What Drives Us?

Humanize is built on a core set of beliefs, which has further given rise to our core purpose of ‘helping enterprises go to market fastest’.

Our core belief revolves around the deepening role of technology in building human-centric solutions for business evolution. The history of the technology services industry can be split into 4 phases, broadly –

1. The first phase (the 90s and early 2000s) revolved mostly around skill arbitrage. As enterprises became aware of the power of technology, the focus squarely was on the optimization of operational expenses. The value offered to the clients revolved around flexible staffing strategy, managing skill gaps, and industrializing the process of digitizing systems and processes. This era is the era of Traditional Enterprise
2. The second phase (early 2000s to 2010) was about standardization. The industry still primarily operated based on human skill, but a focus on processes, standard operating procedures, and efficient methods and structures drove value for, what we call a Digital Enterprise.
3. Now comes the era of digitalization – 2011-2020. While the IT industry had gained a foothold in the enterprise, helping them optimize labor and cost,    the enterprises were increasingly asking, “What’s next?” With the massive amount of data being generated, the IT industry focused on developing secure methods for capturing, cleaning, and storing data. Cloud became a critical part of the offering mix of system integrators (given a huge boost by COVID). As a result, some of the work shifted from BPM (Business Process Management) to Cloud and application. The focus was on creating built-in delivery platforms to make IT delivery much more seamless, scalable, and replicable was the approach. Early attempts at RPA and Cognitive Automation were made, but not at scale. This commenced the era of an Automated Enterprise
4. The fourth phase (early 2020s onwards) is the rise of Generative Enterprise.  In this era we anticipate technology and its association with business operations will get seamlessly integrated without any demarcation. Intense collaboration between humans and machines will take place, where machines will perform the work and human acumen will be in the driving seat. Organizational functions will be divided into smaller, software-based components and delivered as software. To build such organizations, technology services will require a synergistic combination of AI-driven advanced solutions, a human empathy-led design that minimizes human intervention, and maximum efficiency – this approach is what we call ‘Services as a Software’

The role of technology is shifting from a traditional ‘CIO’ function to an expanded ‘CIO + CISO + CDO’ scope, and now into a broader ‘CXO’ responsibility. The success or failure of enterprises increasingly depends on leaders’ ability to build teams that utilize technology effectively, aligned with the unique needs of their industries. Enterprises will adopt ‘Services as a Software’ not out of preference, but as a necessity to achieve the fastest time to market—reflecting the essence of our core philosophy.

What Will We Drive?

This brings us to a critical point. How do we use these existing trends to help shape the ‘New Age SI’ that will drive enterprises in the future? Explore our take on the right approach to building a service-as-a-software model.   “Services as a software” is a tech-led IT services business model that uses deep-tech to package software services as a software-driven platform, and eliminates talent/ cost arbitrage. This model minimizes human dependency, maximizes efficiency, and focuses on the outcome by leveraging orchestration human acumen and technology-precision. We drive ‘speed to value’ – We accelerate enterprise growth and time-to-market powered by AI and GenAI-integrated SaaS solutions while simplifying the customer experience through human acumen.

Today, the enterprise SaaS stack caters to business processes at various layers – experience, functional processes, insight, and foundational layer catered by Tier 1 and Tier 2 SaaS vendors. Companies are struggling to optimize their spending, maximize the utility of SaaS investments, and expedite the speed of implementation.

humanize enables enterprises to go to market faster. We do it through our proprietary platform leveraging AI,  GenAI, and Data Science. The platform enables faster deployment of Low-code/ No-code SaaS platforms supported by our transformation advisory across the evolution lifecycle.

humanize has architected a platform with an integrated enterprise SaaS stack powered by AI/ GenAI. The platform will cater to businesses in manifold layers, including experience, functional processes, insight, and foundational levels. It will create a business ecosystem conducive to:

• Making organizations AI-ready through data architecture, management, and governance
• Leverage SaaS to transform business operations for better performance
• Expedite growth through AI/GenAI-powered Industry Solutions

As the industry’s first ‘services as a software’ company, Humanize is set to harness its AI/GenAI-led hyper-automation to expedite time-to-deploy by nearly 20%. It will accomplish this by making design, documentation, testing, code-generation, and customization to zero-touch. Humanize will also help accelerate enterprise evolution with plug-and-play industrial use cases, from concept to reality. We aim to disrupt the traditional IT services industry with our platform-based approach to tech transformation. Our platform integrates enterprise SaaS solutions (CRM, BPM, Data, ERP) with AI to accelerate the development and deployment of tailored industry solutions.

In addition to horizontal SaaS, Industrial SaaS is becoming a new area of growth where business functions such as Aftermarket, Performance Marketing, Warranty Management, Loan processing, etc., are getting automated. We have built the AI-powered SaaS stack with plug-and-play industrial use cases that automate 30% of such business functions.

What Comes Next?

Our mission, therefore, is to reimagine and disrupt current ways of working. While AI alone will not drive the future of technology services, a mix of AI/ GenAI and human intuition most certainly will. We aim to build an AI/GenAI platform to accelerate SaaS implementation, create domain-specific use cases in targeted industries using our AI/GenAI platform and SaaS stack, and tie it all together to become the first of its kind ‘service-as-a-software’ player in an industry poised for major disruption.

Interested in sharing thoughts and perspectives? Write to us at contactus@staging.humanizetech.ai.

We have always told stories as anecdotes to establish a fact and make it interesting. Let me tell you a few stories, which are not meant to establish any logic, but stories that inspired us to design the creatives of humanize.

Story I: What’s in a name?

When my parents named me, it seems there was a lot of discussion—like I assume is true for all families. From my experience, it’s usually an engaged conversation not just for the parents but also within the immediate family. In my case, and like in most Bengali families, it was no different.

I think naming anything is similar; people who believe in the product or company are passionate about the process and often have strong opinions.

I believe that one should be. I’m from the school of thought that a name is not just where things start, but it is also what connects and reminds you of your purpose.

So, when we first started thinking of our business in the world of AI—amid all the rhetoric about job losses due to AI, AI ruling the world, or the ethics of AI—we thought of two key drivers around which we would build our business:

1. Humans and AI will collaborate, not compete, for a more efficient world.
2. Businesses and society will benefit from the efficiency of AI, but will also need human values such as listening, empathy, and right conduct to make businesses sustainable.

This led to the coming together of “human” and “AI” in our name, humanize. We also wanted to ensure that when we write the name, we always use lowercase letters to represent humility—a key aspect and trait that we aim to inculcate in every member of Team humanize.

 Story II: The human angle

In World War II, there were a lot of planes that used to come down due to anti-aircraft fire. Some of them were managing to come back to the air base, but a lot of them were getting lost. So, the Air Force decided to put together a small team of Air Force Officers, Math geniuses, scientists, and engineers in order to create a small consulting team to find out what they could do to protect these planes. What they actually wanted to do was put armor around the plane. The planes could only take that much extra weight, so they had to decide where exactly on the plane to put the armor without affecting the aerodynamic balance. They identified all the bullet holes per square foot across the length of the plane and realized that the tail section had 1.98 bullet holes per square foot whereas the nose section had 1.1 bullet holes per square foot. The Air Force officer said, “We should obviously, put armor at the tail end section as that’s where more holes were”. However, there was a Math genius called Abraham Wald who said quite the opposite. To everyone’s surprise, he said, “We need to put the armor in the nose section of the aircraft because we are measuring planes that have come back. The planes that have a high number of bullet holes in the nose section never survived. We need to protect the nose section.”

This story, while a common one, is of applicability of human acumen bringing in a human-centric solution! Had it been AI, getting the modeling done from the training data it had received (1.98 bullet holes at the tail of the aircraft), it would have recommended building the armor at the tail of the aircraft. Collaboration between humans and machines, complementing each other’s strengths of acceleration and acumen is the philosophy of humanize.

Story III: The circle of evolution

 I am a big fan of sports, and I keep making conscious efforts to bring learnings from the world of sports to my work. This is the story of Novak Djokovic, a star tennis player, whom I first read about on a Billy Oppenheimer blog. It’s the 2nd of June, 2010. The French Open is underway at the Ronald Garros in Paris. Novak Djokovic was seeded No 3 in this tournament and on this day, he was playing in the quarter-final against Jurgen Melzer who was seeded 22. Djokovic was up two sets. In his career by then, Djokovic had been up two sets 49 times and had won all 49. But today was different. He lost! A few days after losing, Novak Djokovic told his coach, Marián Vajda, that he decided to quit playing tennis. Without reacting too much to it, Marian asked Novak a question, “What is the reason you started playing tennis”. Novak replied instantly “I feel happy when I hold that racket in my hand”. While he was uttering those words, the realization which came to Novak and triggered his journey of continuous evolution, changed the course of his life and the history of tennis. He evolved as a player, he expanded his skills on grass and on clay courts, overcame injuries, and took his game to the level of an unmatched level. If I have to take this story into the business context, clients do not use technology just to modernize their IT real estate, they do that to solve a business problem. Also, transformation is not a point-in-time activity, it is continuous, incremental, and seamless, with a common objective to become better – the essence of ‘continuous evolution’ in business.

We at humanize, aim to be the catalyst of the continuous business evolution of clients, and hence the relevance of the infinite loop in our logo.

Story IV: Colors of life

Have you ever wondered why Apple introduced white-coloured earphones? This interesting story demonstrates that going against Social Proof (a term coined by Robert Cialdini in his famous book Influence – The Psychology of Persuasion) can differentiate one as a new entrant. The theory of Social Proof states people assume that if many others are doing something, then it must be correct. When Apple was planning to launch its flagship product earpods, the competitors that existed in the market were the headbands with black colors. So how to stand out and create social proof that is against the norm? Apple came up with the milk-white color AirPods and no other variants. One can notice it from far, crowded streets of Manhattan to a laid-back beach in Maine, Apple AirPods stood out – It was an instant success! When we were internally discussing the construct of the logo, we were quite convinced of the combination of elements expressing ‘AI, Human, Evolution’ in our logo. The question was how to make us stand apart. When we looked at most of our competitors, we found logos were separate from that of the company name, and mostly in monotone or dual-tone. humanize logo is a unique harmony and co-existence of the ‘name and the symbol’ – one flowing to the other. The font used in the logo is ‘Techead’– an innovative sans serif font family. Techead embodies simplicity, and confidence, and is specially designed for technology start-ups focused on developing sustainable and innovative businesses, which perfectly aligns with the purpose of humanize as a company.

Apart from the core team of founders that worked on it I want to thank the marketing team of NLB for helping spread the story so effectively and Buffalo Soldiers for going through multiple iterations and their patience in developing the logo.. humanize is ready to do what it has promised to do and what its name suggests .. human and AI together.

Here’s presenting to you the identity of humanize

humanize is an innovative technology solutions provider specializing in AI and Gen-AI integrated Industrial SaaS solutions. We create industry-centric solutions as plug-and-play platforms built on Enterprise SaaS stacks powered by AI/ GenAI accelerators.

We blend advanced technology with core human values like empathy and collaboration to enhance agility, acumen, and acceleration of business evolution.

Our partner ecosystem and group of companies combined can deliver digital product engineering, analytics, CX, business process services, and talent management solutions across 20+ countries today.

How are leading businesses leveraging AI strategies to outperform their competitors and customer loyalty? 

The answer lies in the power of personalization, the winning formula for getting to know the customer better. All thanks to artificial intelligence, which empowers businesses with diverse tools that help them understand and serve customers better. 

A recent post stated that almost 86% of people believe that AI will have a transformative impact on customer experience. As a result, 71% of leaders intend to increase investment in AI chatbots to enhance their customer services. 

This thriving technology assists businesses in maintaining a long-term relationship with customers by offering them “made for them” services. With the help of AI chatbots and predictive analytics, businesses can now efficiently interact with customers making customers feel supported and valued.

Drawing New Customers with AI

AI helps businesses find new customers in smarter ways. It analyzes vast datasets of customers to understand their preferences such as their likes, dislikes, and more. This helps companies make better ads and run digital campaigns.

AI tools can guess what products people might want to buy. They then make special messages for each person, which makes people more likely to buy. These tools also figure out the best times and places to show ads.

Additionally, AI also helps sort customers into groups. This lets businesses focus on people who are most likely to buy. It can also send personalized emails and social media messages automatically.

Using AI to find new customers helps businesses in two main ways:

1. Their marketing works better.
2. They spend less money to get each new customer.

Keeping Customers Engaged with AI

AI helps businesses maintain customer interest over time. It analyzes customer behavior to understand preferences and habits. This information steers companies towards creating unique personalized experiences for their clients. 

AI-powered systems are smart enough to predict when a customer will lose interest. In such a situation, the systems suggest ways to re-engage with the customers with the help of a database. 

By utilizing these systems, businesses can set up automated communication and send timely messages to customers seeking support. Additionally, AI tools are programmed with a sense of analyzing customer feedback to identify the areas of improvement. 

This is one of the best ways for businesses to identify the gaps between their services and customers, allowing them to improve their products and services. 

Thus, AI is a golden key for companies to make long-lasting relationships with their clients. 

Using AI to Build Customer Loyalty

Now the question comes, how can businesses leverage AI to build customer loyalty? This is one of the crucial questions that many businesses struggle to decode. 

AI-powered systems can predict customer needs before they arise. They recommend products or services that are relevant when customers are looking for them.

It analyzes customer data to understand each customer’s behavior and preferences and eventually, this ability enables businesses to tailor their offerings to each customer.

Additionally, with the help of these advanced systems, businesses can also encourage loyalty programs to enhance customer satisfaction.

By consistently offering custom experiences with the help of AI, businesses foster long-term relationships with their customers.

Overcoming AI Challenges

Implementing AI in customer relationships has its own set of challenges.  First is data privacy. Businesses must be careful about customer information and comply with AI regulations without any failure.

Next, accuracy is crucial. In rare instances, AI may generate erroneous predictions, leading to poor decision-making. Hence, regular testing of AI models should be a fundamental aspect of businesses.

Another challenge is maintaining the human touch. AI may improve customer service, but it sometimes fails to understand human issues fully.

The higher cost is also one of the common barriers that restrict businesses from investing their money. 

Lastly, with the rising speculations of AI replacing the human workforce, sometimes employees resist adopting new technology. At times like these, businesses can conduct training sessions to make their office personnel aware of the crucial role of AI. 

In Closing

AI is unlocking new ways for businesses to serve their customers better. It not only helps businesses discover new customers but also supports retaining them. The key points to remember from this are:

• AI improves customer targeting and personalization
• It enhances customer engagement through timely communication
• AI-powered tools can predict customer needs and preferences
• It helps in creating more effective loyalty programs

AI can solve customer problems quickly, making customers feel valued, happier, and loyal. While adopting this technology, businesses must consider certain challenges such as data breaches and accuracy. However, careful use of this technology can bring various benefits to the table.

The evolving artificial intelligence innovations, often surrounded by apprehensions and controversies, manage to dominate the headlines. In today’s time, where the world is driven toward eco-conscious measures, the demand to adopt innovations and solutions designed to protect the environment is on the surge. AI presents solutions to modern business problems while significantly reducing the organization’s carbon footprint. Sustainable AI is about fundamentally building, deploying, and managing AI technologies in such a way that it aligns with eco-conscious goals.

Real-World AI Innovations Use Cases

In the current modern scenario, with technological advancements at their peak, it is hard to identify any industry that has not been influenced by artificial intelligence (AI). Following are some key practical areas, where AI has played an influential role in promoting a more sustainable approach.

Analyzing Data While Maintaining Privacy

With the growing risk of data breaches, organizations are responsibly incorporating AI models. For analyzing complex data in their businesses by encrypting customers’ data to extract valuable insights for assessing risk and determining fraud. This approach facilitates businesses to operate more efficiently while managing expenditures.

Fighting Air Pollution

Air pollution is a global health issue and causes over seven million premature deaths every year and $8.1 trillion in health damage alone. AI can give real-time warnings from the data derived from air quality monitors, encouraging people to take preventative health measures like staying indoors or wearing masks, when the pollution levels spike.

Alerts for Natural Disasters

By leveraging AI technologies, the risks of climate change and extreme weather events such as forest fires and flooding can be reduced significantly. AI-powered early flood warning systems can save over 3,000 lives and lessen $14m in economic damages by 2030. Also, cameras and sensors attached to drone, satellites can actively monitor forests and detect unusual fire spots.

Advancing Agriculture

A rise in population leads to an increase in demand for food. To cope with the rising demand and consistent growth, AI innovation plays a crucial role. With the help of AI, farmers can monitor weather conditions, giving them an understanding of when to water their crops and the best time to harvest. Also, AI enhances farm management by detecting issues like pest infestations in crops at an early stage to minimize to use of chemicals or medication.

Reducing Defective Production

Forbes reports that about 17 billion items are returned worldwide each year due to defects or customer dissatisfaction. This results in around 4.7 million metric tons of CO2 emissions annually. Reducing returns by just 10% could save enough energy to power about 57,000 U.S. homes annually. AI-powered computer vision systems can help reduce defective products at the manufacturing stage.

Challenges and Considerations While Adopting AI for Sustainability

While sustainable AI offers promising solutions to address modern challenges, it also comes with difficulties. Some of them include:

• Dealing with human rights: Developing AI solutions to ensure human rights, encourage inclusivity and fairness, protect customers’ data, and contribute positively to society.
• Maintaining Data Security and Privacy: Gathering data and analyzing it while safeguarding an individual’s privacy needs detailed consideration to ensure data security and privacy.
• AI Integration Cost: Developing and implementing AI solutions can be costly, which can be a challenge to smaller organizations and developing countries.
• Achieving Energy Efficiency: Energy consumed to operate AI infrastructure can be challenging. Finding the right balance between energy efficiency and AI models can require continuous optimization efforts for sustainability.

To summarize, leveraging AI-driven solutions in an eco-conscious manner can make a significant stride toward a sustainable future. However, it becomes imperative to address the challenges encountered with AI implementation. By adopting ethical practices we can contribute towards the long-term well-being of our planet and its inhabitants.

Enterprise of the Future will be “Data-focused. Insights-aligned. Experimentation-friendly. Purpose-driven.”. In many ways, the working of this enterprise will emulate the human mind, body, and soul. The future enterprise needs to be highly aware and responsive to its surroundings, empathetic to understand stakeholders, agile in its decisions and must consider human values.

The Conundrum

For centuries businesses have evolved by working mainly with human capabilities supported by machines and technologies. Yet, many stakeholders are struggling to create a holistic balance between performance and purpose. This challenge stems from a lack of human-centered solutions, a responsible technology ecosystem, and sustainable tech-led business models. The gaps are acting as an impediment to faster business acceleration, driving a performance-driven culture, and creating a purpose-led organization.

The Gap

If you look at the golden era of the IT services industry, led by India Inc. and outside of India, the predominant business model is cost-arbitrage leveraging a huge talent pool. IT services companies from India leveraged this talent pool to take over large IT operations, delivering them at significantly lower costs without disruption. A few incremental tweaks like standardization, industrialization, offshorization, IT, and business process automation have been used to fine-tune the business model. But essentially, it continued to be a talent/manpower-driven industry. This led to a huge amount of debt in the ecosystem – technology debt, process debt, talent debt- at both ends of Service Consumer and Service Provider. This means changing old habits. It also means changes in the fundamental principles of the business model of Technology Service providers.

A host of erstwhile pathbreaking businesses failed to innovate with the times and were too late to play catch-up games. The continued cycle of providing manpower-based services will yield tepid innovation and, hence, growth. Cost savings and investments in AI initiatives will continuously shrink the margins, reducing their appeal to the talent pool. It will end up being a negative recursive loop of ‘lack of differentiator-commoditization-cost pressure-lack of talent attractiveness’. The manpower-based arbitrage model is fighting for its attractiveness, and most importantly sustainability.

The Perspective

Amidst this business outlook, in the last few years, enterprises have put a higher emphasis on driving agility and business acceleration powered by technology. Cloud-native SaaS platforms have revolutionized how enterprises have been able to respond, and in some cases, create market demands as well. With the advent of AI and GenAI, I saw an opportunity to turbocharge each of these aspects of agility, and acceleration, and augment it with human acumen to create contextualized solutions – build industry use-cases, on industry SaaS stack powered by AI & GenAI.

Therefore, I believe the future of IT services will not be solely about services but rather about services delivered on an IP or platform enabling extreme automation. The future of IT services will not be provided by organizations offering a wide range of services facilitated by talents but will consist of specialized services delivered by talent made smarter with technology. The traditional business model of the IT services industry is at the cusp of disruption. We aim to challenge the status quo of the technology services industry by delivering ‘value’ against ‘volume and manpower,’ ‘services-as-a-product’ instead of ‘economic services,’ and ‘business as a service’ against  ‘servicing a business.’

The Future

Harvard University Prof. Theodore Levitt once famously said, “People don’t want to buy a quarter-inch drill. They want a quarter-inch hole!”

Acceleration

Every industry is going through massive shifts – shifts in how products and services are taken to customers in an engaging and empathetic way. Shifts in how business operations are run in a near-zero-touch manner, and shifts in how technologies are leveraged to create tech-enabled business models. Technology is enabling enterprises to adopt business models that make collaborators, competitors, and consumers co-creators to expand the pie of business from a Product-centric model to a Product+Service centric model. The use of hyper-automation and Generative AI is making UI-driven processes obsolete, and enabling enterprise processes such as Supply Chain, Finance, and HR operations to be zero-touch. The cost of R&D, the emergence of neo-consumers, and competition from new techs (FinTech, HealthTech, etc.) across various industries are compelling companies to relook at their business model, operations, and technology landscape. It is critical to align your technology services closer to business priorities and accelerate your business evolution.

Agility

On the supply side, digital technologies will drive $650-700bn spending in the current decade. While there are many hotspots, the two prime disruptions that are happening around us are Artificial Intelligence and Low-Code-No-Code driven SaaS ERP platforms. AI/ GenAI is more real than ever driving hyper-automation in technology and business operations. According to IDC research, GenAI will disrupt ~54% of operations in the services industry, including IT & BPS services. SaaS-led enablement of enterprise operations, customer service, and business processes will open newer avenues of tech-led business operations, a market growing at a CAGR of ~20%. Leverage technologies that bring enterprises agility and allow products and services to be launched faster.

Acumen

Standing at the cusp of changing market dynamics and technology evolution, enterprises will need a technology partner who demonstrates agility to adopt new technologies, delivers swift tech implementation because of the adoption of AI/GenAI, and most importantly, is empathetic toward client’s strategic objectives while designing the solutions. The shift from Talent to Technology doesn’t take away the need for human acumen. It necessitates experts who can guide their clients along to help them change with the help of technology. Human acumen will be needed to steer the client through the fast-moving technology ecosystems and work with them to create business roadmaps based on AI, GenAI, and emerging tech to make them nimble, smart, and more efficient. A new-age technology partner is expected to bring in the right balance of advisory, technology acumen, domain expertise, and an ability to constantly use new-age technologies for their evolution.

The business model of the traditional IT services industry operates on the principles of manpower-dependent high-volume growth. As we see it, it’s counterintuitive for legacy SIs to cannibalize their existing business and stress the need for AI and automation in the IT services landscape. On the contrary, clients’ focus is to reduce the cost of operations through Cognitive Automation, both in Business and IT operations. We are differentiating ourselves by leveraging the natural conflict of the business model between the Service Consumer and the Service Provider. The differentiation of a New-Age SI lies in faster implementation of technology leveraging AI, and faster transformation of business functions through AI-powered Industrial SaaS use-cases. In both cases, it should act as a catalyst for Speed and Agility for our clients. The New-Age SI is expected to be an antithesis of what the norm of the traditional IT services industry is – volume and manpower.

SaaS 2.0 or “Services-as-a-Software” is more than just a tweak to traditional IT Services delivery models. It is a fundamental shift to address the inefficiencies of repeatable offerings that have plagued the industry for years. In the next 3-5 years, six out of ten business leaders are planning to replace their professional services with some sort of AI services. The reality is, that mundane or predictable routine work performed by junior/middle-level programmers will now be automated. IT services delivery relies on armies of consultants delivering work that takes months of manual effort. By leveraging AI/GenAI, the effort and cost of IT services delivery is going to be productized – smarter, scalable, and faster.

Traditional IT Service Providers: The Case for Disruption

Since its inception, the IT services industry has been modeled around ‘cost arbitrage’ with a high dependency on manpower. The model is not sustainable and acts against the interest of the client – cost-effective, with a lesser workforce, and minimized total cost of ownership. For instance, over 50% of ERP projects face delays due to heavy customizations and resource bottlenecks.These models rely heavily on human effort, making them slow to adapt and costly to scale. Moreover, they often fail to align service delivery with measurable business outcomes, leaving enterprises dissatisfied with ROI.

The demand for change is clear. Enterprises no longer look for vendors for manpower to solve problems, but look for solutions with minimal IT overhead. Enterprises want partners who deliver results at speed, agility, and efficiency. This is where “Services as a Software” enters the picture, a model built to disrupt the inefficiencies of legacy SIs.

What is ‘Services as a Software’?

“Services as a software” is a tech-led IT services business model that uses deep-tech to package software services and eliminates talent/ cost arbitrage. ‘Services-as-a-Software’ uses AI/ GenAI and Low-code/ No-code platforms to hyper-automate technology delivery. This model minimizes human dependency, maximizes efficiency, focuses on outcome by leveraging talent and technology, delivered through a software. It enhances the ability of enterprises to respond to dynamic business needs and expedite time-to-market.

At the heart of this shift are three key enablers:

1. Hyper-automation: AI automates repetitive tasks such as configuration, testing, and deployment, making implementations faster and more accurate.
2. Preconfigured solutions: Industry-specific frameworks allow for plug-and-play implementations tailored to enterprise needs.
3. Outcome-driven models: By aligning services with measurable business outcomes, this approach ensures tangible ROI for clients.

For example, in ERP or CRM deployment, “Services as a Software” can reduce delays and costs by eliminating manual customizations. Instead, AI-powered tools simulate workflows, generate configurations, and deploy solutions with near-zero touch. This marks a significant departure from legacy SIs, where projects could span months and require extensive manual effort.

Why ‘Services as a Software’ Matters

For enterprises, the benefits of “Services as Software” are undeniable. It offers:

• Speed: Achieve faster results by reducing project timelines by 20-40%, which enables a faster go-to-market strategy without compromising on quality.
• Cost Effective: With AI and automation, eliminate up to 30% of unnecessary expenses and streamline operations.
• Scalability: Adapt seamlessly with modular solutions designed to scale as business needs evolve and avoid traditional models.

Humanize’s approach exemplifies this transformation. By combining AI-driven platforms like humanAIze™ with a humanized focus on client outcomes, the company is redefining what service delivery can achieve. Clients no longer need to navigate the inefficiencies of legacy SIs. Instead, they can partner with an AI-first service provider that prioritizes agility, innovation, and measurable results.

The Future of Service Delivery

“Services as a Software” represents the next chapter in the evolution of IT services, leaving behind the inefficiencies of legacy SIs. It is a model designed for the modern enterprise, one that values speed, scalability, and outcomes over outdated manpower-driven paradigms. With AI and automation at its core, this approach empowers businesses to achieve transformation faster and more effectively than ever before.

At humanize, we are proud to lead this charge. By integrating cutting-edge AI platforms with a commitment to human values, we help enterprises unlock the true potential of “Services as a Software.” The future of service delivery is here. Are you ready to embrace it?

Global Capability Centers (GCCs) have long been the operational backbone for multinational corporations, managing core functions like IT, finance, HR, and customer service. However, the pace of technological advancement demands a new era of innovation for these centers. Enter AI agentification – the deployment of intelligent, autonomous agents that can perform complex tasks with minimal human oversight. This shift promises to reshape the future of GCCs, transitioning them from reactive service providers to proactive value creators.

The Rise of Autonomous Agents in GCCs

AI agentification is a big step forward from the usual automation we’re used to. Instead of just following a set of rules, these autonomous agents use machine learning and artificial intelligence to learn and improve at making decisions independently. They can handle various tasks, whether helping customers, managing finances, overseeing supply chains, or supporting IT services. This flexibility allows them to be effective in many different fields. 

For instance, an AI-powered financial operations agent might detect discrepancies in invoicing, communicate directly with vendors to resolve issues and escalate only the most critical cases to human supervisors. Similarly, in IT operations, agents can monitor systems 24/7, identifying and resolving potential outages before they escalate. This capability ensures reduced business downtime along with operational continuity. 

Operational Benefits of AI Agentification

The primary advantage of AI agentification lies in its ability to enhance efficiency and scalability simultaneously. Traditional GCCs often face bottlenecks due to their reliance on manual processes, which scale linearly with headcount. AI agents break this dependency by enabling exponential scalability without proportional cost increases.

Studies have shown that autonomous agents can reduce operational costs by 30-40% while improving task accuracy by over 90%. With AI agentification, organizations can execute repetitive tasks more efficiently and accurately by eliminating manual errors by humans, and time-zone differences.

In addition to cost and time efficiencies, AI agents empower better decision-making. For example, in HR operations, agents can analyze employee sentiment and productivity data to recommend tailored interventions, boosting retention and engagement. 

Challenges in Implementation

Despite its transformative potential, AI agentification presents several challenges. The first is data security and privacy. Autonomous agents require access to sensitive organizational data to function effectively. Ensuring this data remains secure, especially in industries with stringent compliance requirements, is critical.

The second challenge lies in integrating AI agents with legacy systems. Many GCCs operate on outdated IT infrastructures that lack the flexibility needed to support advanced AI deployments. Retrofitting these systems or migrating to more modern platforms can be resource-intensive.

Finally, there is the human factor. Introducing AI agents often sparks resistance among employees, who may fear redundancy or lack trust in the technology. Addressing these concerns through transparent communication and upskilling programs is essential to ensure smooth adoption.

The Path Forward

Organizations must adopt a structured approach to AI agentification to overcome these challenges. This begins with conducting a thorough feasibility assessment to identify processes that can benefit most from automation. Following this, GCCs should prioritize building a robust data governance framework to ensure data security and compliance. Partnerships with technology providers can also play a crucial role. Platforms that offer modular, pre-built AI solutions tailored to specific use cases can significantly reduce deployment timelines and costs. For example, platforms with built-in capabilities for automating invoice processing, IT monitoring, or customer support workflows allow GCCs to achieve quick wins while gradually scaling up AI adoption.

AI Agentification in Action

Consider a GCC supporting a global e-commerce giant. By deploying AI agents in its supply chain operations, the GCC could use predictive analytics to anticipate stock shortages, automate replenishment orders, and optimize delivery routes. These improvements could reduce operational costs by up to 20%, while improving customer satisfaction with faster and more trustworthy deliveries.

Another example comes from the healthcare sector. GCCs managing patient data and scheduling can leverage AI agents to streamline appointment bookings, automate billing processes, and analyze patient feedback for continuous improvement. The result is a more efficient healthcare system that benefits both providers and patients.

Conclusion

AI agentification is not just a technological upgrade for GCCs; it represents a paradigm shift in how these centers operate and deliver value. By integrating autonomous agents, GCCs can achieve unparalleled levels of efficiency, scalability, and innovation. The benefits extend beyond cost savings to include enhanced decision-making, better customer experiences, and stronger strategic contributions to the parent organization.

While challenges such as data security, legacy system integration, and employee resistance remain, they are far outweighed by the opportunities AI agentification presents. As organizations embrace this transformation, GCCs will transition from being cost-focused support centers to become indispensable engines of growth and innovation.

The question is no longer whether GCCs will adopt AI agentification, but how quickly they can harness its potential to stay competitive in a rapidly evolving business landscape. For GCC leaders, the time to act is now because the future belongs to those who empower their operations with intelligent agents.

Global Capability Centers (GCCs) are embracing the SaaS revolution, leveraging software-as-a-service platforms to enhance operational agility, scalability, and efficiency. As businesses demand faster innovation and reduced time-to-market, SaaS has emerged as the cornerstone of GCC operations. The road ahead for SaaS-driven GCCs is defined by emerging trends, transformative technologies, and strategic opportunities that promise to reshape their value proposition.

Emerging Trends in SaaS-Driven GCCs

The next phase of SaaS evolution in GCCs is marked by three key trends:

1. Vertical SaaS Solutions

It presents a unique opportunity to diverse industries by offering an ecosystem of solutions tailored to specific industry requirements. This enables businesses to understand and integrate into customer’s workflows, building loyalty that’s hard to shake. It also opens doors for upselling and cross-selling. Also, with AI as a catalyst, vertical SaaS unlocks new opportunities in innovation and operational efficiency.

2. Integration of GenAI

Generative AI (GenAI) in SaaS is not just a technology trend but it’s a highly effective tool that can make the software more functional and appealing. It enables advanced AI models capable of creating images, including text, new content, music, code, language translation, data augmentation, etc. With the help of prompt, it empowers businesses with more reliable outcomes.

3. Platform Ecosystems

This ecosystem refers to an intricate network of interconnected players, which includes integrators, software vendors, third-party service providers, and customers that can create and deliver software as a service. It acts like a core platform with complementary apps and the product is built on it.

The Benefits of SaaS-Driven GCCs

With SaaS-driven offerings, GCCs can gain several strategic advantages:

Flexibility

The SaaS model stands out due to flexibility, they can be easily customized without changing the specific needs of the business. This allows GCCs to adapt to shifting business dynamics, adding or removing features as required. 

Scalability

As SaaS systems are based on the cloud, they can be easily integrated into other similar systems, making them scalable. GCCs can expand their capabilities without expanding their infrastructure or manpower.

Better Collaboration

Cloud-based workflows enabled by SaaS models improve collaboration across different geographies, and departments, streamlining projects, and making GCCs more agile and responsive.  

Cost Effective

SaaS can significantly reduce operational costs. It operates on a subscription model, reducing upfront costs for hardware and software licenses.

Challenges on the Path Forward

While SaaS-driven GCCs hold great promise, a few hurdles need attention:

1. Legacy Systems

Many GCCs still rely on traditional IT setups that don’t align well with modern SaaS platforms. Transitioning to cloud-based systems or updating existing infrastructure takes careful planning and resources.

2. Upskilling Talent

As digital technology revolutionizes, the team needs to be updated with the technologies. The team must be familiar with areas like AI, data analytics, and cybersecurity. Upskilling employees becomes a hurdle for GCCs

3. Data Security and Compliance

Handling sensitive data in SaaS platforms makes GCCs vulnerable to cyber threats.

Overcoming Challenges

To overcome these issues, GCCs can:

1. Embrace Modular Platforms

Modular SaaS solutions work well with existing systems and allow gradual improvements, minimizing disruptions.

2. Partner with Technology Experts

Collaborating with SaaS providers gives GCCs access to the latest tools and expert guidance, supporting a smooth transition.

3. Strengthen Data Governance

Clear frameworks ensure data security and compliance with regulations.

4. Employee Training

To help teams stay in touch with the current new technologies and tools, focus on upskilling.

By tackling these challenges strategically, GCCs can unlock the full potential of SaaS-driven operations.

The Future of SaaS-Driven GCCs

As SaaS platforms continue to evolve, GCCs must stay ahead by embracing trends like vertical SaaS and GenAI. These technologies will redefine how GCCs operate, enabling them to deliver tailored solutions with unparalleled efficiency. Moreover, the rise of platform ecosystems will foster greater collaboration and innovation, transforming GCCs into key drivers of enterprise success.

Conclusion

The road ahead for SaaS-driven GCCs is full of exciting opportunities backed by cost-effective and efficient software solutions. As the market evolves, the shifting dynamics bring both challenges and opportunities. As organizations look to the future, SaaS is no longer a luxury but a necessity. For GCC leaders, the time to embrace this transformation is now. The question is not whether to adopt SaaS, but how to do so effectively to remain competitive in an ever-changing business landscape.

Imagine a world…

It’s 6:42 a.m. when Judy stirs awake — not to the blare of an alarm, but to the calm, empathetic voice of Katy, her AI-powered personal assistant. “Good morning, Judy. The CEO has called an 8 a.m. strategy meet as prep for the board session later today. I’ve reshuffled your schedule, alerted your team, and compiled the initial reports. You’ve got this.” Still half-asleep, Judy processes the urgency. She wasn’t expecting this. Reports, projections, insights — all needed in under 90 minutes. But in this world, she’s never alone. She mutters, “Ping Jeff. Tell him to prioritize growth projections and open deals. I need a crisp three-page summary in my inbox before 7:30.” “Already done,” replies Katy. “Jeff’s on it — ETA 7:25. Also, I’ve scheduled your ride — a Level 5 autonomous pod from AeroCruze. It’ll be at your door in seven.” As Judy freshens up, her smart mirror displays snippets of the day’s agenda, trending market dynamics, and even nudges her on key talking points for the boardroom. Meanwhile, her biometric health tracker notes she skipped dinner last night, so Katy’s already placed her usual breakfast — an Earl Grey with wildflower honey and a toasted bagel with cream cheese — ready for pickup at her favourite café on route. Sliding into the self-driving car, she reviews the first draft of Jeff’s report, automatically annotated by her analytics bot for clarity and completeness. At a red light, the windshield display shows a visual forecast of upcoming growth sectors, simulated in real-time. At the office, she’s greeted by Adam, the AI concierge. Not just a navigation bot, Adam syncs her mental model — sensing her slight anxiety, he dims the corridor lights, plays her focus playlist, and guides her directly to the boardroom. Inside, everyone’s settling in. Screens auto-adjust to her preferences. The CEO looks up, “Judy, glad you could make it. What’ve you got for us?” Three hours later, the room buzzes with applause. Her analysis not only answered critical questions — it sparked new possibilities. “Brilliant work, Judy,” says the CEO. “How did you pull this off so fast?” She smiles. “Let’s just say, I had help.” What’s not common here? Katy, Jeff, and Adam — they aren’t colleagues. They’re not even human. They’re AI agents, hyper-personalized to Judy’s life. They anticipate. They decide. They act — in harmony with her needs, habits, and goals. They don’t just follow commands; they understand context. They evolve with her. In this world, time is never wasted on the mundane. Energy is never drained by unpredictability. Every moment is personalized — every decision, accelerated. Welcome to the age of Intelligent Companionship — where humans lead, and AI empowers. In 2025, personalization has evolved from a marketing tactic to a foundational business strategy. At the intersection of AI, data science, and customer experience lies a new paradigm — hyper-personalization. This approach empowers businesses to create unique brand universes for each individual, driven not just by data but by empathy, ethics and meaningful engagement.

Emerging Trends

Advancements in artificial intelligence and predictive analytics are enabling organizations to gain granular insights into consumer behaviour—purchase history, preferences, browsing patterns, and beyond. According to Gartner, hyper-personalized strategies are poised to increase digital commerce profits by 15% annually through 2026.

What sets this era apart is not just the depth of personalization, but the demand for responsible innovation. Consumers are increasingly aware of how their data is used, and brands are expected to balance technological sophistication with ethical clarity.

Benefits

• 3x HigherEngagement: Tailored experiences translate into deeper customer interactions, as seen in multiple industry use cases. Increased
• Conversion Rates: Personalized journeys lead to better decision-making and more efficient sales funnels.
• Customer Loyalty: Thoughtful personalization fosters emotional connection, turning customers into advocates.
• Operational Efficiency: AI-driven automation accelerates time-to-market and reduces human error in campaign execution.

Challenges

However, this evolution doesn’t come without complexity:

• Data Privacy Concerns: With 84% of consumers concerned about how their data is handled (PwC), trust is fragile.
• Regulatory Compliance: Adhering to frameworks like GDPR and CCPA is no longer optional—it’s a brand mandate.
• Transparency in Algorithms: Customers demand clarity on how their data feeds into AI decisions.
• Data Silos and Readiness: Fragmented data can inhibit the effectiveness of personalization at scale.

Industry Examples of Ethical Hyper-Personalization Leading organizations across sectors are showcasing the power of ethical and responsible hyper-personalization:

• Financial Services: A major European bank deployed a responsible AI personalization engine that led to a 3x engagement lift, a 52% increase in click-through rates, and a 100% pass rate in regulatory audits — all within the first 90 days of deployment. The system was built with explainability at its core using SHAP values to ensure model transparency for both regulators and internal teams.
• Retail: A global e-commerce platform implemented consent-based personalization, allowing users to control the extent of personalization through a dynamic privacy dashboard. This not only improved trust scores but also increased repeat purchases by 37%.
• Manufacturing: A smart factory integrated predictive maintenance recommendations using Explainable AI models, enabling plant managers to understand why certain interventions were suggested. It also embedded privacy-by-design principles, ensuring minimal use of identifiable operator data, aligning with both GDPR and internal ethics policies.

Overcoming Challenges

To unlock the potential of hyper-personalization, organizations must balance technological capability with ethical responsibility. It’s not just about smarter systems, it’s about building AI that is fair, explainable, and aligned with human values.

• AI Accelerators with Responsible Design – Modular, plug-and-play AI components can speed up deployment and reduce operational costs. However, they must include fairness constraints, bias detection, and ethical performance benchmarks. Tools like IBM’s AI Fairness 360 or Microsoft’s Fairlearn can help ensure outputs don’t disproportionately impact protected groups.
• Unified Data Lineage with Ethical Oversight – Strong data governance is essential. Clear lineage, from ingestion to insight combined with consent tracking, sensitivity tagging, and retention policies ensures transparency and accountability without compromising user rights.
• Compliance-First, Ethics-Driven Frameworks – Privacy should be built-in, not bolted on. Approaches like Privacy by Design, Data Minimization, and Federated Learning reduce risk while respecting user autonomy. Compliance with GDPR, CCPA, and OECD principles is the baseline, leaders go further by embedding ethics into every layer of personalization.
• Algorithmic Transparency and Accountability – Explainable AI matters. Tools like SHAP, LIME, and counterfactual analysis help clarify how decisions are made. Supporting artifacts like model cards, data sheets, and risk assessments make outcomes more interpretable for both users and regulators.
• Human-Centric, Value-Aligned AI – Personalization should empower, not manipulate. Blending behavioural science with machine learning helps create emotionally resonant experiences. Value alignment techniques ensure AI systems reflect user intent, cultural context, and ethical norms.

The Future

As we look ahead, the future of customer engagement lies in harmonizing AI precision with human creativity. The brands that will thrive are those that:

• Prioritize transparency over opacity
• Design experiences with ethics embedded in the code
• Invest in technologies that are transformative yet trustworthy

At humanize, our vision aligns with this future—powering personalization that doesn’t just connect, but respects, understands and empowers.