The world is in flux. The pandemic has waned, but the definitive trails it left behind still shape the contours of our day-to-day life. The war in Europe promises to prolong. The heat wave is beginning to show its ugly fangs in Europe while California and Alabama in the USA brace for more climatic impacts. Amongst all this, the banking sector in the USA, under the weight of the rising interest rates and poor commercial real estate market (an offshoot of the pandemic), has shown its brittle side. Understandably, in a world as chaotic as this, the CIOs and CTOs of modern-day enterprises want to operate as thriftily as possible, balancing the see-saw between digital transformation and cost savings judiciously.
Many transformation programs are either delayed or stayed in favor of more pressing, low-hanging initiatives that can help save some urgent mullah for the enterprises. Many enterprises are also brainstorming what should be their approach toward digital transformation going forward. It is in this context that the five transformation forces detailed out below could serve as a guiding light for enterprises to cherry-pick their digital transformation initiatives, thus, not only helping them invest prudently but also enabling and positioning them well to emerge successfully as an Enterprise of the Future.
Figure: The Five Transformation Forces for the Next-Gen Enterprise
The five transformation forces are intricately connected and feed into each other. While at the very foundation, enterprises can harness the promise of AI and low code/no code to bring in coding efficiencies and empower citizen technologists to own and create their applications; it is imperative that organizations consciously endeavor to create sustainable solutions with those applications while governing and managing those applications in the true spirit of digital (by bringing in AIOps, MLOps, DataOps together). However, it is not enough to merely govern and manage the applications but do enough to leverage the data generated through those applications, analyze, and derive insights from them, use those insights to refine the ML models, monetize data if possible, and, more importantly, democratize the data through the organization. Only then can the total experience be a reality? A scenario where we do not look at employee experience, customer experience, and user experience in siloed compartments but break those walls to ensure that each of these experiences can be addressed as a continuum, where each feeds the other and together as an enterprise, we provide a holistic stakeholder experience.
Let us evaluate each transformation force in some detail:
- AI Augmented Computing:
- Intent-Driven Code Generation: With the advent of Large Language Models, conversation and intent-based coding is becoming a reality and will prove to be a powerful lever in generating code from commands/intent and describing a code in layperson language as the ecosystem matures. Technologies such as RASA-X are pioneering the Conversation Driven Development approach, and others such as AWS Lex, Nuance Mix, and Oracle Digital Assistant have functionalities for Intent-Driven Development where they analyze and group semantically similar user commands with suggested intents and process them to create results. The current thunder in the chat ecosystem — ChatGPT is also developing its intent-driven processing capabilities, and together, this ecosystem can improve the developer experience and coding efficiencies in the future.
- Low Code No Code powered Citizen Development: AI-infused interventions like the above feed well into the larger democratization of technology movement that’s becoming popular worldwide, thanks to their non-technical, drag-and-drop interfaces that only empower the business teams to achieve their objectives better and faster, but it also relieves the overburdened IT organization in the enterprises to allocate their energies towards strategic initiatives.
- Marketplaces to encourage reuse: Be it APIs, connectors, adapters, or any such programmatic interface that enable intra and inter-enterprise connectivity, maximum RoI is derived only when these resources are reused repeatedly, without necessitating any unnecessary greenfield development. To encourage this behavior of reuse within the organization, API marketplaces, App Stores, and Low Code No Code Libraries are powerful mediums to enable AI-driven recommendation, discoverability, and reusability.
- Business Function as a Service: A model that enables the delivery of the entire stack of services, viz. the core infrastructure layer, the underlying applications and enabling tools, and the services layer for clients- as an integrated as-a-service model that ensures robustness, scalability, standardized and highly predictable service outcomes. In an outcome-centric world, where individual business units function as self-sufficient products, it is important for enterprises to offer infrastructure, business applications, and business operations as a “shareable utility” that can be consumed by multiple clients in a “pay-per-use” model and measured, orchestrated, and scaled using AI.
AI Augmented Computing is all set to be a powerful transformational force in the coming decades. Gartner predicts, “By 2025, 30% of outbound marketing messages from large organizations will be synthetically generated, up from less than 2% in 2022.”
“By 2030, a major blockbuster film will be released with 90% of the film generated by AI (from text to video), from 0% of such in 2022.”
2. Sustainable Solutions: While AI-driven computing will ease coding and development, organizations will do well to use such convenience to create sustainable solutions. We are living on a planet that is warming up at a very rapid rate, and the least that responsible enterprises can do is to ensure they stay on the “greener” side of things. The UN Sustainable Development Goals (SDGs) have paved the path by giving out seventeen different dimensions where we can contribute to the planet.
With most enterprises firming up on their “net zero” commitments, sustainable solutions can be shot in their arms, catalyzing this journey and ensuring they contribute positively towards the sustainability of the planet. Some of the initiatives within this are:
- Green IT Enablement: This is the design, manufacture, use, and disposal of computers, chips, technology components, and peripherals in a way that limits the harmful impact on the environment, including reducing carbon emissions and the energy consumed by manufacturers, data centers, etc.
- Sustainable Finance: Likewise, while making investment decisions, enterprises should take into consideration environmental, social, and governance (ESG) factors, leading to more long-term investments in sustainable economic activities and projects.
- Circular Supply Chain solutions: This is where an enterprise would reuse or repurpose waste and customer returns to convert those into new or refurbished products. A circular supply chain aims to minimize the use of raw materials and minimize discarded waste materials.
- Emission Data and Analytics: Through this, enterprises in the emitting industries, especially Energy, Utilities, Plant, and Manufacturing customers, would want to consciously measure their emissions, track them through various eco-sensitive parameters and create opportunities to optimize the emission severity using such intelligence.
- ESG Consulting: The frenetic activity in this space opens opportunities for IT Consulting firms to come out with unique processes and methodologies that can help enterprises to undertake this journey towards sustainability and to keep up with their “net zero” commitments.
The coming two decades would be painted by the colors of ESG. As McKinsey quoted recently, “ESG [environmental, social, and governance] is no longer about a philanthropic desire to do good and be a good corporate citizen. It heavily influences the way that investors, customers, and potential hires look at us as well.”
3. Digital Operations: It is not enough to create AI-driven applications and not sufficient to create them for a sustainable purpose. These applications need to be managed and governed well too, lest the technical debt will make the entire stack unsustainable very quickly. This is where the whole paradigm of “Digital Operations”— encompassing themes such as AIOps, MLOps, DataOps, observability, and simulation through a Digital Twin, Chaos Engineering to check on preparedness and security robustness, Rapid Troubleshooting and Hyperautomation play a critical role to ensure that the applications and systems within an enterprise are maintained, managed, governed, and run in a digitally potent manner.
- MLOps, AIOps, and DataOps: While MLOps is a core function of Machine Learning engineering, focused on streamlining the process of taking machine learning models to production and then maintaining and monitoring them, AIOps utilizes such models and applies AI capabilities to automate and streamline operations, creating valuable data insights through observability while DataOps brings together data producers and data consumers within the enterprise, breaking down siloes, making data more democratized, available and reliable. Thus, each is connected to the other, and robust models, when well governed, can result in great insights that can be plowed back to enterprise strategy.
- Digital Process Twin: Utilizing virtual representations of a process, object, or system that is updated with real-time process data can be used to run scenario simulations with machine learning on top, shaping the decisions for each such scenario. This is a powerful tool, and many enterprises are already leveraging its benefits.
- Chaos Engineering: With this, we inject failure and faulty scenarios into the runtime to verify its resilience in the face of random disruptions. These disruptions can cause applications to respond unpredictably and break under pressure, but such unpredictable behavior becomes a trigger to explore solutions and plug such gaps to make the applications more secure and airtight.
- Rapid Troubleshooting: Where the “collaborative intelligence” of humans and machines is harnessed to solve issues.
- Hyperautomation: This is an approach where a combination of automation approaches come together to address various enterprise-wide use cases. This sets an enterprise on the path towards truly intelligent automation that is outcome and use cases focused rather than being fixated on a technology or a particular automation approach, thus yielding maximum business ROI.
Digital Operations is a big transformation force where technology, tools, people, processes, products, and services all come together. This is a potpourri of activity, a cauldron where transformation is governed and managed using next-gen approaches and, if done right, will not just result in a stable and state-of-the-art process but will also help generate valuable data, the intelligence from which can become a goldmine for future business strategies. This is where our next transformation force emerges.
4. Hybrid Intelligence: If Data is the key to opportunities, the intelligence innate in the data is the magic potion that unlocks them. Analytics-driven insights from business operations, AI-driven insights on customer behavior, and semantic intelligence scraped by the chatbots on the front —all come together to provide strategic directions to business owners. Having systems in place that can capture such intelligence in real-time and prompt the customer-facing crew to tailor and tinker with their responses in real-time — therein lies the magic and conversion of business.
Further, if each business unit is potentially a data producer and every other unit is potentially a data consumer, treating data as a “product” that can be shared between units and thus “monetized” by the source unit can trigger new avenues of value. This is orchestrated through the Data Mesh architecture, where domain teams are empowered to manage their data and monetize it as a shared utility. Industry-specific AI/ML solutions are an important cog in developing a full-fledged Hybrid Intelligence offering. As each domain has its own uniqueness and regulatory guardrails, solutions need to be tailored to fit each domain.
5. Total Experience: AI-driven computing to create sustainable solutions that are governed to create copious amounts of data intelligence will create a basis to provide a holistic stakeholder experience. Happy employees (EX) keep the end users happy (UX), and when end users are happy, the customer experience (CX) is at the zenith too. In a world where experiences are becoming extremely ephemeral and where stakeholders jostle through various competing technologies, it is important for modern-day enterprises to cater to such multi-experience (MX) appetite (be it VR/MR/XR/Metaverse, etc.). Many of the fashion and retail industry customers have already embarked on Metaverse in a big way, and therefore, consulting partners must have services around those new-age channels to be able to engage with the employees and end uses of such customers. As a part of this, offerings such as Avatar creation, tokenizing assets in the form of NFTs, investing in blockchain to streamline supply chain operations, etc., become crucial components. On the employee front, enabling Metaverse-driven employee onboarding solutions and enabling virtual KYC for banking industry customers can reimagine the total stakeholder experience. At a broader level, the whole Web 3.0 is a big opportunity area for next-gen enterprises.
The total experience is the default expectation today as it brings all cogs together and ensures that the sum of all parts is greater than the whole. As Gartner predicts, “By 2024, organizations providing a total experience will outperform competitors by 25% in satisfaction metrics for both CX and EX.”
Conclusion
Next-gen enterprises will have to be holistic beings that respond dynamically to the changing ecosystem. These changes will be more rapid than ever, and hence a ground-up approach to Digital Transformation will make the whole journey systematic, methodical, and connected. Right from the time a code is written and an application is built, there must be a sustainable purpose attached to it, and such sustainability can be maintained only when the governance is robust and continuous through digital operations. Such robustness in operations will yield a goldmine of data that can be harvested for intelligence and utilized to drive Total Experience. Thus, these pentagonal forces are not isolated monoliths but connected systems that co-exist with each other’s support. This is not a linear journey, too, where a customer needs to start from one to reach five in the pyramid shown in Figure 1. This is a very fungible model where if Customer A is doing well on one and two, can directly embark on three and so on.
As the global ecosystem wades through all the flux and enterprises re-balance their priorities between transformations and cost savings, it is important that they undertake the basic spadework in the above-mentioned five dimensions so that the moment sanity gets restored and things get back to the status quo, they can amp up their transformation game.
The world is in flux. The pandemic has waned, but the definitive trails it left behind still shape the contours of our day-to-day life. The war in Europe promises to prolong. The heat wave is beginning to show its ugly fangs in Europe while California and Alabama in the USA brace for more climatic impacts. Amongst all this, the banking sector in the USA, under the weight of the rising interest rates and poor commercial real estate market (an offshoot of the pandemic), has shown its brittle side. Understandably, in a world as chaotic as this, the CIOs and CTOs of modern-day enterprises want to operate as thriftily as possible, balancing the see-saw between digital transformation and cost savings judiciously.
Many transformation programs are either delayed or stayed in favor of more pressing, low-hanging initiatives that can help save some urgent mullah for the enterprises. Many enterprises are also brainstorming what should be their approach toward digital transformation going forward. It is in this context that the five transformation forces detailed out below could serve as a guiding light for enterprises to cherry-pick their digital transformation initiatives, thus, not only helping them invest prudently but also enabling and positioning them well to emerge successfully as an Enterprise of the Future.
Figure: The Five Transformation Forces for the Next-Gen Enterprise
The five transformation forces are intricately connected and feed into each other. While at the very foundation, enterprises can harness the promise of AI and low code/no code to bring in coding efficiencies and empower citizen technologists to own and create their applications; it is imperative that organizations consciously endeavor to create sustainable solutions with those applications while governing and managing those applications in the true spirit of digital (by bringing in AIOps, MLOps, DataOps together). However, it is not enough to merely govern and manage the applications but do enough to leverage the data generated through those applications, analyze, and derive insights from them, use those insights to refine the ML models, monetize data if possible, and, more importantly, democratize the data through the organization. Only then can the total experience be a reality? A scenario where we do not look at employee experience, customer experience, and user experience in siloed compartments but break those walls to ensure that each of these experiences can be addressed as a continuum, where each feeds the other and together as an enterprise, we provide a holistic stakeholder experience.
Let us evaluate each transformation force in some detail:
- AI Augmented Computing:
- Intent-Driven Code Generation: With the advent of Large Language Models, conversation and intent-based coding is becoming a reality and will prove to be a powerful lever in generating code from commands/intent and describing a code in layperson language as the ecosystem matures. Technologies such as RASA-X are pioneering the Conversation Driven Development approach, and others such as AWS Lex, Nuance Mix, and Oracle Digital Assistant have functionalities for Intent-Driven Development where they analyze and group semantically similar user commands with suggested intents and process them to create results. The current thunder in the chat ecosystem — ChatGPT is also developing its intent-driven processing capabilities, and together, this ecosystem can improve the developer experience and coding efficiencies in the future.
- Low Code No Code powered Citizen Development: AI-infused interventions like the above feed well into the larger democratization of technology movement that’s becoming popular worldwide, thanks to their non-technical, drag-and-drop interfaces that only empower the business teams to achieve their objectives better and faster, but it also relieves the overburdened IT organization in the enterprises to allocate their energies towards strategic initiatives.
- Marketplaces to encourage reuse: Be it APIs, connectors, adapters, or any such programmatic interface that enable intra and inter-enterprise connectivity, maximum RoI is derived only when these resources are reused repeatedly, without necessitating any unnecessary greenfield development. To encourage this behavior of reuse within the organization, API marketplaces, App Stores, and Low Code No Code Libraries are powerful mediums to enable AI-driven recommendation, discoverability, and reusability.
- Business Function as a Service: A model that enables the delivery of the entire stack of services, viz. the core infrastructure layer, the underlying applications and enabling tools, and the services layer for clients- as an integrated as-a-service model that ensures robustness, scalability, standardized and highly predictable service outcomes. In an outcome-centric world, where individual business units function as self-sufficient products, it is important for enterprises to offer infrastructure, business applications, and business operations as a “shareable utility” that can be consumed by multiple clients in a “pay-per-use” model and measured, orchestrated, and scaled using AI.
AI Augmented Computing is all set to be a powerful transformational force in the coming decades. Gartner predicts, “By 2025, 30% of outbound marketing messages from large organizations will be synthetically generated, up from less than 2% in 2022.”
“By 2030, a major blockbuster film will be released with 90% of the film generated by AI (from text to video), from 0% of such in 2022.”
2. Sustainable Solutions: While AI-driven computing will ease coding and development, organizations will do well to use such convenience to create sustainable solutions. We are living on a planet that is warming up at a very rapid rate, and the least that responsible enterprises can do is to ensure they stay on the “greener” side of things. The UN Sustainable Development Goals (SDGs) have paved the path by giving out seventeen different dimensions where we can contribute to the planet.
With most enterprises firming up on their “net zero” commitments, sustainable solutions can be shot in their arms, catalyzing this journey and ensuring they contribute positively towards the sustainability of the planet. Some of the initiatives within this are:
- Green IT Enablement: This is the design, manufacture, use, and disposal of computers, chips, technology components, and peripherals in a way that limits the harmful impact on the environment, including reducing carbon emissions and the energy consumed by manufacturers, data centers, etc.
- Sustainable Finance: Likewise, while making investment decisions, enterprises should take into consideration environmental, social, and governance (ESG) factors, leading to more long-term investments in sustainable economic activities and projects.
- Circular Supply Chain solutions: This is where an enterprise would reuse or repurpose waste and customer returns to convert those into new or refurbished products. A circular supply chain aims to minimize the use of raw materials and minimize discarded waste materials.
- Emission Data and Analytics: Through this, enterprises in the emitting industries, especially Energy, Utilities, Plant, and Manufacturing customers, would want to consciously measure their emissions, track them through various eco-sensitive parameters and create opportunities to optimize the emission severity using such intelligence.
- ESG Consulting: The frenetic activity in this space opens opportunities for IT Consulting firms to come out with unique processes and methodologies that can help enterprises to undertake this journey towards sustainability and to keep up with their “net zero” commitments.
The coming two decades would be painted by the colors of ESG. As McKinsey quoted recently, “ESG [environmental, social, and governance] is no longer about a philanthropic desire to do good and be a good corporate citizen. It heavily influences the way that investors, customers, and potential hires look at us as well.”
3. Digital Operations: It is not enough to create AI-driven applications and not sufficient to create them for a sustainable purpose. These applications need to be managed and governed well too, lest the technical debt will make the entire stack unsustainable very quickly. This is where the whole paradigm of “Digital Operations”— encompassing themes such as AIOps, MLOps, DataOps, observability, and simulation through a Digital Twin, Chaos Engineering to check on preparedness and security robustness, Rapid Troubleshooting and Hyperautomation play a critical role to ensure that the applications and systems within an enterprise are maintained, managed, governed, and run in a digitally potent manner.
- MLOps, AIOps, and DataOps: While MLOps is a core function of Machine Learning engineering, focused on streamlining the process of taking machine learning models to production and then maintaining and monitoring them, AIOps utilizes such models and applies AI capabilities to automate and streamline operations, creating valuable data insights through observability while DataOps brings together data producers and data consumers within the enterprise, breaking down siloes, making data more democratized, available and reliable. Thus, each is connected to the other, and robust models, when well governed, can result in great insights that can be plowed back to enterprise strategy.
- Digital Process Twin: Utilizing virtual representations of a process, object, or system that is updated with real-time process data can be used to run scenario simulations with machine learning on top, shaping the decisions for each such scenario. This is a powerful tool, and many enterprises are already leveraging its benefits.
- Chaos Engineering: With this, we inject failure and faulty scenarios into the runtime to verify its resilience in the face of random disruptions. These disruptions can cause applications to respond unpredictably and break under pressure, but such unpredictable behavior becomes a trigger to explore solutions and plug such gaps to make the applications more secure and airtight.
- Rapid Troubleshooting: Where the “collaborative intelligence” of humans and machines is harnessed to solve issues.
- Hyperautomation: This is an approach where a combination of automation approaches come together to address various enterprise-wide use cases. This sets an enterprise on the path towards truly intelligent automation that is outcome and use cases focused rather than being fixated on a technology or a particular automation approach, thus yielding maximum business ROI.
Digital Operations is a big transformation force where technology, tools, people, processes, products, and services all come together. This is a potpourri of activity, a cauldron where transformation is governed and managed using next-gen approaches and, if done right, will not just result in a stable and state-of-the-art process but will also help generate valuable data, the intelligence from which can become a goldmine for future business strategies. This is where our next transformation force emerges.
4. Hybrid Intelligence: If Data is the key to opportunities, the intelligence innate in the data is the magic potion that unlocks them. Analytics-driven insights from business operations, AI-driven insights on customer behavior, and semantic intelligence scraped by the chatbots on the front —all come together to provide strategic directions to business owners. Having systems in place that can capture such intelligence in real-time and prompt the customer-facing crew to tailor and tinker with their responses in real-time — therein lies the magic and conversion of business.
Further, if each business unit is potentially a data producer and every other unit is potentially a data consumer, treating data as a “product” that can be shared between units and thus “monetized” by the source unit can trigger new avenues of value. This is orchestrated through the Data Mesh architecture, where domain teams are empowered to manage their data and monetize it as a shared utility. Industry-specific AI/ML solutions are an important cog in developing a full-fledged Hybrid Intelligence offering. As each domain has its own uniqueness and regulatory guardrails, solutions need to be tailored to fit each domain.
5. Total Experience: AI-driven computing to create sustainable solutions that are governed to create copious amounts of data intelligence will create a basis to provide a holistic stakeholder experience. Happy employees (EX) keep the end users happy (UX), and when end users are happy, the customer experience (CX) is at the zenith too. In a world where experiences are becoming extremely ephemeral and where stakeholders jostle through various competing technologies, it is important for modern-day enterprises to cater to such multi-experience (MX) appetite (be it VR/MR/XR/Metaverse, etc.). Many of the fashion and retail industry customers have already embarked on Metaverse in a big way, and therefore, consulting partners must have services around those new-age channels to be able to engage with the employees and end uses of such customers. As a part of this, offerings such as Avatar creation, tokenizing assets in the form of NFTs, investing in blockchain to streamline supply chain operations, etc., become crucial components. On the employee front, enabling Metaverse-driven employee onboarding solutions and enabling virtual KYC for banking industry customers can reimagine the total stakeholder experience. At a broader level, the whole Web 3.0 is a big opportunity area for next-gen enterprises.
The total experience is the default expectation today as it brings all cogs together and ensures that the sum of all parts is greater than the whole. As Gartner predicts, “By 2024, organizations providing a total experience will outperform competitors by 25% in satisfaction metrics for both CX and EX.”
Conclusion
Next-gen enterprises will have to be holistic beings that respond dynamically to the changing ecosystem. These changes will be more rapid than ever, and hence a ground-up approach to Digital Transformation will make the whole journey systematic, methodical, and connected. Right from the time a code is written and an application is built, there must be a sustainable purpose attached to it, and such sustainability can be maintained only when the governance is robust and continuous through digital operations. Such robustness in operations will yield a goldmine of data that can be harvested for intelligence and utilized to drive Total Experience. Thus, these pentagonal forces are not isolated monoliths but connected systems that co-exist with each other’s support. This is not a linear journey, too, where a customer needs to start from one to reach five in the pyramid shown in Figure 1. This is a very fungible model where if Customer A is doing well on one and two, can directly embark on three and so on.
As the global ecosystem wades through all the flux and enterprises re-balance their priorities between transformations and cost savings, it is important that they undertake the basic spadework in the above-mentioned five dimensions so that the moment sanity gets restored and things get back to the status quo, they can amp up their transformation game.