On 22 February 2024, Human Managed and Microsoft Philippines hosted a joint roundtable session with our customers and partners to discuss operationalizing AI in enterprises' day to day processes, within their unique contexts. 

This article covers the key takeaways on the 3 'Ops' with the highest impact and value for businesses in the age of AI: DataOps (feeding AI), MLOps (tuning AI), and IntelOps (applying AI).

The progress in AI largely spearheaded by tech giants, open source communities and new players has made a new generation of AI-driven products and services accessible to any business that want them. Many are calling it the newest ‘inflection point’ in technology since cloud computing. Pilots, initial development and initial launch of AI apps for specific use cases have never been easier. But adoptable examples of operational AI with reusability, scalability and adaptability — especially in enterprise settings across multiple domains, is still limited.

We wanted to kickstart the conversation on what operationalizing AI in ASEAN businesses really looks like. It was an incredibly engaging session where we focused at length on the enterprise problems to which AI can be applied, particularly the “Day 2” operational ones that keep the business running and generate revenue. Thank you again to our customers and partners for being a part of this journey.

The first section of DataOps brought the focus to ground reality amidst the overwhelming hype and promises of AI — what working with data everyday to secure and scale a business is like.

I asked our speakers and guests to share openly about 

“What is the day to day data problem you have in operations?”

Their answers could be summarized into 5 buckets:

It’s difficult to know what you have and to track the changes in your business consistently. Without current and accurate view of your assets, their posture and behaviors, you are running blind.

Data outputs generated by tools and solutions are not always trustable or right. Human’s role is to analyze data critically, apply context and other perspectives. However, verifying data and removing false positives still remains largely manual, repetitive and time consuming.

Accurately predicting what is likely to happen from data analysis is desirable, but very challenging to achieve, because you need large volumes of reliable data, effective models, and quick feedback loops.

With data, events and alerts being generated everywhere, it’s hard to prioritize what is important, and to decide what to do at speed.

Bringing together all technologies and tools into cohesive operational pipelines to detect, react, and respond is difficult, especially with the volume and velocity of data keeps on increasing.

After establishing the ground reality from data perspective, we zoomed out to see what the world of opportunities and possibilities is from AI perspective.

, Data & AI Specialist from Microsoft shed light on the fast evolving landscape of AI, powered by huge advancements in foundation models including large language models. Businesses are spoiled for choices when it comes to plug-and-play AI tools and features that solves specific problems or tasks, such as document and meeting summaries, code generation, or natural language chatbot.

However, operating AI at scale across multiple use cases and business processes require custom-trained models. This is where businesses can potentially unlock competitive and differentiating value from AI, rather than using a ready-made solution that everyone else has access to. However, this is also where most MLOps and LLMOps challenges occur, such as:

with AI developments evolving so quickly, it is difficult to decide what direction to take and what models to use.

even after identifying the use cases and models for AI application, building the pipelines and productionizing requires deep technical knowledge, not to mention the capacity for experimentation (which will definitely come with failures).

typically in an enterprise, data generation and management is siloed, which leads to siloed and limited decisions. To achieve holistically contextualized intel from data, tracking the data lineage at its raw form is important to train the AI models but difficult to achieve, due to the data not being integrated.

Generic or foundation AI models, no matter how advanced, will not magically produce accurate and precise outputs suited for your unique business context. For machine learning to be operational, AI models must be trained, tuned, and improved with data, logic, and patterns unique to your business.

AI models only improve with proper feedback, so decisions have to be made as to who will give the feedbacks and the mechanism for it. There is also a responsibility to check for and reduce bias and prejudice.

No data to serving pipelines = no repeatable processes

Even when you make headway with the DataOps and MLOps problems, there is one big piece of the puzzle that is left in operationalized AI: presenting and serving the AI outputs to the right recipient (humans or machines) at the right time. What the outputs of dataops (insights & intel) and MLOps (labels) are in themselves important. What is also important — but often deprioritized — is how they get generated, and how they get delivered and tuned to be usable across the business.

At Human Managed, we call this continuous process of data-to-intel, intel-to-labels, labels-to-serving pipelines: IntelOps. To get the most out of AI in your day to day business processes, we believe it's crucial to build in the specifics of distribution of labor between humans and machines.

What Human or AI processes analyze what type of data and use cases? (Logic, model)

What Human or AI processes generate outputs (insights, recommendation)?

What Human or AI processes do you use to execute actions? (Functions, tasks)

Finally, how and where do you present the AI outputs? (API, report, notification)

We shared the ways that HM will be applying AI across all of our services. There is no one mode of AI; it will be different based on the type of implementation, function and the level of automation required.

Specific applications of AI depends on its implementation, function, and degree of automation.

Conclusion: The AI game is one of data, context, and processes

We cannot know what the future of AI holds, but one thing that is for certain is that there will be more data, not less. Everything is a datapoint that could be analyzed if you want it to be. But to what end? The human capacity and process improvements are not increasing at sufficient rate to keep up with the technological developments and expected outcomes, so we need to prioritize the data problems to solve.

Working with data is a given in today’s operations, but many enterprises are far from applying data ops at scale to contextualize and train AI models that is operational across key business processes.

Making data, models, and their outputs operationalized and always production-ready — not just once, but every day — with limited resources is the real challenge.

The good news is that it’s not that these problems are impossible to solve — in fact, many of them have been broken down and solved by different ecosystem players and domain experts, bringing new innovative products and services to the enterprise industry.

This shifts the technology service and partnership models as we know it, and makes data and AI platform a lot more accessible to enterprise customers than ever before. Just like how cloud computing distributed infrastructure and software, today’s AI developments are breaking down and distributing the AI functions (analytical, generative, model) even further.

The AI game is one of data, context, and processes. We believe that the companies that continuously build the context of their business as distributed and scalable data (instead of tribal knowledge in individual’s minds) and work with distributed ecosystem of partners and suppliers will be the ones that grow with AI instead of constantly playing catch up.

We all understand the potential of AI, but how are you applying it for your organization's operational processes, within its unique context?

Operationalizing AI: Human Managed & Microsoft Joint Roundtable on AI Strategy for ASEAN

A review of the year 2023 at Human Managed

2023, Processed

We launched our platform and the Human Managed I.DE.A (Intelligence, DEcisions, Actions) at GovWare Conference. This is our transformative experience in running an exhibition booth and many other things for the first time.

Back in June 2022, we took the decision to be a 

, Asia’s largest cybersecurity event held in Singapore. This year was its 7th edition — the first in-person conference since 2019, and was anticipating over 10,000 participants (and in the end, did have that many participants!).

Initially there was push back from the team: “We are not ready for a stage as big as GovWare” / “We’ve never run an event before” / “Why don’t we start with a smaller scale event first?”

These were all valid concerns, but we had a stronger case to go for it:

COVID-19 hit soon after our inception. Borders shut down and we were forced to build and grow 100% remotely. This equipped us with a lot of valuable learnings on distributed work, but it also slowed us down, especially when it came to creative, synchronous work. So we missed a few opportunities to share our work in the market in a way that was impactful enough. Now was not the time to wait a little longer – it was time to go big.

Immediate market feedback from customers, suppliers and partners is invaluable — we wanted to see how people who don’t know us interact with our products with zero prior knowledge.

Immense learning opportunity to be surrounded by such a rich variety of cybersecurity players in the ecosystem, from industry behemoths to startups.

External pressure, when applied appropriately, can boost motivation and effectiveness. This was the moment for our entire team to jump straight into the pit and show what we’ve got.

The I.DE.A that solves the problem of the Information Age

There are countless problems in the Information Age. The particular one we have chosen to solve is 

indecision and inaction from information overload

Our problem statement became clearer and simpler over the years, after continuously researching the market and observing directly from our customers.

What they do not have, however, are what comes after the data. “Now you have it, what do you do with it?” :

 They are not getting relevant intelligence from their data when they need it.

 They are unable to take effective and responsible decisions from data.

 They are not making progress on executing their decisions because things get dropped or get blocked by inefficient processes.

The best intelligence and decisions are of no use if they are not followed up with actions. So, we built a platform that doesn’t do just one thing but many things. At our core, we: generate intelligence, recommend decisions, and track actions.

We call this the Human Managed I.DE.A. — and this naturally became our leading message for GovWare. Check out our 30 seconds 

Our service flows turns data to intel (Discover, Profile, Detect) and intel into decision (Triage, Course of Action) and decision into action (React or Resolve).

The booth for design thinking and open source

For our booth, we wanted to openly share our thinking and execution behind our products and services.

We use flow charts and sticky notes whenever we do design thinking, so it made perfect sense to use our booth’s double-sided walls to display the 

 that literally run our platform services.

We don’t have teams for marketing, branding or events, but by sticking to our core values, we had our DIY booth design that was distinctively ‘HM’ in a matter of a few weeks.

This was a fun and spontaneous session: finalizing our artworks and planning their placements based on the traffic flow in our booth.

The team got hands-on to test mounting our wall displays in our office.

Inside the walls, our booth area was divided into two to represent our ‘front stage’ and ‘back stage’.

The front stage was set up like a studio for our platform’s 

 — our web application. We had multiple touch screens for visitors to interact with and ask questions on our product.

In contrast to the clean and simple front stage, the back stage was designed for interactive and creative conversations. We had physical whiteboards and sticky notes covering an entire wall for design thinking discussions, workshops, and brainstorming sessions.

Put the components all together and, voila! There it was: the Human Managed platform.

It was a surreal and emotional moment to see our ideas come to life in the middle of the Marina Bay Sands Expo and Convention Centre — the largest exhibition venue in Singapore.

The interactions between the ecosystem players

Our goals for GovWare were simple: to share our I.DE.A with our ecosystem, and get up to five quality conversations with potential users, customers, and partners. We are proud to have achieved them and much more.

The energy and interest from the crowd were beyond our expectations.

Private and public sectors from various countries strongly resonated with the problem we are solving.

And of course, we didn’t go without some engaging whiteboard sessions.

GovWare was an event of many firsts for us, stretching ourselves in ways we couldn’t imagine until we found ourselves breaking through the only things that were imagined in the first place: limits.

how businesses can achieve their digital transformation expectations through cybersecurity

, in a world that is totally distributed and ‘invisible’. The entire process from preparation to delivery was not easy, but extremely rewarding and fun. More than anything, I was excited to share HM’s perspective on a topic that is a challenge for anyone running a digital business today. More on this in a separate blog soon.

A slide from my talk that illustrates the huge amount of data exchange that happens throughout a distributed supply chain of assets of a digital business. Here I highlight the data flow throughout the ‘invisible’ side of the business that lies behind the front-facing web application (i.e. between the machine-to-machine interactions such as APIs and code.)

All of the team got in front of the camera to speak about their domain — unscripted and on the fly. Their initial hesitation didn’t last long, and in no time they were sharing confidently about their work, in their own words and styles. 

 where these content will be uploaded soon!

We demonstrated a deployment of our partner 

Crowdstrike’s Falcon LogScale log management solution

 in less than three minutes on Kubernetes, using: a) 

 (an infrastructure control plane) to provision compute resources independent of cloud provider. b) a CD pipeline with 

Our Chief Engineer (aka. MicroBuilder) Jesum destroys and builds a log management service in less than 3 minutes using Crossplane and ArgoCD.

The months leading up to GovWare were the most stimulating experience I’ve had to date at Human Managed.

Everything we had shown at GovWare, we have built ourselves, from the messaging, to the web app, to the booth artworks. And to get market reaction and feedback real time on something that you’ve poured your everything into is an indescribable experience.

We’ve learned so many valuable lessons. Here are a few.

Build for internal resiliency, not external glory. Videos and photos only tell a small, curated portion of the reality. There were so many curveballs and multiple things did not go to plan. We had technical issues at the eleventh hour before the event. In the end, some team members (including those that were going to lead some key demos and workshops) were not able to attend the conference at all. But we backed each other up. Some things were completely missed, but some other things turned out better than plan A. There were no unexpected curveballs that our team was not capable of handling and that was the much more important part, not whether everything was presented as we intended.

Doubts (both self-inflicted and external) will always be there. But keep going and give it your best. You never know who might be inspired by your example. Startups less than two years old came up to our booth to learn from our journey — they said they were motivated by our presence, especially as a fellow Singaporean startup. A student came to ask whether we had any work opportunity because she got inspired by talking to a few of our team members. Getting this kind of feedback was awesome. Going forward, we hope to inspire many more in our ecosystem as well as continue to learn from them.

You will never be fully ready, but you are (almost always) more ready than you think.

Create a challenging but supportive environment where your team can try new things on their own. An event or conference is a great one, but so are other initiatives, like an impromptu video shoot ;)

We felt like we’ve aged a few years by the time the conference was over. Internally, we joke about “Life Before GovWare” and “Life After GovWare”. There is definitely some truth to this, in that the conference was a marker of a state change for us.

We’ve been sprinting for as long as we can remember. But we all know that life after GovWare is just another starting line, with more spring in our sprints and a growing conviction that we are on the right path.

Thank you to everyone who has interacted with us. Your feedback, encouragements and critiques gave us so much clarity on the next steps to improve on our products and services. We would love to have more of you along on our journey.

Want to conduct an I.DE.A workshop or build a prototype in less than two weeks? Have an idea for collaboration or partnership? Feedback on our products and 

Note: This blog was originally published in November 2022 and has been updated for accuracy.

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Operationalizing AI: Human Managed & Microsoft Joint Roundtable on AI Strategy for ASEAN

2023, Processed

Human Managed launches at GovWare 2022