist…everything must use AI? Wait a minute! You probably realise right now, a few paragraphs before, we did mention that not all use cases need AI! AI is just one of the tools in the data toolkit! If a simple average is needed to solve a business challenge, why go through all the hassle and the costs (short- & long-term)?

4. So What Do I Need?

Data will be the new normal, and taking advantage of data will be what good business leaders will constantly be thinking about. There are multiple tools, with variation in value derived from data, that business can take advantage of, and Artificial Intelligence is part of it.

What you really need is a Data Strategist, that can help your business see end-to-end from data collection all the way to change management when the derivatives of data, such as decision models or insights, are being used in business processes.

I could write a whole article on how you should select a Data Strategist for your organisation (let me know in the comments below if you’d like to read this), but to give some quick pointers, the person needs to have background in the following:

Data Management & Data Quality
Data Analytics & Data Analysis
Machine Learning, Model Deployment & Model Monitoring
Generic Business Processes & Change Management

I can tell you very quickly such talents are not easy to find. But a word of caution, do not jump onto one because of the titles. Titles are easy to award, but of most importance is their experience and background! Be comfortable with it.

And please remember:

“AI is not a one-size-fits-all solution, and other data tools might be more suitable for specific problems. It works better when there are good quality data, followed by the availability of relevant data.”

I wish your company all the best in building up data capabilities and taking advantage of data analytics and artificial intelligence (if any). –– Koo

Have you considered the benefits of a Data Strategist for your business instead of an AI Strategist? Share your thoughts below and don’t forget to subscribe for updates on AI and AGI developments. You can also connect with Koo Ping Shung on LinkedIn by tapping here.

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Author

Koo Ping Shung (Guest Contributor)

Koo Ping Shung has 20 years of experience in Data Science and AI across various industries. He covers the data value chain from collection to implementation of machine learning models. Koo is an instructor, trainer, and advisor for businesses and startups, and a co-founder of DataScience SG, one of the largest tech communities in the region. He was also involved in setting up the Chartered AI Engineer accreditation process. Koo thinks about the future of AI and how humans can prepare for it. He is the founder of Data Science Rex (DSR). View all posts

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ByteDance’s $12 Billion Investment in AI Infrastructure Set for 2025

ByteDance plans to invest over $12 billion in AI infrastructure in 2025 to enhance global model training capabilities with Nvidia chips.

Published

7 hours ago

January 30, 2025

AIinAsia

TL;DR:

ByteDance is planning to invest over $12 billion in AI infrastructure in 2025, with $5.5 billion allocated for AI chips in China and $6.8 billion dedicated to enhancing model training capabilities internationally.
This move is aimed at strengthening ByteDance’s AI prowess to stay competitive against Chinese tech giants like Baidu, Alibaba, and Tencent.
The investment includes a significant focus on acquiring Nvidia chips to bolster global AI initiatives.

If you’ve been keeping an eye on the tech world, ByteDance—the mastermind behind TikTok—is making headlines again. This time, they’re gearing up for a colossal $12 billion investment in AI infrastructure in 2025, according to the Financial Times. Let’s break down what this means and why it’s a big deal.

The Investment Breakdown

ByteDance’s ambitious plan involves:

$5.5 billion on AI chips in China: This substantial investment is set to double their spending from the previous year, highlighting a strong commitment to enhancing domestic AI capabilities.
$6.8 billion to boost global model training capabilities: A significant portion of this budget is earmarked for acquiring advanced Nvidia chips, underscoring ByteDance’s strategy to leverage top-tier technology for AI model training.

Why This Matters

Elevating AI Capabilities: With this hefty investment, ByteDance aims to elevate its AI infrastructure, ensuring that platforms like TikTok continue to offer cutting-edge features and personalised user experiences.
Staying Ahead in the AI Race: In the fiercely competitive tech landscape, this move positions ByteDance to keep pace with, or even outpace, rivals such as Baidu, Alibaba, and Tencent, all of whom are making significant strides in AI development.
Strategic Partnerships: By investing heavily in Nvidia chips, ByteDance is aligning itself with a leader in AI hardware, which could lead to more advanced and efficient AI models powering its platforms.

The Bigger Picture

This investment isn’t just about staying competitive; it’s about setting the stage for the future. As AI continues to evolve, companies that invest in robust infrastructure and cutting-edge technology will be better positioned to lead the market. ByteDance’s substantial commitment to AI underscores its vision to be at the forefront of this technological revolution.

Final Thoughts

ByteDance’s planned $12 billion investment in AI infrastructure is a bold move that signals its intent to lead in the AI-driven future. By focusing on both domestic and international advancements and partnering with industry leaders like Nvidia, ByteDance is not just keeping up with the competition—it’s setting the pace.

What are your thoughts on ByteDance’s massive AI investment? Let’s discuss in the comments below.

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Business

Paul McCartney’s Concerns: AI Copyright in the Creative Industry

Sir Elton John and Sir Paul McCartney are raising concerns over AI’s impact on artists’ copyrights.

Published

1 day ago

January 29, 2025

AIinAsia

AI and copyright in the creative industry

TL;DR:

Sir Elton John and Sir Paul McCartney are calling out AI for ripping off artists’ work—without paying a dime.
They’re backing changes to the Data (Use and Access) Bill to protect copyrights in the age of generative AI.
This is a global wake-up call: AI is amazing, but can creators afford to lose control of their own art?

What’s the Fuss About?

If you’ve been paying attention to the creative world lately, you’ve probably heard a lot about AI “stealing” from artists. Sounds dramatic, right? Well, it’s not just hype. Big names like Sir Elton John and Sir Paul McCartney are making some noise about how AI is being trained on artists’ works—without permission or payment.

Here’s the deal. AI systems, like the ones used to create fake Drake songs or uncanny art, need heaps of data to learn. That data? Often, it’s pulled from publicly available sources, which means your favourite song, artwork, or book might have been used to teach an AI how to mimic its style. And guess what? Nobody’s cutting cheques for the original creators.

The Legal Battleground: The Data (Use and Access) Bill

This is where the Data (Use and Access) Bill comes in. Right now, it’s under review in the UK, and some suggested amendments could be a game-changer. If approved, they’d make sure creators have a say (and get paid) when their work is used to train AI. Think of it as copyright protections 2.0—designed for the AI era.

Sir Elton and Sir Paul argue this is essential. Without such protections, creators might lose control of their own work, leaving the door open for corporations to profit off their creativity without a second thought. And let’s face it: that’s not a future anyone wants.

McCartney’s concerns are shared by a coalition of publishers, artists’ groups, and media organisations known as the Creative Rights in AI Coalition, which opposes weakening copyright protections.

Why Creators Are Worried

The backlash isn’t just about royalties (although, let’s be honest, that’s a big part of it). It’s also about authenticity. Imagine an AI-generated song using Sir Paul’s voice—but without his input or consent. Is it still “his” music? And if the lines between real and fake keep blurring, what happens to trust in the creative industry?

The tension is real:

Creators say AI is exploiting their work without permission.
AI advocates argue it’s all “fair use” and promotes innovation.
Fans? They’re caught in the middle, wondering if the next viral song is even legit.

What’s Next for AI and Copyright?

The future of copyright and AI is still being written (pun intended). If the amendments to the Data (Use and Access) Bill pass, it could set a global precedent for how we protect creativity in the AI age. But legislation is only part of the solution.

Here’s what needs to happen:

Transparency: Companies need to be upfront about where their training data comes from.
Fair Compensation: If you’re using someone’s work, pay them for it. Simple.
Collaboration: Artists, lawmakers, and tech firms must find a balance that works for everyone.

Platforms like OpenAI are starting to take small steps, allowing rights holders to opt out of having their work used for training (source: OpenAI Blog, https://openai.com/blog). But let’s not kid ourselves—there’s a long way to go.

And you can watch the interview with Paul McCartney here.
You can read more about the proposed legislation and its potential impact on APNews.

The Big Question

AI is undeniably powerful, but it doesn’t replace human creativity. It’s like giving a robot a paintbrush—it can make something impressive, but does it have soul?

What do you think? Should AI have free reign to use whatever it wants, or is it time for tighter rules to protect creators?

Join the conversation, subscribe to our newsletter, and become part of our community of AI enthusiasts. Let’s shape the future of AI—together.

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DeepSeek vs. Silicon Valley: How a Chinese AI Startup is Outpacing Global Giants

How DeepSeek, a Chinese AI startup, is challenging Silicon Valley’s dominance with innovative, resource-efficient AI technology. Learn why Asia is the next big thing in AI.

Published

2 days ago

January 28, 2025

AIinAsia

TL;DR:

DeepSeek, a Chinese AI startup, has unveiled the R1 model, which can self-improve without human supervision, challenging resource-heavy methods favoured by Silicon Valley.
Asia’s growing tech ecosystems, like those in China, Singapore, and India, are proving that homegrown talent and focused R&D can compete globally.
China is projected to dominate 26% of the $15.7 trillion AI market by 2030, showcasing its rapid rise as an AI powerhouse.

When you think of cutting-edge AI development, Silicon Valley probably comes to mind first—home to giants like OpenAI, Google, and Meta. But here’s a twist: a relatively small Chinese startup, DeepSeek, is making waves with its groundbreaking AI innovations, leaving some of the West’s biggest names playing catch-up.

How is DeepSeek pulling this off with fewer resources? Let’s dive into their secret sauce and why this matters for Asia—and the world.

The Underdog Story: DeepSeek’s R1 Model

DeepSeek recently unveiled details about its R1 model, which can self-improve without human supervision. Yes, you read that right. Their AI doesn’t just rely on training data—it learns, refines, and grows all on its own. This marks a shift from resource-heavy methods favoured by Silicon Valley to something far more efficient.

Unlike the West, where AI labs have access to near-limitless funding, DeepSeek operates with lean resources. This forces them to be laser-focused on optimising their tools. It’s a story of innovation through necessity—and one that tech hubs in Asia can learn from.

As The Financial Times explains:

“DeepSeek’s ability to make strides with limited computing power and localised talent pools underscores the growing sophistication of Chinese AI development.”
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Why DeepSeek Matters for Asia

DeepSeek’s success sends a strong message: you don’t need Silicon Valley’s mega budgets to make a global impact. For countries like India, Indonesia, and even Singapore, this demonstrates that homegrown talent and focused R&D can compete on a global stage.

Asia is already leading in digital innovation—look at the rise of super apps like Grab and Gojek, or how TikTok has reshaped the social media landscape. DeepSeek’s approach could pave the way for other regional startups to disrupt industries, from healthcare to fintech, with AI-driven solutions.

The Global AI Chessboard: What’s at Stake?

This isn’t just a “cool tech story.” It’s about the shifting dynamics of global AI power. For years, the narrative has been: Silicon Valley leads, everyone else follows. But DeepSeek’s R1 model—and its bold claim to challenge Western dominance—flips that script.

According to a report by PwC, AI could contribute $15.7 trillion to the global economy by 2030, with China expected to take nearly 26% of that share. That’s $4 trillion—just from China.

It’s clear that Asia is not just participating in the AI race; it’s positioning itself to lead it.

Lessons for Asian Startups

DeepSeek’s story holds valuable lessons:

Efficiency is Key: You don’t need a $500 billion budget to innovate (looking at you, OpenAI). Focused, resourceful development can yield incredible results.
Local Talent Wins: DeepSeek’s reliance on regional talent highlights the untapped potential in Asia’s growing tech workforce.
Think Global, Build Local: DeepSeek’s model shows that even regionally focused projects can have global implications.

The Road Ahead

DeepSeek’s trajectory raises questions: Can other Asian startups replicate this success? Will the global AI stage see more “DeepSeeks” rising from unexpected places? One thing is certain: Silicon Valley should keep an eye on Asia—not just as a market but as a competitor.

But here’s a question for you: With AI innovation heating up across Asia, are you ready to keep pace with the latest breakthroughs? Stay ahead of the curve by subscribing to our free AIinASIA newsletter, where we deliver cutting-edge insights, trends, and stories like this straight to your inbox. Don’t miss out—sign up today and join the conversation!

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