Early adopters of cloud technology were early beneficiaries of its positives: scalability, cost savings, accessibility and more. But as experts caution, a move to the cloud isn’t a “set it and forget it” decision. Over time, outdated security protocols, underutilized resources and legacy architectures can create issues that hinder business growth and even open you up to cyber risks.

If your business was an early mover to the cloud, now is a good time to review your cloud strategies and identify opportunities for optimization, automation and modernization. Below, members of 

 share key areas businesses should focus on when assessing their cloud presence and why these factors matter.

Over time, cloud waste accumulates—unused resources linger and ownership becomes unclear. Manual cleanup is tough, and outdated access roles often remain overprivileged, creating security risks. Automating discovery and remediation is key to tackling hidden waste and excessive permissions, helping organizations reduce costs, strengthen security and maintain a well-governed cloud environment. - 

For seasoned cloud pioneers, now is the moment to rethink legacy security blueprints. Designed for simpler eras, many early frameworks fall short in countering today’s advanced cyberthreats. Modernizing encryption, access controls and monitoring systems is crucial for meeting evolving compliance standards, enhancing resilience and safeguarding critical assets in an ever-changing threat landscape. - 

Companies reviewing their cloud presence should assess cost optimization. Over time, cloud usage can become inefficient due to unused resources, outdated pricing models or overprovisioning. Regular audits help identify savings, improve performance and ensure alignment with business needs, maximizing the value of cloud investments. - 

Cloud repatriation is on the rise because many are paying a premium for overprovisioned resources and vendor lock-in pricing models that appear designed to inflate costs. With AI, costs will likely go up. Take the time to list out and divide out what belongs on the cloud and what should be brought back on premises, as greater savings and security may be achieved in company-owned environments. - 

Businesses should evaluate workload portability—the ability to migrate workloads across cloud providers or repatriate them on premises. This flexibility prevents vendor lock-in, enhances cost control and strengthens negotiating power as cloud expenses rise. A portable infrastructure ensures adaptability to meet changing business needs while optimizing long-term cloud strategy and efficiency. - 

AI will unlock a new layer of intelligent cloud management, including automated cloud provisioning, intelligent cost and throughput management, advanced analytics, enhanced security features, and more. Evaluate your cloud service for value-added features that can unlock business leverage, instead of looking through a pure cost lens. - 

Companies should assess scalability challenges across physical and virtual spaces. While a holistic approach offers advantages, it adds complexity. The cloud helps, but it isn’t a one-size-fits-all solution, especially for specialized resources like GPUs for AI workloads. Technologies like Kubernetes enable scaling, but applications must be designed to leverage these tools effectively. - 

8. IVR And Contact Center Testing Strategies

Early cloud adopters should reassess their interactive voice response and contact center testing strategies. Many moved to the cloud for scalability, but they may not have full visibility into global call quality and the customer experience. Ensuring consistent, real-time monitoring and proactive issue detection can prevent silent failures that impact customer satisfaction and revenue. - 

The main thing that companies should focus on is getting executive buy-in for locking down cloud portals to read-only and “break glass” type changes. This focus, combined with automation solutions, will greatly benefit companies seeking to gain control of sprawling cloud environments. - 

Companies must review cloud security and data compliance, which many service providers offer. Data regulations have evolved, so revisiting cloud strategies and security is essential, especially as organizations integrate AI. - 

11. Cloud Economics, Automation And Interoperability

Early cloud adopters must balance AI-driven innovation with a systematic cloud strategy. As AI evolves, enterprises need resilient, more cost-efficient systems. CTOs should focus on cloud economics, automation and interoperability. Leveraging AI where impactful—while avoiding overreliance on trends that may not drive lasting business value and enterprise growth strategy—is crucial. - 

Early cloud adoption was infrastructure migration. Now the world is run on data—and cloud resources as a data model is one of the most complex. Businesses must take stock of their entire digital assets (including the cloud) and their relationships and criticality to the overall business. Getting data-driven visibility will inform decisions for cost optimization, security and risk management. - 

13. Resource Rightsizing And Optimization

Many businesses are overprovisioned, paying for underutilized instances or outdated architectures. A review should focus on rightsizing resources, leveraging spot instances and optimizing storage tiers. Cloud-native AI tools can help automate scaling, reducing waste while maintaining performance and security. That said, it only works if AI is enabled across the organization. - 

Companies should reevaluate their current cloud architecture for modernization opportunities. Many early cloud adopters still run outdated lift-and-shift deployments that fail to leverage serverless computing or AI-driven automation. Refactoring applications can enhance scalability, reduce costs and improve performance, ensuring the cloud strategy remains competitive and future-ready. - 

15. Security Protocols And Infrastructure

Companies assessing their cloud presence should take a look at their security protocols and infrastructure. Early cloud adopters often implemented security measures that may now be outdated, leaving them vulnerable to new threats. A thorough review can identify potential weaknesses and ensure alignment with current best practices for data protection and compliance. - 

Over time, many early adopters have accumulated a mix of cloud services, legacy systems and third-party tools that may not work seamlessly together. This can lead to data silos, inefficiencies, a suboptimal experience and unnecessary costs. In my opinion, a key area to evaluate would be interoperability opportunities that can create a one-stop-shop experience through a consolidated engagement layer. - 

17. Data Classification, Life Cycles And Governance

Early cloud adopters often lifted and shifted data without a long-term plan, leading to inefficiencies, security risks and skyrocketing costs. Reviewing data classification, life cycle policies and access governance ensures storage aligns with business needs. A modern approach optimizes performance, cost and compliance while unlocking AI-driven insights. - 

18. Opportunities To Leverage Managed Cloud Services

Early cloud adopters built a lot of services and infrastructure, which public cloud providers ended up offering as managed services. While some of these managed services were not good at launch, a lot of them improved over time and are now better, cheaper and more reliable than their self-managed versions. Early cloud adopters should assess cloud infrastructure that competes with their internal services. - 

In the analytics space, there are a great number of early adopters with bespoke data lake implementations. Such systems require significant system engineering and maintenance, and a large majority are candidates for cloud data warehousing platforms. Those that do fit the data lake pattern can benefit from modern tooling and frameworks, as well as “data lake as a service” offerings. - 

Many businesses “lifted and shifted” on-premises workloads to the cloud, merely transferring existing inefficiencies. Companies should evaluate whether they’ve embraced cloud-native technologies, like serverless computing, purpose-built databases and automated infrastructure management, to convert technical debt into opportunities for operational efficiency. - 

originally published on Forbes Technology Council

Time For A Cloud Check-Up? 20 Key Areas To Review

The Human Managed Intelligence Decision Action (I.DE.A.) platform improves decision efficiency in the age of Data and AI

Decision Dilemma From Information Overload

Most modern digital businesses are united in one aspect: they want to leverage data to drive innovation, fuel growth and improve service. All business leaders also need to accept and plan for one trend: whatever the future of AI, there will be more data, not less.

The volume, velocity, and variety of data generated and stored are growing exponentially, estimated to reach more than 2,000 zettabytes by 2035. Oracle cites that more data leads to a 10x increase in the number and complexity of decisions business leaders and employees make every day. Alert fatigue, manual analysis, and indecision all contribute to operational inefficiency.

Decision Efficiency From Data-Driven I.DE.A.s

Leveraging data and AI to drive business value must be approached from both data and process levels. All businesses have unique data and processes, so the approach to data-driven outcomes must be contextualised for every business.

Human Managed is a cloud-native service platform for 24/7 cybersecurity, risk, and data operations with a mission to improve decision efficiency. Our platform processes data and generates custom intelligence, decisions, and actions (I.DE.A.) to protect and manage their digital assets. Some of our top solutions cover Cloud Security, SecOps Center (SOC), Fraud Management, and Data Cataloging.

To get started customers simply need to select the cyber, risk and digital outcomes they want, and install the agent for the Human Managed Foundational Platform (hm.works) to receive or collect data for chosen outcomes. Customers can bring their own data source (BYOD) or go with standard software in the Human Managed platform.

Our Product Stack structures the data and runs cycles of analysis to generate personalised intelligence for selected use cases in Cyber, Risk, and Digital Workbenches. The Workbench platforms are domain expert systems that generate and serve customized insights for selected use cases.

Finally, our Intelligence Decision Action (I.DE.A.) platform delivers the outputs as a 24/7 service through custom report, notification, and dispatch. Customers can also explore and search their processed and contextualised data.

A more intelligent & secure approach to I.DE.A.s

As a leader in comprehensive data management, we seek to help businesses unlock smarter and more secure decision-making. By controlling threats, managing risks and scaling opportunities our modular platform transforms complex data into actionable insights.

Through this we make data accessible to all, providing companies with the intelligent insights they need to achieve greater progress and unlock their full potential. Our platform helps organisations reduce downtime by automating processes that would otherwise be manual, enhancing workflow effectiveness and efficiency.

Through continuous monitoring and rapid incident response, we ensure businesses are protected against emerging threats, giving them the ability to stay focused on growth while their data security needs are expertly managed.

With in-depth insights and transparent reporting, companies can maintain better governance and comply with regulations more effortlessly, ensuring both business continuity and long-term sustainability.

Our suite of microservices cover everything from compliance management to threat detection to enhancing your cybersecurity posture, leading to significant gains in operational efficiency.

By leveraging advanced analytics and machine learning, companies of all sizes can uncover hidden opportunities, enhance productivity, and gain a competitive edge in their respective industries.

Our platform has been built to boast high adaptability, enabling businesses to tailor solutions to fit their unique needs and empowering them to stay competitive in a rapidly evolving digital landscape.

To read the full Aboitiz Group report, click 

Empowering Decision-Making with Data Intelligence

Picture this: You’re driving through a sprawling, unfamiliar city at night, with roads constantly shifting beneath your wheels and signs disappearing as soon as they appear. That’s what managing enterprise cybersecurity can often feel like.

Cloud assets can be vast, and a lack of visibility can create blind spots where threats lurk. Many businesses operate in multicloud environments, each with its own architecture, rules and configurations. Without a unified approach, inconsistencies can arise, leaving gaps in defense. Add to this the problem of siloed operations. Often, security, IT and DevOps teams work in isolation, leading to inefficient communication, slow response times and increased vulnerability to risks.

IBM’s Cost of a Data Breach Report 2024

 found that 40% of data breaches affected data residing in multiple environments. The report also indicated that breaches involving data in public cloud environments had the highest average cost, reaching $5.17 million. Organizations are increasingly applying AI and automation to reduce the cost of such breaches. However, businesses are still grappling with technological possibilities and actual decision-making.

Decision Intelligence Platforms: A GPS For Better Outcomes

Here's where decision intelligence platforms (DIP) can help. DIPs provide a real-time, unified view of all your data and insights transformed into explicit decisions and actions to be taken to reach a certain business outcome. Think of it as a context-aware GPS taking you to a required destination, and once that destination is achieved, the system learns from the path taken for subsequent improvements.

Although still an emerging trend, the adoption of decision intelligence (DI) tools and techniques has been growing to deal with the increasing complexity of decision making. According to 

, by 2026, market convergence will prompt 50% of organizations to evaluate analytics and business intelligence (ABI) and data science and machine learning (DSML) platforms as a single platform.

In the context of cybersecurity, DIPs provide the following:

• Contextualized And Unified Visibility:

 DIPs map out all assets across cloud platforms, track changes as they occur and maintain up-to-date business context to inform decisions.

 DIPs continuously monitor the cloud environment, providing early warnings and allowing teams to reroute or take preventive action before damage occurs.

 DIPs anticipate potential vulnerabilities by analyzing historical patterns and emerging trends, shifting from reactive to proactive security management.

 DIPs combine data analytics and AI techniques to generate explicit and auditable decisions.

 DIPs evaluate outcomes from decisions and actions and consistently improve decision models based on feedback.

Potential Roadblocks Of Decision Intelligence Platforms

However, multicloud complexity also needs technical problem-solving, stakeholder management and strategic decision making to navigate obstacles, including:

 Many security analysts and IT teams have been using manual processes or legacy tools for years and may view DIPs as disruptive rather than beneficial.

 Organizations often have complex security stacks that include firewalls, SIEMs, threat intelligence feeds and compliance tools.

 DIPs rely on large-scale data collection and analysis, raising concerns about data privacy, sovereignty and compliance, slowing down platform deployment.

 Without proper tuning, DIPs can flood teams with threat intelligence alerts, many of which are false positives.

Transforming Data Into Decision Intelligence: Two Case Studies

No matter how advanced a navigational system's features are, a journey can't begin without a well-defined destination. In the context of decision intelligence platforms, it's crucial to have a clear understanding of the organization's goals and the needs of its stakeholders.

When a major ASEAN bank approached Human Managed, they faced significant challenges: a lack of visibility over their assets, complex environments with data silos and legacy architecture. Their goal was clear—protect distributed digital assets and enhance the maturity of their security operations.

To reach this goal, relevant data sources and use cases were curated. Their digital assets and security controls were cataloged with their context and relationships. The data catalogs and graphs formed the knowledge base to generate decision intelligence for various cyber and risk use cases, including cloud security, SecOps and fraud management. Over time, the bank's decision navigation system was built to support the bank's strategic, tactical and operational decisions.

Some outcomes were transformative. The bank saw a 90% reduction in network security violations, a 97.8% reduction in mean time to respond (MTTR) to phishing attacks and a 97.9% reduction in MTTR to DOS attacks.

 or SDVs, projected to include up to 300 million lines of code by 2030. An IBM and Microsoft 

 shares the case study of an auto manufacturer that was looking to go fully electric and expand its base of connected vehicles. A sample of critical IT assets revealed that assessed risk could result in annual losses of over $1 billion. Leveraging its investment in Microsoft Azure platform services, the company worked with IBM to embed a “secure by design” methodology across the entire supply chain. Gaining visibility over and closing security gaps from suppliers to backend services was a result of leveraging decision intelligence infrastructure.

The world of decision intelligence is still new territory, and any journey into the unfamiliar can be daunting. The service providers who are building the nuts and bolts of decision intelligence platforms and the early adopters who are willing to test these initial versions are true collaborative partners. They're building a future where human and machine interactions will run the gamut of decision support, decision augmentation and decision automation. It’s an exciting future, but at present, many unknowns still must be worked through. Collaborative partnership thus holds the key to the future of decision intelligence.

Navigating Cybersecurity With Decision Intelligence Platforms

How Charmaine Valmonte is steering a century-old firm’s evolution from traditional infrastructure to AI-powered tech enterprise while maintaining security

The transformation of century-old enterprises into digital businesses has become a defining challenge of corporate strategy. In Southeast Asia, where traditional conglomerates maintain significant economic influence, this evolution carries implications for national infrastructure and development. The Aboitiz Group, with 44 business units spanning power generation, banking, food, land, and infrastructure, highlights this shift.

From its beginnings in Philippine agriculture, through shipping and maritime operations, to its current position managing critical infrastructure, Aboitiz has adapted to technological change. Today, the company positions itself as the Philippines’ first “techglomerate” – integrating AI and data science across sectors that range from renewable energy to digital banking.

Securing this transformation falls to Charmaine Valmonte, Chief Information Security Officer at Aboitiz Equity Ventures. “We're using technology to accelerate and enhance innovation through our people,” she explains, describing the company's shift from traditional operations to technology-enabled growth.

Operating at the scale of protecting systems that touch millions of lives across Southeast Asia, Charmaine brings military precision to this corporate mission. “I spent 21 years in the US Army as a signal officer, providing telecommunications, data and voice to our troops wherever we deployed, whether in the middle of the desert, Europe or Asia,” she says. This background in military communications evolved into cybersecurity leadership as technology shifted. “We started in information security around 2001-2004. Back then it wasn't known as cybersecurity.”

The stakes extend beyond corporate strategy. With interests from renewable energy to digital banking, the security of Aboitiz’s systems impacts national infrastructure. The company aims for a 50-50 split between renewable and thermal power capacity by 2030, while its financial services handle critical banking data. This combination of traditional infrastructure and digital transformation creates distinct security challenges, requiring new approaches to system protection.

The stakes of this transformation extend beyond corporate strategy. With interests from renewable energy to digital banking, the security of Aboitiz’s systems impacts national infrastructure. The company aims for a 50-50 split between renewable and thermal power capacity by 2030, while its financial services handle critical banking data. “We drive change for a better world by advancing business and communities,” Charmaine says.

Aboitiz’s path from agriculture to technology spans more than a century of Philippine economic development. The company began in farming, moved into shipping and maritime operations, and now operates across 44 business units. Yet its current transformation marks the most significant shift in its operating model.

“We have legacy businesses – power, banking, brick and mortar operations – but we're pivoting to digitisation,” says Charmaine. This pivot manifests in concrete changes across sectors. The banking arm exemplifies this evolution, moving from traditional branches to cloud-enabled digital banking, powered by data science and AI.

The company’s manufacturing operations now stretch across five Southeast Asian markets, while its infrastructure division manages multiple airports. Each sector incorporates technology not as an add-on, but as a fundamental operating principle. This creates what Aboitiz terms the ‘techglomerate premium’ – value generated through cross-sector technology implementation.

“We can develop different products, create synergies between the businesses, and learn from one another using technology and innovation to add more value,” Charmaine highlights. This approach requires significant cultural change. “Our CEO has said that to transform, it has to start with all of us as humans – we are the builders and beneficiaries.”

The transformation extends beyond internal operations. Aboitiz’s data science and AI arm develops models for government, healthcare and financial services. In power generation, the company deploys AI to expand renewable energy operations, targeting equal distribution between renewable and thermal capacity within six years.

The scale of change demands new approaches to infrastructure. “We’re moving away from analogue to digital mills using AI and robotics in our plants,” says Charmaine. “Infrastructure now means understanding how we can utilise new technology in cement production and water supply services.”

This transformation creates distinct technical challenges. The company must maintain security and operations across both legacy systems and new digital infrastructure. “In cloud environments, we understand that it’s easy for the organisation to bring up an instance, take in a serverless application, take in a function as a service,” notes Charmaine. “The old way of doing things when we were system-focused is no longer applicable.”

The results of this transformation appear in practical metrics. In financial services, AI models achieve 86% accuracy in detecting fraudulent accounts. In manufacturing, digital systems enable real-time monitoring across five countries. Each success reinforces the company’s strategy of technology-led growth across sectors.

As Charmaine explains, the shift from physical infrastructure to cloud operations at Aboitiz necessitated a fundamental change in security strategy. “Previously, we used to think of security in terms of how many systems you have, where you store your data and how you compute,” she says. “But when you flip that story over, you're using data versus systems because at the centre of everything is data points.”

This transition reflects broader changes in enterprise computing. With workloads distributed across cloud platforms, traditional perimeter-based security measures have proven insufficient. Aboitiz operates across multiple cloud service models, making system-centric approaches obsolete. “Being system-centric will not work today,” Charmaine states. “It’s important to understand where your data points are.”

The company’s security team implemented this shift through continuous monitoring of data movement. Their platform tracks data points across the enterprise, identifying patterns and detecting anomalies. When a new cloud system launches or a configuration changes, the monitoring system flags potential security implications immediately.

“If there is a noncompliant configuration in our systems, perhaps in the firewall or cloud environment, we detect it through AI-powered monitoring,” explains Charmaine. “These patterns serve as a base – if there is a deviation, there is a trigger or alert.”

The speed of business transformation creates specific security challenges. Aboitiz likens the process to ‘changing the tyre while the car is moving.’ Security teams must protect new digital initiatives without impeding business growth. This requires constant adaptation of security models.

“The organisation may pivot, align, take on mergers, acquisitions, or centralise, decentralise,” says Charmaine. “There are many pivots that create an evolving computing environment. That becomes your challenge, but it’s also an opportunity because we build around this requirement.”

The data-centric approach provides flexibility during transformation. Rather than securing specific systems, the team focuses on protecting data flows. This allows business units to adopt new technologies while maintaining security standards. The approach proves particularly valuable during mergers and acquisitions, where different technology stacks must integrate securely.

“We need to protect the organisation as we're going down the road,” Charmaine notes. “Both threat actors and your organisation will continue to grow and move. Understanding this reality shapes our security strategy.”

At Aboitiz, AI serves as a force multiplier for security operations. “We call it augmented intelligence because at the base of it is a human, but we use AI to do what humans cannot,” says Charmaine.

The company’s implementation of AI in financial fraud detection demonstrates the potential of this strategy. Their system identifies mule accounts – legitimate accounts controlled by criminal networks – with 86% accuracy. “In the financial world, accounts can be taken over by syndicates or used by attackers,” explains Charmaine. “Our AI models allow us to proactively identify money laundering activities.”

Identity management represents another key application of AI at Aboitiz. The system builds baseline patterns of user behaviour, then flags deviations automatically. “If someone connects at three in the morning and that’s not their usual pattern, they get bumped out of the system,” says Charmaine. “They can’t access internal files because they're not connecting during their baseline period.”

This behavioural analysis extends beyond time-based patterns: the AI correlates data across multiple systems, tracking how users interact with data and how information moves through the enterprise. When anomalies appear, the system can trigger automated responses or alert human analysts.

“The collection, analysis and correlation between data streams cannot be done by humans today,” notes Charmaine. “It has to be augmented with deep learning or machine learning capability.”

The company maintains this balance between automation and human oversight through clear delineation of roles. AI handles data correlation, pattern recognition and initial threat detection. Security professionals focus on investigating alerts, developing new detection models and understanding emerging threats.

“We use automation for the menial tasks and AI for the data sets,” Charmaine explains. “This allows our professionals to continue learning new technologies and how to secure emerging systems.”

Aboitiz’s technology transformation relies on strategic partnerships to accelerate implementation. “We look into projects that the organisation will require a year or two from now, not today,” says Charmaine. “You’re trying to learn if your partner allows you to scale and experiment with new ideas.”

The company works with Palo Alto Networks to implement zero-trust network architecture, replacing traditional VPN systems. This partnership provides AI-enabled threat detection capabilities across the enterprise. Human Managed, meanwhile, delivers asset visibility and cyber intelligence platforms – enabling the correlation of security data across monitoring systems.

“These partners are integral to our success,” explains Charmaine. “They provide the technologies we need, the expertise on technologies we want to learn, all with the goal of protecting the organisation and its stakeholders.”

This partnership approach extends to Aboitiz’s infrastructure investments. The company secured contracts to operate three airports, adding to its existing aviation portfolio. In the power sector, investment focuses on renewable energy expansion. “We’re putting significant funding and resources into providing a clean energy portfolio,” says Charmaine. “We want a 50-50 balance between renewable and thermal capacities by 2030.”

Data science and AI applications vary by sector. In power generation, AI optimises renewable energy operations. The financial services division uses AI for fraud detection and risk assessment. Manufacturing facilities implement AI and robotics to modernise production processes.

“We’re using AI across sectors – power, banking, food, infrastructure," notes Charmaine. “The organisation’s next step is to understand how we use technology to advance and enable business growth.”

Aboitiz: Journey to a tech-driven enterprise

The transformation of a century-old conglomerate into a technology-driven enterprise requires methodical execution balanced with willingness to experiment. “The only way to survive is to really transform,” says Charmaine. “You're moving away from analogue systems, implementing AI and robotics, understanding how to utilise new technology across traditional sectors.”

For Aboitiz, this transformation manifests in concrete metrics: 86% accuracy in fraud detection, contracts for three new airports, progress toward 50% renewable energy capacity. Yet the technical implementations remain secondary to cultural change.

“The main goal is to align technology initiatives with organisational objectives,” explains Charmaine. “We need to foster resilience while enabling growth.” This balance between security and innovation shapes the company's approach to digital transformation.

The security team’s military-derived expertise proves relevant to corporate digital defence. “In both contexts, you need to understand your environment, adapt to changing conditions, and protect critical assets,” notes Charmaine. “The technology changes, but the principles remain consistent.”

Aboitiz’s experience demonstrates how traditional enterprises can leverage technology to create new value. The company’s strategy of treating technology as a core capability rather than a support function offers a model for other organisations navigating digital transformation.

“We see technology as a means to advance business and communities,” Charmaine concludes. “The challenge lies in securing that advancement while maintaining the trust of our stakeholders.”

originally published on Technology Magazine

Aboitiz transforms its century old organization through Tech

While the hype around Gen-AI will shift to the buzz around agentic-AI in 2025, traditional sectors such as manufacturing will still be coming to terms on deploying AI strategies to become participants of Industry 4.0 or the Fourth Industrial Revolution.

This is the next phase where disruptive trends including data, connectivity, analytics, robotics and human-machine interaction is said to transform manufacturing significantly.

, while over 70% of ASEAN manufacturers polled claimed to be “non-leaders/followers” of AI adoption, 47% believe investment in AI is “very critical” for their business. Gartner’s

 Prism use case of GenAI in Manufacturing encourages leaders to evaluate AI use cases on a strategy that addresses both value generation and feasibility .

Value parameters include increased revenue, increased efficiency, managed risk and non-financial gain.

Feasibility parameters include technical feasibility, internal readiness, external readiness.

So how can leaders operationalise AI, keeping value and feasibility in mind?

 At Human Managed, we believe it begins with an understanding of data. A framework to understand the highest impact for manufacturing businesses in the age of AI, would be to understand 

“3 Ops”-DataOps (Feeding AI), MLOps (Tuning AI), IntelOps (Applying AI).

In the manufacturing sector, data can be provided through assets in an interconnected ecosystem. From a range of sensors, machine data on temperature, vibrations, pressure, power consumption, operating hours etc are collected. Industrial machines, robotic arms and assembly lines can provide process data includes information on production rates, cycle times, error rates, material usage. Image and video data from cameras capture data for product dimensions and defect detection. ERP systems provide information on inventory levels, order information, transportation data.

Given the vast array of data, manufacturing enterprises have to first solve for the issues around understanding the data itself. These can be clubbed into 5 buckets –

The visibility problem – to have a current view of assets, their posture and behaviours to be able to track business changes.

The verification problem – to ensure that data outputs generated by tools and solutions are verifiable and trusted.

The foresight problem – to predict accurately based on large volumes of reliable data, effective models and quick feedback loops.

The prioritization problem – to prioritize quickly, which data, events and alerts are essential and need attention.

The orchestration problem – to bring together multiple tools and technologies into cohesive operational pipelines to detect, react and respond effectively given the volumes and velocity of increasing data.

After establishing the ground reality from a data perspective, manufacturing enterprises need to zoom out to see opportunities in business models from an AI modelling perspective. Manufacturing data can train machine learning models for a vast array of use cases including predictive maintenance, quality control, supply chain optimization, process optimization, robotics and automation.

While there have been significant advances in foundation models and large language models, with many plug-and-play AI tools, operating AI at scale across multiple use cases and business processes, requires custom-trained models. However, this is also where most MLOps and LLMOps challenges occur, such as:

Choice paralysis : with AI developments evolving so quickly, deciding what direction to take and what models to use is challenging.

Lack of technical expertise : even after identifying use cases and models, deep technical knowledge and capacity for experimentation is needed.

Siloed data flow : to achieve holistically contextualised intel from data, data sources and management which are typically isolated in enterprises, need to be integrated.

No context models: 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 must be made about who will give the feedback and the associated mechanism. There is also a responsibility to check for and reduce bias and prejudice.

Even when you make headway with the DataOps and MLOps problems, one big piece of the puzzle is left in operationalized AI: presenting and serving the AI outputs to the correct recipient (humans or machines) at the right time.

For manufacturing enterprises, these could include real-time monitoring and alerts to track machinery, production and supply chain in real time, predictive analytics for planning and resource allocation, integrating AI-driven analytics to support decision in quality control, inventory management and workforce allocation.

While the outputs of DataOps (insights & intel) and MLOps (labels) are important, what is also essential—but often deprioritized—is how they get generated, delivered, and tuned to be usable across the business.

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

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

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

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)

There is no one mode of AI; it will be different based on the type of implementation, function and the level of automation required.

Conclusion: Balancing value and feasibility of AI in manufacturing depends on data, context, and processes

The future of AI in manufacturing will belong to companies that prioritise building a robust, data-driven foundation, making data, models and their outputs operationalized and always production-ready- not just once, but every day.

By breaking away from reliance on isolated, human-held knowledge and fostering collaboration with a distributed ecosystem of partners and suppliers, these forward-thinking manufacturers will not only harness AI to enhance efficiency but also gain a strategic edge in innovation, making Industry 4.0 a reality.

We believe that the companies that continuously build the context of their business as distributed and scalable data (instead of tribal knowledge in individuals’ minds), and work with a distributed ecosystem of partners and suppliers, will be the ones that grow with AI, balancing dual parameters of value and feasibility.

originally published on Asia Manufacturing News

Operationalising AI for manufacturing in ASEAN

Online fraud, costing ASEAN economies billions of dollars annually, is a major security crisis.

Traditional banks and fintech companies have faced significant threats in recent years due to fast-evolving fraud techniques. Recent studies show that the average cost of a data breach in ASEAN reached an all-time high of 

. Efforts from regulators and businesses show the rising importance of effective fraud management.

On October 24, 2024, the Monetary Authority of Singapore announced the 

, assigning relevant duties to financial institutions (FIs) and telecommunication companies (Telcos) to mitigate phishing scams and pay affected scam victims where these duties are breached, coming into effect from December 16, 2024. Recent reports in Malaysia 

 “the establishment of a fraud intelligence network across Asean banks to enable real-time sharing of data on fraudulent activities, enhance threat detection, and foster a unified response to cyber risks, in line with the Asean Cybersecurity Cooperation Strategy (2021-2025).” A self-initiated 

 approach between Globe Telecom and the Bankers Association of the Philippines is helping reduce financial scams.

So, how can we access better intelligence to fight online fraud in ASEAN? At Human Managed, we believe that the answer lies with the collaborative machine learning technology of Federated Learning and privacy preservation techniques.

But let’s first have a look at why fraud management needs a different approach in the first place.

Effective fraud management needs to overcome issues of data, privacy and cost

Over years of co-creating data-centric use cases with enterprise customers, we have learned that intelligence is particularly effective and trustable for outcomes when it has three defining qualities: traceable (based on verifiable sources and knowledge bases), timely (served at the right time for decision making) and fresh (generated from a recent collection of events and analysis).

However, 3 key factors limit organizations from using AI for building systems for continuous fraud management. These pertain to quality data, privacy and LLM model training costs.

Reusable, scalable and adaptable AI requires 

A recent survey of 600 data leaders shows that “

” is the top (42%) data-related obstacle to the adoption of generative AI and large language models, followed by data privacy and protection (40%). Additionally, researchers also predict that if current Large Language Model (LLM) development trends for training AI models continue, we may run out of available datasets 

Enterprises are concerned about loss of data privacy and misuse. According to Cisco’s 

 study, data privacy is a critical element and enabler of customer trust. 94% of organizations report that customers would not purchase from them if they did not protect their data properly. There is also a clear understanding that promoting privacy is good business, with 95% of respondents saying benefits exceed costs and the average organization realizing 1.6x return on their investment.

Specialized technologies are required to reduce large language model training costs.

Current estimates highlight that some of the biggest models cost USD100 million to train; the next generation could cost 

USD1 billion and the following iteration USD10 billion

. To make Gen-AI economically viable, the industry is trending towards innovations in specialized technologies, from chips to software and a creative mix of models to reduce processing time and costs.

Collaborative Machine Learning Needed For Better Intelligence

While blockchain and cryptocurrencies have sparked regional interest, regulators in ASEAN remain cautious. Project Inthanon explored the use of blockchain for cross-border payments in 2018, while Singapore’s Payment Services Act (PSA), implemented in 2019, provided regulatory guidance for digital token services. These technologies, along with tokenization for authentication, will be on the rise for building fraud management strategies. However, technologies are still needed to address the primary requirement of better 

For continuous and consistent fraud intelligence, a wide variety of data needs to be processed, analyzed and applied to well-defined problems, then distributed to the right channels at the right time. At Human Managed, our journey of building better intelligence has led us to the emerging groundbreaking solution of federated learning (FL) and privacy preservation techniques that allow models to be trained across decentralized devices while keeping the data localized and secure — this is 

Fraud Management Use Cases For Privacy-First, Collaborative AI

 on federated learning that includes industry expert insights on fraud management. Aloysius Chong Kin Faa, head of fraud & projects at PayNet, shares the intent behind the recently launched National Fraud Portal in Malaysia for fraud response and proactive detection for the financial ecosystem, along with use cases for eKYC and credit risk scoring.

He says, “While we don’t have a federated learning (FL) use case in production today, we are exploring its potential as a more secure and collaborative approach to fraud detection within our ecosystem.”

Centralized Intelligence for Fraud Management

As a primarily B2B organization, PayNet facilitates the exchange of data or intelligence on a sector level for both payments as well as other value-added services (e.g., fraud intelligence sharing) via the newly launched National Fraud Portal (NFP), which was co-designed by Bank Negara Malaysia (BNM), PayNet and other financial institutions to 

strengthen the capabilities of the National Scam Response Centre (NSRC)

The NFP was developed with the objective of facilitating collaboration and data sharing across financial institutions to combat online financial fraud and scams. Today, the system serves as a centralized channel to handle incidents received by the National Scam Response Centre (NSRC) call center and financial institution customer complaint channels. It also utilizes models to trace and intercept victim funds for recovery. This enables a comprehensive and continuous collection of standardized fraud labels for the development of fraud detection models, which is a crucial next step in our strategy to combat fraud alongside our ecosystem players proactively.

AI and ML will be essential as manual processes cannot effectively manage fraud monitoring, detection and investigation at current payment volumes. However, given most contextual customer data reside in silos with the financial institutions, exploring FL applications for collaborative model development with our participants is a potential alternative to centralized data sharing and fraud modeling.

Separately, PayNet has been exploring other value-added services with fintech providers that may involve AI and ML models with FL applications (e.g., alternate credit scoring, digital ID, etc.).

Electronic Know Your Customer (eKYC) Solution

Providers have successfully demonstrated FL in this space, especially in cross-device applications. For example, in an eKYC facial recognition and proofing solution, sensitive biometric data like facial images, videos or fingerprints are not shared directly with the users’ bank. Instead, the model is deployed locally on the users’ device, sending only the model learnings back to the bank’s central system. This ensures the users’ data remains secure in the device. Such use cases are more feasible, given that a single organization can deploy and maintain a common model architecture.

Credit risk scoring is potentially a low-hanging fruit for FL adoption. All banks manage credit risk and aim to minimize bad loans and would stand to benefit from the shared insight on risky or fraudulent loan applications. Additionally, regulatory standards and centralized credit intelligence agency data for customer credit assessments have led to more standardized datasets being used for credit assessments, making feature set convergence more achievable. While individual bank credit assessment models may differ, these are generally less complex models, enabling banks to more easily develop and adopt an FL global model as an alternate credit risk scoring reference to their internal models.

Incentive Structures for Collaborative Fraud Management

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