The ‘P’ Playbook Part 3: 10 Core Principles of IT Platforms Management for Tech Leaders in the AI Era

MAITA (My AI Tech Assistant) by Romeo Siquijor

Artificial intelligence has come a long way since it was coined by John McCarthy at Dartmouth College during the summer of 1956.  Since then, scientists all over the world have been trying to build machines that mimic human cognitive functions including, but not limited to talking, hearing, seeing, analyzing, problem-solving, and even imagining.

This year, at the Gartner Symposium—arguably the largest tech conference in the world—focused entirely on AI. Nearly every presentation and conversation among tech and business leaders at the conference revolved around how to leverage and apply AI in mainstream industries. In this chapter of the ‘P’ Playbook on Platforms Management, I will focus on the tools and technologies that will pave the way for a smarter future and reshape how we live, work, and interact.

As a bonus, I’ve created MAITA (My AI Tech Assistant), to demonstrate how end-users and IT leaders can benefit from a simple online polyfunctional agentic IT support chatbot.  She (MAITA identifies as she/her) is embedded in this article so feel free to play with her. For example, you can try to ask MAITA, "What is the P Playbook?"

Note that the following are the top 10 Platforms and Technologies that I’m betting on and bullish with.  This is purely my opinion, so if there’s something that you believe I missed, please feel free to comment below.

1. Prompt Engineering: Ask (correctly) and you shall receive (the answers correctly)

With Gen AI (Generative Artificial Intelligence) and LLM (Large Language Models), the key to success often boils down to asking the right question the right way. Technically, this is known as prompt engineering.  This skill is critical for getting the best results from AI models like GPT-4. A poorly crafted prompt can lead to vague or useless answers from hallucinations, while a precise prompt can unlock AI’s full creative and generative potential.  Mastering prompt engineering is the art and science of guiding AI towards better, more relevant answers. It’s the skill that can turn anyone into an effective AI intercessor—without any coding expertise required.

Prompt engineering empowers anyone—not just developers.  To prove this point, I’ve created MAITA by simply instructing a GPT (Generative Pre-trained Transformer) prompter with a specific goal and asking it to guide me to create a virtual IT tech assistant.  Then, I fed it with some information mostly from my blog, other tech blogs, and some pre-filtered datasets and information—and voila! —I have my own tech support GPT. I intend to continue developing MAITA to help me and my team capture insights about the technical issues that our users are experiencing, with the intention to be more proactive and strategic, rather than reactive.

2. Proactive Agentic AI: Gather, process, predict, act, repeat

Introducing the latest tech buzzword: Agentic AI. 

The term “Agentic” was widely used during the Gartner Symposium and no doubt that IT folks will talk about it for a long time.  Simply put, proactive agentic AI automation tools anticipate a future state using predictive analytics, then taking actions without any human intervention. These Agentic tools can enhance productivity and will enable systems to handle complex tasks autonomously, making them essential in a world where speed, efficiency, and accurate foresight are critical.  

Imagine a system that monitors machine’s performance, predicts breakdowns, orders parts, and schedules repairs automatically. That’s Agentic AI at work.  Agentic systems will play a central role in automating workflows, enhancing customer experience, and enabling data-driven decision-making at unprecedented speed and scale. This proactive capability will be essential for businesses to stay competitive in an increasingly fast-paced and highly automated world.  

I intend to make MAITA an agentic bot connected to open-source tools, automation workflows, and Open AI's GPT-4. Eventually she can take decisions based on datasets, feedback, and models I’ll be feeding her with.  For example, I want MAITA to handle approvals and trigger actions for software request including its installation.  This is a simple process but is a very high-touch, highly bureaucratic process that triggers a lot of frustrations on the part of the end users.  Having MAITA handle this process, from seeking approvals, up to the installation of the software will alleviate a lot of the frustrations from the end users, reduce several teams’ workload, improve IT service delivery time, and even save money.

3. Polyfunctional Bots: The bot that will rule them all

Single-task bots are so last season. Enter polyfunctional bots, which adapt to users’ needs and handle multiple tasks simultaneously and seamlessly. These bots can manage your calendar, send reminders, track deadlines, build presentations, and even provide tech support—an all-in-one productivity tool.

Polyfunctional bots will save time and resources by streamlining workflows.  For example, I intend MAITA not just to help users to create a service ticket, but rather take actions to resolve their issues, track them, and report what can be done to avoid future recurrence.  MAITA can be programmed to monitor a database, so when it is saturated, because of excessive workload, she can either open a service request to increase the resources of the database or trigger the actions herself.  Then, the only thing that the human system administrators will have to do is to validate, audit, and take actions that MAITA cannot do, like physically installing a piece hardware.

4. Pervasive Wireless Communication:  The artery of the Internet of Everything (IoE)

AI applications require constant and seamless connectivity to work at its full potential. As such, pervasive wireless systems will be required to stream datasets from different sources, such as sensors, human inputs, and the Internet of Interconnected Things. Whether it’s IoT devices in factories or wearables on your wrist, wireless communication will allow AI to function continuously and provide digital synapse feedback to its core cognitive system in the cloud. As Nikola Tesla predicted back in 1926: “When wireless is perfectly applied, the whole earth will be converted into a huge brain.”

Wireless communication will be the artery of any AI ecosystem, enabling real-time data interchange and uninterrupted operation between machines, people, processes, and things.  While MAITA can run on any computing platforms, from browsers, phones/tablets, and any computers—it requires connectivity to connect to its digital central nervous system. 

5. Preemptive and Self-Healing Systems: Success is inevitable when failure is not an option

Organizations can avoid the high costs of outages, data loss, or equipment failure with self-healing technologies. AI-driven infrastructure and systems will be able to anticipate potential issues, then autonomously resolve them before they escalate, ensuring minimal disruption to operations and avoiding passing the ball to different escalation layers.  These systems shift focus from reactive problem-solving to proactive and automatic maintenance workflows.  This will be critical as organizations and infrastructures become more reliant on complex, interconnected smart technologies.  

For example, a self-healing cloud infrastructure can automatically detect server overloads or memory leaks. Then, it can automatically reassign workloads, spin up additional virtual machines, patch bugs, and test/certify that the applications are working correctly—without waiting for any human interventions.

Another example is a self-healing network system that detects signal or bandwidth degradation in a particular site. This self-healing network reconfigures traffic routing or activates new tunnels to maintain uninterrupted communication. 

In cybersecurity, AI-protection systems can detect unusual activity in a network that may indicate a potential attack.  It can isolate the affected nodes, block suspicious traffic, and apply patches automatically to prevent the spread of malware.  This can prevent costly breaches and mitigate cyberattacks in real time, without the need for manual oversight.  

In the future, these self-healing AI-based ecosystems will become foundational in sectors such as IT services, telecommunications, healthcare, manufacturing, among other industries.  It will guarantee robust, reliable, and self-sustaining systems.  By resolving issues in real-time, these systems will keep operations running smoothly while freeing up resources for more strategic tasks, rather than endless firefighting.

6. Predictive and Prescriptive ML Models: The AI-based digital crystal ball

Predictive AI models tell us what’s likely to happen, while prescriptive models go further by recommending what to do about it. In supply chain management, for example, predictive AI can forecast demand spikes, depending on seasonality insights, while prescriptive AI can suggest optimal stock levels and distribution plans to avoid either stockouts or overstocks.

Predictive and prescriptive models are essential for strategic decision-making, helping supply chain leaders act based on data-backed forecasts.  It is like having a digital crystal ball that uses data-driven analysis and statistical models, powered by AI.

Applying this model in IT Project Management, I intend to use MAITA to help in resource planning to forecast how many FTEs and how much time is needed to complete a specific project.  It can tell me the status of the project, the cause of the delays (if any), and suggest corrective actions on how to recover from the backlog.  As a result, plans will be more accurate, delays preempted, less execution frictions, and projects are delivered on time and on-budget.

7. Perceptive Smart Sensors: The Power to See the Invisible

Perceptive Smart Sensors combine advanced industrial sensing technology with data analytics to monitor, measure, and analyze things in real time. This innovation delivers unparalleled visibility into factors that were once hidden or difficult to quantify. By deploying networks of smart sensors, industries can capture vast amounts of data on machine performance, energy consumption, environmental conditions, emissions, among other things.  This constant stream of information powers AI systems to optimize operations, reduce waste, prevent costly downtime or fines, and maximize profits.

In agriculture, for instance, sensors can monitor soil health and crop conditions, enabling AI to make precise adjustments to irrigation and fertilizer use for maximum yield and efficiency.

Through the lens of AI, we now possess the "power to see the invisible"—detecting and measuring previously imperceptible elements such as CO2 and greenhouse gases. This capability enables organizations to assess emission levels, identify inefficiencies, and implement data-driven strategies to optimize operations, improve safety, and significantly reduce environmental impact.

Smart Cities are poised to harness this transformative technology to monitor air quality, optimize energy usage, manage traffic flows, and enhance public safety. By integrating IIoT sensors and AI, cities can create sustainable, efficient, and more livable urban environments.

The equation is simple yet revolutionary: IIoT Sensors + AI = Smarter Industries. This synergy can unlock new levels of efficiency and innovation across industries, from smart manufacturing, to smart farming, to smart cities. With Perceptive Smart Sensors, the once invisible becomes visible, empowering efficiency and sustainability like never before.

8. Peer-to-Peer and Blockchain Platforms in Tandem with AI: The road to a decentralized smart computing future

Peer-to-peer (P2P) networks and Blockchain platforms provide computing solutions that are transparent and secure—without relying on a central authority.  This is the technology behind bitcoin and cryptocurrencies, but their application is vast.  Some of the top use cases where this trio will thrive include:

• Decentralized Finance (DeFi): Blockchain enables trustless financial ecosystems, allowing users to access financial services like lending, trading, and payments without intermediaries, increasing accessibility and reducing costs.

  
  

• Borderless Supply Chain Transparency: Blockchain and P2P networks enable end-to-end supply chain transparency by providing a tamper-proof ledger for tracking goods and automating processes like payments and inventory management with smart contracts. AI can enhance these systems by optimizing performance, identifying bottlenecks, and using data—such as temperature logs for perishables to predict spoilage, reroute shipments, reduce waste, and improve public safety.

  
  

• Decentralized Identity Management: Blockchain can also enable self-sovereign identities, to empower users to control their personal data securely, enabling seamless, direct verification and authentication without the reliance on centralized authorities.  This extends from club memberships, access to concerts or sporting events, passports, visas, voter's ID, among many other use cases that require stricter identity verification.

9. Pseudo-Worlds and Metaverses:  The Next Breakthrough After the Internet

The metaverse is a concept of pseudo or virtual worlds that envisions a shared, immersive, persistent, 3D digital space where individuals can experience life in ways that may not be possible in the physical world. It is often described as the next iteration of the internet, offering a digital world that exists parallel to the real world. Pseudo-worlds and metaverses offer AI safe spaces to test real-world scenarios. From flight simulators, digital twins of shop floors, VR-based e-commerce, to virtual city replicas.  These digital environments allow for experimentation without real-world risks. Training autonomous vehicles in a virtual world, for instance, can accelerate the development of the technology and laws to govern and put guard rails around it without risking road safety.

According to McKinsey & Company, the Metaverse is projected to significantly impact e-commerce, potentially generating a market influence between $2 trillion and $2.6 trillion by 2030.  Different companies and experts have varying perspectives on what the metaverse entails. For example, Meta (formerly Facebook) envisions it as the next evolution in social connection and the successor to the mobile internet, while Microsoft sees it to potentially evolve virtual meeting rooms for training new hires or chatting with remote coworkers. Metaverses and pseudo-worlds will open doors for many innovations, allowing AI to be tested and refined in ways real-world conditions does currently permit either because of laws or the laws of physics.

10. Prepare for Quantum Computing: The engine that will drive the next wave of innovation

Quantum computing is a type of computing that harnesses the unique properties of quantum mechanics to perform certain calculations much faster than classical computers. Classical computers process information in bits that are either 0 or 1, while quantum computers use quantum bits (qubits), which can represent both 0 and 1 simultaneously due to a property called superposition.  It leverages entanglement and quantum interference to solve specific problems exponentially faster, with potential applications in cryptography, genomics, complex process optimization, scientific simulations, AI modeling, and many other fields.

As quantum technologies continue to develop, its mainstream adoption will transform AI capabilities, driving innovation and efficiency across different industries.  Quantum-enhanced machine learning algorithms can process large datasets more efficiently, enabling faster insights and more sophisticated models. As a result, it can improve the performance of AI systems in various applications, from image recognition to natural language processing.  As quantum computing continues to mature, its synergy with AI will unlock new levels of efficiency, solving problems once thought impossible.

Conclusion

In a few decades, technologies that once seemed like science fiction—flying cars, robotic household assistants, metaverse-based universities, holograms, borderless supply chains, drone deliveries, driverless transport systems, and crypto-based payments—will become everyday realities. The rapid evolution of AI and the technologies outlined in this chapter are set to accelerate these transformations, reshaping how we live, work, and interact. Yet, the true impact of these innovations will ultimately depend on how wisely and ethically we choose to use them.

Many of us struggle with neophobia—an irrational fear or aversion to the unfamiliar and new. This has historical roots, stretching back to Mary Shelley's Frankenstein (1818), a story that shaped early fears of a “buggy cyborg.” In the novel, the creature—initially docile and curious, became destructive out of loneliness and frustration, sparking fears that still resonate in modern concerns about AI. However, it is important to remember that emotions like love, frustration, and recognition are uniquely human attributes and do not inherently apply to algorithmic beings.

Humans are driven by emotions—which sometimes lead us to make irrational decisions (or what we can call as "natural stupidity”—the opposite of “artificial intelligence”). AI does not possess emotional volatility, as it operates purely on logic, math, and algorithms.

While emotions can be our weakness, it is what makes us human, adding depth, meaning, and purpose to life.