- 2 days ago
AI agents promise to transform how businesses operate. But where should companies invest, and will the returns justify the cost? Cynthia Ng speaks with AWS's Nam Je Cho about the realities of adopting AI agents and how they're rewriting the rules of work.
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00:09Hello, you are watching Awani Review. I'm Cynthia Ng.
00:13We have become used to using AI to help us work faster,
00:18but now it is actually taking on the work itself.
00:22And we have seen some examples where projects used to take months
00:27being completed in just days with smaller teams.
00:30That is the promise of AI agents.
00:34But how practical is this for businesses that may not have large resources like a big tech company?
00:42And also, how much control are we prepared to hand over to these AI agents?
00:48That is what we will be discussing today.
00:51And joining me, we have Namjie Cho from Amazon Web Services.
00:55He is the Director of Technology for Asia-Pacific, Japan and China.
01:00Namjie Cho, great to have you here and really excited to our conversation.
01:04Thank you, Cynthia.
01:05So Namjie, let's start with this because you work with businesses across the region,
01:0911 countries to be specific in adopting new technologies.
01:13So we have spent the last few years talking about how businesses can use AI.
01:20And now we are talking about AI agents actually carrying out the work itself.
01:25How big a shift is this?
01:27I think the shift began when the generative AI hype cycle began back in 2022.
01:33Now, if you look at the journey of AI from when the chat GPT moment happened to today,
01:38there is definitely a shift in the way that AI agents are being used from just asking a question
01:45and getting an answer to moving on to actions and outcomes.
01:50Now, what we are seeing with our customers around the world today is so exciting
01:53because there is a big tech shift happening.
01:55And as part of that, the technology is becoming very accessible for every individual and every business
02:02to complete tasks very, very easily.
02:06Alright, let's talk about real world applications.
02:08How much of this are you seeing using AI agents?
02:11How much of this are you seeing in businesses today?
02:13I would say almost all of our customers who develop software and develop applications
02:18are using some form of AI coding agents today.
02:21So today, I think I can't imagine an organization developing software without an assistance of an AI coding agent like
02:31Amazon Kiro.
02:32We have lots of customers who also run Claude Code or Codex on Amazon Bedrock today.
02:40Okay.
02:41Well, I do also want to talk a bit about Malaysia because we have plenty of SMEs, smaller businesses.
02:46And I think the challenge is that while they are knowledgeable in basic AI applications,
02:54getting from that to authentic AI to AI agents could be quite a journey.
02:59So what have you been observing with their interaction with local businesses here so far?
03:03So in my experience, what I see right now with this AI transformation
03:07is the capabilities of AI is equally available to whether you're a large enterprise
03:12or you're a small to medium business.
03:15So what AI agents and these agentic applications will allow you to do
03:19is actually lifting the floor for everybody to have that equal playing field
03:23to be able to build really innovative applications to transform their business.
03:28Okay.
03:28So this idea that this technology is available to bigger companies or organizations that have more funds
03:37is just not true.
03:38That evidence that I see is that the technology and the capability of AI agents
03:43is just so accessible to everybody.
03:45Now, anybody that comes onto AWS today can sign up to a free account
03:50and give our Amazon Kiro a try almost instantly.
03:53Okay.
03:55I want to hone in on that.
03:56Accessibility.
03:57Cost is lower now, but how low?
03:59So let's say an ordinary business running a fairly small company, 50 employees.
04:05And what is that practical journey for them to get on board with using AI agents from AWS?
04:14Yeah.
04:14What I have seen work really well with these businesses, small to medium businesses,
04:19is that you start small and you start with a use case that's well known
04:25in terms of the problem statement inside your organization, where you know the challenge that you want to solve for
04:30and there are KPIs that already exist.
04:33And we have our desktop agent like Amazon Quick, which is really quick to set up.
04:38You connect it to all your data sources.
04:40And then all you have to do is to have a conversation with your AI agent about the goal
04:45and the outcome that you want to achieve.
04:47And it gets underway in solving your problem almost instantly.
04:50All right.
04:51So one area where we're already seeing some pretty remarkable change is our results in software development.
04:57And I want to bring in an example from AWS itself,
05:02where I think a project that was originally expected to take about 30 developers up to 18 months.
05:08You guys completed it in 76 days with just using AI agents and six senior engineers.
05:16That is quite a difference.
05:18What made that possible?
05:20I'm just trying to understand the productivity and efficiency of it.
05:23I mean, the great example you're talking about there is how Amazon's Bedrock service team
05:27rebuilt their inference engine on the service.
05:31And this project in perhaps before the world of AI agents would have taken anywhere from 12 to 18 months
05:38with 30 engineers.
05:40And for this particular project, six senior engineers were able to complete the project in just 76 days.
05:48Now, what a lot of people, when they look into this example, will say is that,
05:55was it the AI coding agent on his own that was able to achieve that outcome?
05:59But the reality is that there's a lot of things that needs to be combined in order to achieve that.
06:04It's the context of that organization that we had and the experience of the engineers who worked on that project.
06:10But I want to emphasize the fact that that example is very much about fundamentally changing the ways that you
06:18work inside a software engineering team.
06:20It's changing the workflow.
06:22It's optimizing every step of that process with AI agents where some of those steps could be what I would
06:30call easy decisions.
06:32So you hand it off completely and that particular step to become autonomous.
06:37Whereas there are some steps that actually require the experience of a very senior engineer to actually make a joint
06:43decision with the AI agent.
06:45But this was some of the most experienced senior engineers at Amazon, right?
06:48So how much of that result could an ordinary business realistically replicate?
06:52Yeah, that's a great question because what I love talking to our customers about is that one of the great
06:57things about what we do with our customers is that we get to learn from all of these experiences inside
07:03Amazon,
07:04as well as what a large enterprise in, say, Australia is doing or what an innovative startup in Singapore is
07:11doing.
07:12And we combine all of that knowledge and bring that force to our customers to share in those learnings, those
07:18methodologies, the best practices.
07:21So one of the things that I get to do with my customers every single day is to bring our
07:26learnings and make sure that they're not reinventing the wheel.
07:30And they're actually not starting from zero, but they have an accelerator that can start on with us.
07:36All right. Taking that example now, but if a team can work with far fewer people, doesn't that change how
07:43many people a company actually needs to carry out a task now?
07:46So I think that's a very commonly held belief in the world of AI today.
07:50And what I would say to you is that what I'm observing from my direct customer engagements across the region
07:58is that these customer teams that are building software and that are building really innovative solutions hasn't actually been reduced.
08:08What they're able to do is that as their AI agents enable them to finish their work quicker, be able
08:14to complete their tasks and outcomes faster.
08:18What they're able to do is to actually take on more experiments, more projects, and they're actually able to solve
08:24their existing problems within the businesses much, much quicker.
08:28It means that there is an acceleration of benefits that they're delivering to their customers.
08:35I do want to press on your point a bit because there is another consequence here while we understand the
08:42remarkable productivity gain.
08:44A lot of the routine work that AI agents are doing right now is precisely how junior employees learn, right?
08:52So if machines are already doing that work, automating the more junior work, the basic work, what then happens to
09:00experience, the talent pipeline?
09:02Just wanted to get your thoughts on what you're seeing in AWS itself.
09:06Yeah, I mean, I would probably start off by saying I would quote our AWS CEO, Matt Garman, when he
09:11was asked this very similar question in that,
09:14are we investing in junior talent that I was on? And he said, absolutely. You know, we believe that like,
09:19you know, these junior talent that come through the ranks that are gaining that human experience is going to be
09:25critical for a long, long time to come.
09:26And I see that across my own organization, where we're making big investments in hiring talent from what we call
09:33early career talent program, all the way up to senior talent.
09:36And we want to make sure that human experience is gained through the knowledge and experience of their peers around
09:45them.
09:45So I strongly believe that having junior talent that comes through an organization and continued investment is critically important.
09:54And so I think that's what our CEO, Matt Garman, has very clearly stated on multiple interviews as well.
10:01Okay. And when you meet businesses, local businesses especially, of course, investing in AI or AI agents while the cost
10:08has come down, it still requires a bit of money.
10:11Right. So I think naturally businesses are asking where are the returns and, you know, am I investing in the
10:18right systems in the right places?
10:21Right. So can you share with us, you know, what are some of the considerations that leaders in the company
10:27have concerns with when you speak with them?
10:30When it comes to valuing the return?
10:32Yeah. I think there are a lot of concerns today that I would acknowledge that as they start to look
10:37at some of these use cases,
10:40I think cost has certainly been a big consideration.
10:43And there is certainly concerns from business leaders around potential cost of AI.
10:50And right now what we're learning is that there isn't going to be one or one or two models that
10:57actually is going to be able to address all use cases.
11:00So we have a service called Amazon Bedrock, Amazon Bedrock. And what we provide on Bedrock is a choice of
11:07models. Like you don't need the world's smartest models to do very simple tasks.
11:12And also there are some really intelligent models that are really optimized for very complex tasks.
11:21But where we have been able to advise our customers on the best use of some of these models is
11:28we understand their business use case and the goals that they're trying to achieve.
11:31And then we work backwards from the outcome that they want to achieve. And right now on Amazon Bedrock, we
11:37have a number of model choices where we have the two front team model providers from Anthropic as well as
11:43OpenAI.
11:44But also you would have heard about the OpenWeights models where they are significantly cheaper, they're still very intelligent.
11:51And so the level of intelligence that these OpenWeights models has is able to achieve customers' goals for a very
11:57low cost.
11:58Okay. I do want to bring up this research by Gartner, the research from Gartner, which forecast that more than
12:0540% of agentic AI projects could be cancelled by the end of 2027,
12:09and largely because of either unclear business value is one of the reasons.
12:15So I do want to get a sense of like how should a business work out and whether it's actually
12:20getting its money worth when they're investing in AI or AI agents.
12:25Yeah. So I would just anchor on that sort of snippet that says unclear business requirements.
12:31Well, there are a few reasons. One of them is a rising cost, unclear business value and inadequate risk controls,
12:36which we'll go to later.
12:36Yeah. I mean, let me sort of pick on the unclear business value because, you know, we've been working with
12:43our customers well over 20 years now.
12:45So, you know, AWS Cloud, it was a business that was established back in 2006. Can't believe it's been 20
12:50years already.
12:52And what we have been doing with our customers all the way through our cloud transformation is that we want
12:59to understand the customer's business need.
13:01What is the problem that they're trying to solve? And then we work backwards from our customer's requirements.
13:07Now, it's very important in whether it's in the cloud transformation, digital transformation or AI transformation, is that you clearly
13:14understand what is the business problem?
13:16What are the measures of success? And then you start working backwards from what the problem that you're trying to
13:22solve for.
13:23And I think often when a new innovative technology lands, people want to play around with it, people want to
13:29experiment.
13:30And often what we find is that if there is an unclear requirement or a KPI for that particular project,
13:38then the chances of success may not be as high as if you had a very well defined business requirement.
13:44You have the KPI for success. And then once you achieve on that sort of project outcome and say, did
13:51we hit our KPIs, you have a clear measure of success.
13:54And I think for us, it's important to work with our customers to help them to teach some of these
13:58key learnings that we've had over the years in making sure that every experiment starts off with a clear business
14:06statement.
14:07We believe that it's very important to have a clear business owner as well, because often when a new technology
14:15lands, there's always an inclination towards, is this a technology problem?
14:20But actually most challenges inside an organization is actually in their business.
14:24Or the human.
14:27People play such a critical role in the success of the business and the technology adoption as well.
14:32So define a measured outcome as one of the key areas to look into to measure success.
14:39But what are the other things that when you speak to a leader of a company, what are some of
14:44a characteristic of a tech leader to ensure that the transformation is well executed?
14:50I think you've hit the nail. It's the people.
14:53And I think what we also learned from our early days cloud experience is that skills and that people change
15:01element is so important.
15:02Because when you look at agentic AI today, even with the tools, your ways of working changes.
15:09And in many cases, you have to be able to go through that change journey in order to adopt the
15:15technology successfully and to achieve on your business goals.
15:20So I think that skills and training and the right set of enablement and people's willingness to go through that
15:26journey from this is how you work today.
15:28But this is potentially how your job and what you do changes in order to get that better outcome.
15:35Okay. Now I want to pivot and talk a bit about trust and control because now we are, I mean
15:41the whole appeal of AI agents is that they can take action without someone guiding them every step of the
15:46way.
15:47But where do you think we should draw the line on AI agents being useful and giving it too much
15:54control? Is that a concern? Is that a worry even at AWS?
15:57Yes.
15:58That's a really great question. And I would start off by saying that no organization starts their first AI project
16:05and hands off full control to an AI agent and gives it 100% autonomy.
16:12And sometimes the mental model around autonomous agents is that you either give them full control or none at all.
16:20But it's not a binary decision in that many of our customers when they start with a coding agent or
16:25Amazon's quick, that is a fully human in the loop AI agent where every important decision requires a permission from
16:34you to proceed to the next step.
16:36Now, this idea of handing control to an AI agent, autonomous agent is an incremental step.
16:44You experiment and you deploy that first use case that is low risk.
16:49You learn how to operate that because agents still need to be operated.
16:53And so once you learn about how to monitor it, how to make sure that it's behaving in the right
16:59way, you have the right evaluation and how you monitor that performance of that agent.
17:04Then you add on top and you iterate you more one step at a time.
17:08So I want to talk about when AI agents are not behaving in the right way.
17:12So when things go wrong, maybe AI agents get things wrong.
17:16Who is responsible? Because you're working with partners, businesses are using your technology.
17:20So is that something that AWS look into?
17:23Absolutely. And I think this is something that we're learning with our customers day by day.
17:27And we get a lot of feedback from our customers around some of the things that these AI agents are
17:32really good at.
17:33But also our customers give us feedback that there are certain things within deploying AI agents can actually be quite
17:41complex.
17:42For example, in order to deploy an AI agent into your business environment, you need to make sure that you
17:48think about the identity these agents undertake.
17:51You want to make sure that they have the right knowledge base, the context to make the right decisions.
17:56You also want these agents to have some memory so that you're not repeating the same thing each and every
18:01time.
18:01So in order to address some of these complexities, and this is something that is actually quite difficult to set
18:08up for an organization.
18:10So what we did was we listened to our customers and then we built a service called Amazon Bedrock Agent
18:16Core,
18:16which actually packages up all of these capabilities into a service that our customers can safely deploy these agents.
18:23Has things gone wrong before? How do you mitigate that?
18:28So I think it's important for me to emphasize that this is a very new technology and we're learning as
18:34we go.
18:36But what we, you know, I think security and trust has always been really on the top of our priority
18:43list.
18:44And so we want to make sure that anything that we develop and we make available to our customers is
18:51in response to their business need.
18:53And I think one of the things that we want to make sure is that as customers scale and adopt
18:59agents inside their businesses,
19:02it's important that they do it with confidence and trust.
19:05So we now have some services that we offer to our customers that actually address some of the domain of
19:13security, for example,
19:14where it helps customers to do the really hard things that were not easy to do in the past.
19:20But things like we have a service called AWS Continuum where it does code scanning, vulnerability scanning.
19:28It does penetration testing.
19:31And these are all capabilities that were really hard to implement inside an organization until agentic.ai came along.
19:39All right. So I know you look into 11 countries as under your remit.
19:43But I want to bring it back to Malaysia because Malaysia is seeing growing AI adoption.
19:48But research shows, this is AWS's own research, is that AI adoption in Malaysia has reached 38%,
19:55but two thirds of adopters are still using fairly basic applications.
20:00Let's talk about what's stopping them from going further, especially when they try to move beyond that pilot stage.
20:08Okay. I think this right now, there are varying different degrees of adoption and adoption maturity in the region.
20:19And what I would say is that the really easy place to get start for most organizations is an AI
20:27agent like Amazon Quick or Kiro.
20:29And then as they learn more about the capabilities of these agents, there is steps to maturity in getting these
20:39agents to solve more complex problems inside your organization.
20:43Let me put it this way. What do you think businesses underestimate when they start using something like an AI
20:49agent?
20:51That's a really good question. Underestimate. I would say most organizations that are trying to adopt AI today is so
20:58new to everybody that it's a learning journey.
21:01And I think organizations that I've seen succeed and that are moving quicker with AI than those that are not,
21:07ones that are actually focusing on people training enablement and making sure that it's actually tied to a business outcome
21:15and also measurable business outcomes with clear KPIs.
21:19So the end-to-end workflow from the inception all the way to the delivery of the outcome is well
21:25planned and measured.
21:26And I think that's something that these customers who have reached that maturity have gone through that journey from early
21:32adoption of AI to where they are today.
21:35So it's the maturity journey that they've gone on to, to reach that where they are today.
21:40In Malaysia, where do you see, what stage are you think most businesses are at in terms of adoption?
21:45I wouldn't generalize and say they're all in certain one or the other place, but I come across some very
21:53innovative startups to large enterprises that are adopting AI at scale here in Malaysia.
21:59I see lots of really dynamic environment with SMBs as well as SMEs and startups that are just starting out
22:07their journey.
22:07But what's really interesting trait amongst all of them is that they're all moving very, very quickly because you can
22:14pick these things up and just give it a problem.
22:17And you realize that it can solve some problems that you haven't been able to solve before.
22:21And the next step, they're experimenting on their next use case and the next use case.
22:25So that's what's so exciting to see.
22:27Okay.
22:28Well, just a final minute that we have now, if you're advising a Malaysian business today, what would you tell
22:33them about investing in AI agents if they are just getting stuck?
22:38If they're just getting started?
22:40If they're just getting started?
22:40Yeah.
22:40The barrier to entry, or I would say the floor has been lifted up for everybody and you can get
22:46started in minutes and you can experiment very safely.
22:50And as I said earlier in our conversation, I think like the mental model of people has to change from,
22:57it's not just good at answering questions.
23:00It can take actions and it can drive outcomes and it can do it very quickly.
23:05So I would say just, you know, learn and just get hands on.
23:08Okay.
23:09Well, thank you so much Namjie for spending some time to talk to me about the opportunities of AI agents.
23:14And great to have you.
23:15Hope to have you again soon.
23:16Thank you, Cynthia.