Executive Perspective for CIOs and Business Leaders

The AI Surge and the Scaling Gap

Artificial Intelligence has become the centerpiece of enterprise strategy. CEOs are pushing aggressively to “go AI” across functions, and budgets are shifting fast. Gartner forecasted that worldwide generative AI spending will reach $644 billion in 2025, while IDC projected global AI investments will grow to over $500 billion by 2027.

Yet value realization remains significantly behind expectations. Only 14% of enterprises have managed to scale AI beyond isolated pilots. The challenge isn’t the maturity of AI technology; it’s the maturity of the enterprise environment into which AI is deployed.

Organizations are chasing the want of AI: generative models, autonomous reasoning, synthetic data pipelines, edge accelerators, and self-learning orchestration layers; while neglecting the need for Intelligent Automation: workflow optimization, process re-engineering, rules-based automation, hybrid RPA, system interoperability, and predictable operational scaling.

The hard truth CIOs must confront is this: before enterprises can truly harness AI, they must first master Intelligent Automation.

AI Value Realization Won’t Happen on Top of Operational Chaos

Across industries, CIO roundtables and transformation assessments reveal a consistent pattern: enterprises are deploying AI into operations that aren’t ready for it.
High process variability, manual workflows, fragmented integration layers, poor data trust, and limited telemetry all undermine AI’s effectiveness. When AI is introduced into inconsistent systems and non-standardized processes, outputs become unreliable, governance becomes harder, and scaling becomes nearly impossible. AI cannot compensate for operational fragmentation. Automation can.

Why Automation Must Precede AI

Most enterprises are still far from ready for AI at scale. Deloitte’s 2025 Workflow Automation Outlook highlights that 73% of enterprises have not reached mid-level automation maturity. McKinsey’s research shows that over 60% of AI’s economic potential depends on process optimization. AI may be powerful at reasoning and prediction, but it cannot deliver results without clean data, interoperable systems, stable workflows, measurable outcomes, and real-time connectivity. These foundations are created not by AI, but by Intelligent Automation.

Automation is the real force multiplier: it cuts cycle times by up to 80%, drives near-perfect accuracy, doubles, or quadruples throughput without adding staff, and reduces operating costs by as much as half. Once automation strips away friction and standardizes processes, AI can finally be layered on top to generate intelligence over predictable, measurable operations.

For CIOs, the message is clear: automation maturity is the prerequisite for AI value realization.

AI-Ready vs. AI-First: The Leadership Distinction

Many enterprises rush into an AI-first approach: pilots, use cases, and model experimentation, only to stall when scaling. The organizations that succeed are AI-ready: they invest first in the foundations.

Automated end-to-end workflows, governed data pipelines, API-driven integration, standardized processes across regions, and a clear governance model create the conditions for AI to deliver value. This readiness accelerates time to impact and minimizes risk, ensuring that when AI initiatives expand, they do so on solid ground.

The Enterprise Intelligence Stack

CIOs who succeed in scaling AI follow a layered maturity path. The journey begins with process standardization to ensure consistency across the enterprise. Intelligent automation then removes friction and drives efficiency.

Once workflows are automated, data engineering and governance provide the clean, reliable pipelines that analytics depend on. With trusted data in place, organizations can generate meaningful insights, creating the conditions for artificial intelligence to deliver transformative value.

Skipping these layers may create the illusion of progress, but it inevitably leads to operational bottlenecks when AI initiatives attempt to scale.

What CIOs Should Prioritize Over the Next 12–18 Months

To unlock AI’s full potential, CIOs must first focus on automation maturity, stabilizing high-variance workflows, and digitizing end-to-end processes to create a reliable operational backbone.

Modernizing integration architecture is equally critical. AI is only as effective as the systems it can “talk to,” making API-led connectivity, low-code orchestration, and event-driven design essential.

Building enterprise data trust is non-negotiable. Without quality, lineage, and governance, AI outcomes deteriorate rapidly. CIOs must also shift from chasing AI use cases to prioritizing business value cases, those tied directly to customer experience, cost, risk, or revenue.

Finally, establishing an AI operating model with cross-functional pods, federated decision-making, and transparent ROI governance helps reduce pilot fatigue and accelerates enterprise adoption.

Closing Thought: CIO Leadership Requires Sequencing, Not Speed

AI represents ambition, but Intelligent Automation represents readiness. The CIO’s role is not to slow innovation; it is to sequence it correctly.

AI is the destination. Automation is the road that gets the enterprise there safely, at scale, and with measurable value. CIOs who invest in automation-first strategies will unlock the full economic and operational potential of AI, while reducing risk, improving efficiency, and building an enterprise truly prepared for the next decade of intelligence-driven transformation.

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Author:

Kishore Kamarajugadda,
VP-Enterprise Architect

Artificial Intelligence has ceased to be merely a familiar technological advancement of the future. Instead, AI has become the fundamental tool that enables vital changes within an organization, from the operators to the final decision-makers. More recently, sales leadership has been one of the areas gaining the most from AI advancements. As organizations continue grappling with the increasing complexity and competitiveness of the business environment, IT sales are being propelled by AI and Generative AI, which are solving problems and providing sales leaders with new technologies on how to interact with customers, run queues, and make management decisions based on metrics.

Initially, the main task of artificial intelligence was the mechanization of monotonous actions. At present, however, it has matured into providing actionable insights, forecasts, and suggestions. In a recent Salesforce report, 52% of IT sales leaders deploy AI technology for enhanced productivity, with a further 85% predicting that it will form a core part of their strategies within the next five years. Companies that apply AI to their business report an increase in conversions of 10 to 15% and a growth of 5 to 10% in the rate of client retention.

Managing complex sales pipelines and client contracts is a constant headache for IT sales leaders. Failing to remember a renewal date or a milestone shoots a company in the foot, resulting in incoming revenue loss and, in the future, an opportunity loss as well. AI-based applications like Salesforce Einstein, Microsoft Dynamics, and HubSpot bring in real-time alerts for such cases, along with reminders for renewals and milestones. They allow sales teams to pursue customers and improve chances for retention as well as upsells. A Salesforce global report showed that the participating companies that had AI integrated within the sales cycle saw their pipeline accuracy increase by 50%.

A case in point here is Schneider Electric, a global energy management and automation leader, that implemented Salesforce’s Sales Cloud and Service Cloud as part of its “One Schneider” strategy to unify its systems and create a 360-degree view of customers. The company enhanced its CRM with AI-powered CRM Analytics and Einstein Discovery, which analyze data from multiple sources, including IoT, to identify sales opportunities and predict conversion likelihood. This AI-driven approach helped Schneider Electric reduce its sales cycle time by 30%, ensuring that global sales teams focus on the most promising leads.

It is equally important to ensure active scanning of new customer data. The most sophisticated tools, such as Perplexity AI and TextCortex, are now crucial tools. Unlike conventional analytical resources, Perplexity and several other resources combine offline and online information to create a picture of the target audience’s behavioral activities and tendencies at the moment. This allows sales teams to access such data whenever there is a change in clientele’s needs or interests, making interaction with such clients more helpful and more focused. This function is most effective with highly developing industries or highly dynamic clients.

Tools of Generative AI, such as ChatGPT, CoPilot, or Google Bard, have also been of great use in ensuring that communication is effective and, above all, personalized. Enterprise sales teams can use these tools to automate the generation of personalized proposals, along with any follow-up and marketing copy that will appeal to their clients in no time. This level of automation ensures that interactions with clients remain relevant and personal, with no substantial input of manual effort, thereby easing the burden of responding to the increasing demands of customers in terms of their expectations for the degree of personalization and speed. The application of such tools is seen globally in many different fields, such as industry and finance, where there is increasing pressure to provide clients with tailored services and fast feedback.

Another common challenge is the degradation of knowledge, particularly during team transitions or onboarding new hires. AI-powered tools like Gong.io and Chorus capture and analyze sales conversations, while IBM Watson organizes and stores insights from past client interactions. These platforms reduce reliance on “tribal knowledge,” aid new hires in getting up to speed quickly, and ensure that best practices are documented and shared across the team. With teams often dispersed across different regions, these tools are crucial for maintaining consistency and knowledge continuity in global sales operations.

For example, Iron Mountain, a global leader in storage and information management, used Gong to reduce new-hire ramp time by 3 months and improve sales effectiveness. By leveraging Gong’s AI-driven insights, the company provided data-backed coaching and visibility into sales calls, helping new reps quickly identify the best practices and improve their performance. As a result, 60% of new reps hit their key metrics within five months, compared to just 9% before Gong, leading to a 148% improvement in performance. Gong’s call tracking and coaching features enabled more efficient onboarding and faster ramp-up, even in a fully remote environment.

As we have seen, therefore, both AI and Generative AI are transforming the IT sales landscape as they allow leaders to more efficiently manage their pipelines, enhance their customer interactions, and protect crucial information. They enable sales leaders to tackle the challenges of modern, fast-paced, global markets effectively. They can remain in touch with customers, be aware of the latest trends and strategies, and be prepared for expansion. As AI evolves further, it has been predicted that AI will complement sales functions with increasing significance, and it is likely to become an important success factor for IT sales leaders globally.

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Author:

Harsh Agarwal,
Senior Manager – Business Development

Super Apps are redefining the future by offering unparalleled convenience and hyper-personalization. These apps seamlessly merge our digital and physical lives, anticipate and predict our every desire and need, and let us access a universe of services, from augmented reality shopping to virtual healthcare and everything in between. Their superpower? They make everything available with a single tap. As boundaries blur between industries, super apps are forging new ecosystems, creating a gateway to a world of limitless possibility. 

So, what is a Super App? One way to think of them is as a collection of related services and functionalities on a smartphone targeted at specific interest groups, where everything is a single click away. Given their rising success with consumers, businesses will soon want Super Apps to improve workplace communication, collaboration, efficiencies, engagement, and loyalty. Super Apps have the potential to transform the mobile landscape and offer exciting opportunities for businesses and users (see Figure 1).

Figure 1

Businesses that have invested in digital transformation are developing internal applications with roles-based access for sales, marketing, operations, finance, and facilities. However, the application environment soon becomes crowded, unwieldy, and difficult to manage. This is illogical, several workflows—such as login or chat—are common to every application, and even a tiny change in the process means the enterprise must make the change separately across several such applications. A single app reduces the effort to the barest minimum.

Building a Super App requires significant time, resources, and expertise in user experience design, software development, and infrastructure management. To succeed, Super Apps must offer a seamless and intuitive user experience while providing a wide range of functionalities and services (see Figure 2).

Figure 2

However, the potential benefits of a Super App make it a worthwhile endeavor for businesses looking to provide a one-stop-shop solution for their customers. A single Super App minimizes the overhead of onboarding and change management. It delivers richer user behavior data to help developers customize, rationalize (the features), and improve the Super App. 

For enterprises that are amid their digital transformation journeys, an organization-wide Super App has become a necessity, especially because employees want frictionless interaction with colleagues and easy access to organizational processes, some of which are listed here:

• Important organization-wide broadcasts
• Project and product announcements and reporting
• HR Playbooks, checklists, guides, and support
• New employee onboarding
• Training and Development programs 
• Procurement processes
• Approved vendors
• Access to the service delivery platforms
• Polls, surveys, and internal competitions
• Events
• Customer details/ Customer visits
• Community activities
• Social messaging and chat
• Video conferencing
• Helpdesk access
• Service anniversaries
• Job postings/ Referrals
• Podcasts, blogs
• ESG targets and commitments
• Rewards and Recognition
• Travel requests
• Expense claims
• Timesheets
• Attendance management
• Salary disbursement
• Leave management
• User profile
• Password reset

Super Apps are a revolution in the making—and China’s WeChat, with a staggering , is showing us the way. And across the world, from and in Southeast Asia, and in India, and in other parts of the world, everyone is following. 

A 2022 study called showed that 7 in 10 respondents were interested in a Super App, 90 percent of whom were motivated by the convenience of an integrated app. Gartner estimates that by 2027. 

One of the leading reasons for the growth of Super Apps is app fatigue. App downloads have plateaued (), and studies forecast they will keep falling. Users will migrate to Super Apps–where all the action is brewing.

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Author:

Dileep Kumar,
Practice Head – Mobility

Enterprise Architecture (EA) as a practice, as a role, as a thought process has come a long way in the last three and half decades since its inception. Various standards, frameworks, industries, and governments joined hands and climbed the ship of EA from time to time and reached destination (goal) of their stipulated time.

EA role has both evolved and involved over the period and at this time of the AI era (other terms in the AI Epoch), multiple people have coined or termed different new terminology of EA as per their own perspective. The origin of these terminologies over the period has the basic thought that “EA is in crisis.” These all-coined terminologies are nothing but different viewpoints of EA.

EA was never and will never be in crisis. Organizations and industries crisis from the past was failure of not implementing EA. Such as the banking crisis (post which BIAN was set up) or the real estate crisis of the 2000s or the recent COVID-19 crisis. Each time one or multiple industries gets affected and there was a change in gear within organization(s) to understand EA as per their own understanding by following EA standards and frameworks specific to their industry. Over time, the standards, and frameworks pertinent to these specific industries and Enterprise Architecture have undergone a process of evolution and enhancement.

For a span of ten years, I have maintained a catalogue of EA Reference Architecture. It is important to note that the alterations to the site links are minimal and the list is not comprehensive, extending beyond the specified industry verticals or domains. While there are multiple such catalogues accessible in the market, this one is my preferred choice as it facilitates effective tracking.

In 2024 itself, one of the ” with context to obviously EA in crisis. People contested it across the news and social media with few in agreement and few in disagreement. My viewpoint is from disagreement with the strong contest is EA was and will never be specific to any stack be it business or technology stack. Then why do you need a full stack? EA was always full stack; it is not about any of the cards from the stack.

Below is the snapshot from one of EA frameworks TOGAF 10, which describes that EA is more than 70% about business and the remaining 30% would be part of data, application, and technology.

 

Issue or Crisis has always been in understanding EA. “There is always an ambiguity in understanding EA because EA is an area of the field with everything ambiguous.” Explained nicely by Dr. Pallab Saha, The Open Group CEO at one of the events. EA was never in crisis; it was always people or organizations who never realized the value of an EA until there was a crisis knock at the door. A few examples of such a mindset are in late 2020 and now ; both have not emphasized EA much. EA in such large banks has always been more of a department to complete the compliance checklist.

Now the reason this happened in Apr’2024 itself, is this AI Epoch. AI was always there in the background, in our studies during graduation but now it is reality in front of us. As mentioned earlier in this article, all EA frameworks and standards across industries are evolving to cater to AI (not just Gen AI). Last year 2023, even Gartner reflected to all CXOs, and leaders with similar guidance on the dilemma of EA during AI times.

The success of any EA depends on multiple factors, but the mantra is simply “Scale Fast, so that can’t be undone,” again from Dr. Pallab Saha. This EA mantra allowed multiple EA designs to become frameworks and standards. Consider the example of DoDAF which is EA Framework for the US Department of Defense, MoDAF which is EA Framework for the UK Ministry of Defense, or IndEA which is for the Indian Government.

Another reason in the AI era, everyone is worried about the “EA in crisis” kind of stuff is because AI is influential, and EA is noisy for 90% of organizations across the world from all diverse types of industries. The best example to explain EA being noisy would be COVID-19 crisis. Various industries disrupted by COVID-19, especially healthcare, hospitality, tourism, manufacturing, supply chain, and travel, among others. Again, the reason was not proper EA utilization whether internal or outsourced. Even research firms like Gartner, IDC, and Forrester started emphasizing EA with titles such as “EA – the dawn of new era,” and “EA – must for transformation.” As it again has gone backseat for few organizations, but all the organizations started realizing that EA is must to achieve their business vision with a timeline of 3-5 years as the target.

The authentic use of AI in EA is when AI operates as an auxiliary or support system, rather than serving as a substitute. AI tools have facilitated the acceleration of tasks within EA. However, the comprehension and expertise of Enterprise Architects remain essential for validating data points and perspectives in alignment with the business vision. Facilitating a collaborative environment between AI and EA governance could provide organizations with a competitive advantage, enhancing their alignment with their business vision and expediting the execution of EA tasks.

Multiple EAMs (Enterprise Architecture Management) or EA Tools are already incorporating AI in their core. One such tool is LeanIX which enables AI utilization within its EAM (Enterprise Architecture Management) tool. Even The Open Group has started work towards “”

Now coming to EA facets (multiple names), multiple terminologies coined over the period. I learned during one of the EA webinars, which is a fact, that there are more than eight (8) million architects’ titles in the market. If we narrow this down to EA, the count will be more than two (2) million. Organizations as per their understanding of EA and their convenience as per their businesses, have defined, coined, or termed these architectural roles. In this cycle, even EA frameworks like TOGAF have jumped and coined another term such as Digital Architect.

Another reason to have so many terminologies for an EA is because of the mindset “Architecture is about taking control of an organization.” Architecture is not about taking control of an organization. It is about giving control to organizations. You still need the organization. The clue is in the name, organization.

From my viewpoint, there are only four facets to any EA framework as mentioned in Figure 3 earlier and they are business, data, application, and technology. To contest my thought process, there is already a trend in the market that allows Architecture-as-a-service (AaaS) such as (again open source). These AaaS tool(s) are specific to an application component of EA.

There are multiple Enterprise Architects who share similar thought processes and agreements, and they play a significant role in supporting the EA practice worldwide. One such individual is Sam Holcman, whose focus is streamlining the Business Architecture component of Enterprise Architecture. Similarly, courtesy to George Hohpe, Martin Fowler, Neal Ford, and Mark Richards who keep inspiring new architects to move towards EA. The very definition of EA is now enabled as syllabus at universities, especially in India apart from other countries which would enable EA facets taught to our educational mindset even before we start working as an individual.

The mantra that I have learned over the period, especially as an EA, is to be useful and trusted to be an influencer as an EA. To achieve or execute this mantra, the focus should be on people.

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References:

• The Software Architect Elevator – George Hohpe
• Fundamentals of Software Architecture – Neal Ford
• Whole EA – Tom Graves
• TOGAF
• DoDAF
• BACOE
• Gartner

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Author:

Neeraj Gautam,
Enterprise Architect at 91¶¶Ňő

The benefits of DevOps are widely known and simple to understand. The practice brings together people, processes, engineering, operations, and technology to deliver products faster, better, and more confidently to meet business goals. However, some organizations do this better than others. Separating the leaders from the laggards is the use of tools. The teams that use DevOps tools achieve outstanding results. Our experience shows that using DevOps tools can reduce lead times to deployment by 45 percent, reduce failure rates by 50 percent, and bring down support cases by 70 percent.

With such a promising upside, the DevOps market is naturally set to balloon. suggest that it will grow from an estimated $8.66 billion in 2022 to $47.81 billion by 2030, growing at a CAGR of 23.80 percent over the forecasted period.

Life before DevOps: Chaotic

DevOps, through the adoption of agile and lean practices, has introduced a sea change in the science and art of product development. Life before DevOps was chaotic for project managers. Miscommunication between developers and the operations team would result in haphazard development, delayed deployment, the need to constantly monitor application maintenance and performance, a lack of continuous integration, and spiraling operational costs. A simple code issue would set the project manager off to see if the data team had changed values or if the developer had a reason to change a column name and to see what the tester had to say. It was a never-ending chain of debugging—with the primary outcome being employee burnout.

Life after DevOps: Under control

This has changed. The chaos can now be brought under control. Using DevOps tools and processes, program managers can now quickly identify which code broke functionality, who wrote the code, and the time it will take to fix.

In Azure DevOps, user stories are task descriptions that are treated as work items. A developer is assigned a task and must transform the user story into a functionality. To do this successfully, the developer needs, say, a database table and an API. This information is captured in the work item, along with the relevant test cases that get updated in a tool. When the developer commits the code, it is linked to this work item.

Improving the value of DevOps

Every organization that embraces DevOps believes it has evolved as a business. In reality, these organizations, without realizing it, are living with sub-optimal results from their DevOps initiatives. The situation is comparable to that of generative AI. Every organization claims to be using generative AI. However, the value of generative AI is directly linked to the data it is trained on. If the data is inadequate, the model cannot be effective. It is the same with user stories. If the project manager does not record detailed user stories, outcomes will be sub-optimal. DevOps tools are critical to avoid this prospect.

DevOps tools allow user stories to be captured in detail. Typically, the tool will capture the features a user story needs to deliver (see Figure 1), the acceptance criteria and standards (usability, design, performance, security, etc.) that will be considered to judge if the user.

Story has been successfully executed as a feature, the difficulty level in completing the task, the developer or the team to which the task has been farmed out, dependencies/impediments, associated feature requests, time spent to deliver a task, task status, etc. These tools provide a platform to record discussions and conversations before the work item is committed as code. The complete end-to-story helps the program manager stay on top of what is happening without human dependencies. Organizations that are not using these tools cannot claim to have embraced DevOps. These organizations have only implemented an Agile process with standup meetings and scrum calls. The project manager’s role should be to ensure the tools and systems capture the details.

Once the details are in the system, a dashboard can provide a deep dive (see Figure 1): How many stories and work items do we have? How many commits have been done for a particular code snippet? What is the build health? What is the quality of the code before it is put into its environment? How many builds have been successful? Is the developer doing unit testing before pushing the code?

The barriers to benefits

Several DevOps tools are available to capture development tasks (see Figure 2). These include Azure DevOps, Bitbucket, Gitlab, GitHub, Selenium, etc., which help inject transparency into development. The challenge for the project owners and managers is to ensure the team is ready to move away from traditional development models and embrace transparency. Without a mindset change and a cultural change, the tools cannot be ineffective.

Part of the reluctance to change the mindset is rooted in a misconception. Many believe the tools will eliminate the need for humans in the process. This is not true. The tools merely show the efficiency of the process that the team is following. The tools tell us how clean the product is when it goes to market, what kind of observability we have, the time a task is likely to take, etc. Tracking this information is a huge and complicated job. The tools simplify it; they automate nothing.

Organizations need to realize—and eventually, they will—that they have been using DevOps for 15 years but have not simplified it. This is because they are resistant to change. The organizations that close the culture gap and change will move ahead. The rest will play catch-up.

How to trigger cultural change with Agile Methodology

The challenge for organizations is to address the gap quickly. Here is one way to do it: Most project managers have two or three projects to manage simultaneously. The Project Management Office (PMO) should make sure there are daily calls for each project, and the manager is forced to provide a status update. Usually, the manager is a layman, not a developer. To them, the PMO should ask, “The developer did something. Did you understand what was done?” The obvious answer is, “No!” But this is where the change can be triggered. The PMO must ask the manager to read the details from the DevOps tool and get on top of the delivery. The point is to challenge managers with simple questions, “How much time will the developer take for the next build?” or “Who do you think is the right resource for the task?” or “What are the dependencies for a task?” The answers to these questions can be generated by the tool. Many of these tools have pre-defined queries, making them simple to use.

It is easy to see that organizations that claim to have embraced DevOps may not be extracting as much from the practice as possible. Their ROI is low. For example, they may not be able to unlock the ability to experiment and innovate, deliver superior product quality, release code quickly, dramatically improve time to resolution, address unplanned work with agility, reduce the cost of production, and reduce developer burnout. To all this, there is one simple answer: Create Agile culture that uses DevOps tools like a pro.

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Author:

Swetha Yalamanchili,
Head of DevOps

In an increasingly complex product development landscape, effective Product Lifecycle Management (PLM) becomes a vital part of any business’ strategy. As part of our continuous efforts to provide our clients with the most effective and innovative strategies, we’ve recently published a White Paper exploring the nuances and benefits of PLM Roadmapping.

PLM Roadmapping: A Journey of Continuous Improvement

PLM Roadmapping provides an initial strategic plan for implementing, extending, and optimizing PLM, and continuously adapts and optimizes this plan throughout the PLM journey. It aids businesses to align their PLM objectives with their overall goals using a structured approach: the roadmap helps to connect expected value to target solution components, ultimately guiding teams during implementation and deployment phases. We’ve recently explored these outcomes in greater detail, highlighting how to refine and leverage PLM strategies more effectively.

Key Components of PLM Roadmapping

Our exploration of PLM Roadmapping highlighted key components such as Value Definition, Proof of Value, Master Plan, Program Strategy, and Governance Model. Understanding these components is vital to ensure efficient and successful PLM strategies.

Applying PLM Roadmapping to Greenfield and Brownfield Implementations

Whether it’s a greenfield project (starting a new implementation from scratch), or a brownfield project (transforming an existing PLM solution), PLM Roadmapping plays an instrumental role. It provides a structured approach that caters to the unique challenges and opportunities presented in both scenarios.

Embracing Agility

At DxP Services, we firmly believe that agility in adapting to changes and alignment of PLM objectives with overall business goals are vital for successful PLM strategies. This alignment ensures that PLM Roadmapping directly supports the unique needs and strategic direction of the company.  We encourage businesses to adopt these principles within their PLM Roadmapping for optimal results.

Continuing the Conversation

This journey into PLM Roadmapping was made possible by insights from our colleagues Dr. Robert Gräb and Serdar Bulut. We’re glad to have the opportunity to share this knowledge with you and hope it brings new perspectives to your PLM strategies. If it sparks a question or an idea, we would be delighted to hear from you. For more in-depth analysis and insights on this topic, we’ve published a detailed exploration into PLM Roadmapping as a White Paper. We encourage you to delve into it and look forward to discussing your thoughts and experiences in PLM Roadmapping.

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About the Authors :

Dr. Robert Gräb is a Director and Chief Architect at DxP Services within 91¶¶Ňő. He is an expert in PLM Roadmapping and advanced PLM Architectures.
Robert.Graeb@itcinfotech.com

Serdar Bulut is a Senior Consultant at DxP Services within 91¶¶Ňő. He is an expert in Variant Management and PLM Roadmapping.
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For more information, download the full whitepaper here: /°ů±đ˛ő´ÇłÜ°ůł¦±đ/·Éłóľ±łŮ±đ±č˛ą±č±đ°ů/±č±ôłľ-°ů´Ç˛ą»ĺłľ˛ą±č±čľ±˛Ô˛µ-łŮłó±đ-°ě±đ˛â-łŮ´Ç-˛ą-±ą˛ą±ôłÜ±đ-˛ú˛ą˛ő±đ»ĺ-łŮ°ů˛ą˛Ô˛ő´Ú´Ç°ůłľ˛ąłŮľ±´Ç˛Ô/Ěý

The lending industry has new opportunities due to a rise in efficient technology and new types of lenders. There has been rapid adoption of technology to streamline the overall process of getting a mortgage, personal and business loans, enhancing the consumer experience into a smoother and faster one and expanding consumer access to financing products. While many banks are working on providing a smoother loan application experience by digitising the lending workflow process and front-end platform. However, the digitisation of the industry still needs to be improved by leveraging modern technology and data effectively. Many banks still take 2 – 4 weeks to process the loan because of labour-intensive processes, the complexity of the technology landscape and the fragmented system.

Lenders using AI and ML modelling have seen improvements in loan assessments, default pattern identification, and accurate customer behaviour prediction. This helps banks to flag risky loans and make informed decisions to minimise losses.

Traditional lenders often struggle to see the E2E customer journey because data is dispersed between multiple channels and touchpoints. Thus, they lose the insights from all that data to drive a better customer experience.

Reshaping the lending Industry with Novel Approach and Modern Technology

• Non-bank lenders continue to grow popular –
  • Non-bank lenders have invested heavily in the digitisation of user interfaces that simplify application submission, processing and collaboration with customers through real-time communication using digital channels. They offer low-cost, high-value lending products while providing users with an easier path to obtaining loans.
  • According to Oracle’s Digital Demand in Retail Banking study of 5,200 consumers from 13 countries, over 40% of customers surveyed think non-banks can better assist them with personal money management and investment needs, and 30% of respondents who haven’t tried a non-bank platform said they’re open to trying one.
  • This means bad news for traditional banks that are still slow to transition and apply digitised tools to deliver differentiated lending services.
  • Neo banks operate entirely online and provide credit and lending services digitally. It leverages data models to understand customer needs and behaviours to attract new customers and retain existing customers.

• Optimizing Customer Experience
  • Based on the study conducted by McKinsey & Company, 60 per cent of customers say they are comfortable with a completely online application. Personalisation, reassurance, transparency, simplicity and speed are vital to attract and retain the customers.
    With information like demographic data, behavioural data, psychographic attributes, cash flow of customers, and alternative data sets – like social media data, and partner ecosystem data, the banks can construct meaningful customer insight and build products that serve customer needs.
  • Banks should prioritise getting things right first time, offering quick, precise, 24×7 status updates, pre-approval within 24 hours, and providing a single point of contact.
  • AI and machine learning empower lenders to provide highly personalised experiences to customers. Lenders must build advanced algorithms to collect customer data, analyse financial profiles, and suggest customised lending options. Furthermore, the platforms could leverage crowd wisdom to source the best rates, guaranteeing customers the most competitive offers. The integration of hyper-personalization with AI and machine learning has significantly improved the lending journey, delivering convenience, efficiency, and unmatched customer satisfaction.
  • An agile tech stack with seamless integrations, including access to lifestyle and contextual data, such as social media, to provide banks with a complete picture of prospects so that offers can be tailored for outstanding customer experience.

• Third-Party Technology Providers and Open Banking for NextGen Lending
  • Open banking helps create a value-driven, profitable lending journey that retains market share and margins.
  • The future banking practice demands opening customers’ entire financial footprint to trusted third parties, including mortgages, savings, pensions, insurance, and consumer credit data
  • By harnessing unconventional data sources, open banking performs a holistic assessment of customer creditworthiness
    It also helps with income verification, Know Your Customer (KYC) confirmation and customer onboarding
  • Third-party technology and data providers are leveraging open banking to support the banks. Their activities involve marketing lending products, gathering borrower information, and underwriting, closing, or funding a loan.
    The expansive list of services is available, including loan origination platform, workflow management, document extraction and management, income and asset verification, employment verification, title verification, appraisal management, e-closings, automated compliance, and decisions model.

• Cloud-based SAS solution – Improved time to market and customer experience
  • The digitisation of the Loan origination system (LOS) helps to enable self-servicing for the broker and the bank’s sales team, provide real-time collaboration, and increase transparency. Many Fintech and Product firm offer SAS solution on the cloud that helps the bank to implement the solution much quicker and faster
  • Cloud analytics services enable the correct set of tools to develop the data model and insight that would significantly help to keep the lender products competitive and help retain the customer longer
  • Cloud-based interoperable solutions enable lenders to benefit from multiple APIs and other technology that enhance the user experience and allow for new propositions to be brought to market swiftly and safely
  • Adoption of SaaS cloud-based solutions helps create a portal between the lender, borrower, and other mortgage stakeholders, offers immense potential to automate processes through self-servicing, improve opportunities and accuracy, and reduce costs and workloads.

• ESG: Driving Sustainability and Inclusion in Mortgage Services
  • ESG Integration: Organizations worldwide, including community financial institutions, are prioritising ESG considerations in their corporate agendas. This includes local banks focusing on mortgage lending to promote diversity and inclusion and improve the lives of their customers and communities.
  • Technology-driven Solutions: Banks are harnessing technology and advanced analytics models to incorporate ESG risk to enhance risk assessment accuracy and reduce funding costs. This enables them to issue mortgages at lower rates, reducing costs for both banks and borrowers.
  • Expanding Homeownership Opportunities: Affordable homeownership aligns with ESG goals, promoting sustainability and inclusion within mortgage services. Lower costs and improved risk assessment enable a more accessible housing market, fostering economic stability and improving quality of life.

Conclusion

The risk mitigation of lending and its volatile market can be controlled by leveraging data and innovative technology solutions. AI and ML data models improve fraud and risk management and proactively detect and reduce risk exposure.
Adopting SaaS and cloud computing offers flexibility, efficiency, security, increased collaboration, reduced costs, and improved time to market.

Banks can cut down 30 – 40% of operating costs through E2E automation and redefine customer journey by leveraging third-party services and open banking ecosystems. This advancement not only enhances the reliability and value of data but also enables banks to make better-informed decisions. Moreover, it also opens new avenues in the lending market, expanding its potential reach.

ESG factors are revolutionising the mortgage and business loan services industry. Cutting-edge technology solutions empower eco-friendly approaches, broaden access to homeownership, and foster financial inclusivity. This ultimately yields advantages for both financial institutions and borrowers alike.

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Author

Kalpesh Mistry,
Senior Vice President