A strategic dive into the digital operating model for an AI-first enterprise


  • A future-ready digital operating model allows swift transformation across businesses, which drives efficiency and growth.
  • We advise a five-pronged operating model, guided by shared digital infrastructure, micro change management, and a partner ecosystem.
  • Investing in each area creates an AI-first firm with start-up agility; customer-centricity; rapid innovation; competitive platform advantages; and pervasive automation.

The future-ready organization leverages AI to foresee trends and innovate in a dynamic landscape.

Future readiness entails an AI-first, composable, and ‘software-driven’ operating model which can be reconfigured quickly. This allows swift transformation across businesses, which drives efficiency and revenue.

Describing this model means thinking about how business components and capabilities interact. Achieving it requires a system-level change, not just a point solution. This entails tackling challenges associated with achieving customer-centricity, sustainable innovation, agility across the value stream, and a “data-first” approach to harness the power of AI.

Here, we introduce a five-pronged digital operating model for the AI-first enterprise, guided by shared digital infrastructure, micro change management, and a partner ecosystem (Figure 1). It evolves from our work with Fortune 500 clients and builds on our digital and cloud-first approaches.

Figure 1. A digital operating model for the AI-first enterprise

Figure 1. A digital operating model for the AI-first enterprise

Source: Infosys

Each element holds equal importance, yet full maturity necessitates the synergy of all five (product+, design+, data+, talent+, engineering+).

Investing in each area creates an AI-first firm with start-up agility; customer-centricity; rapid innovation; competitive platform advantages; and pervasive automation.

Go product-centric for speed and innovation

Product+ is about organizing the firm around ‘flow of value’ by reimagining business and technology capabilities as products aligned to customer journeys and associated value streams. Importantly, the product is a service, capability, or experience that creates value.

Product+ firms align teams to deliver in a minimum viable product (MVP) approach. As with products, capabilities and services are delivered by autonomous, cross-functional product teams, grouped to orchestrate an end-to-end customer journey, such as self-help mortgage processing or music sharing within Spotify. This eliminates internal team siloes, increases business velocity and experimentation, and prioritizes customer needs. Adopting an Objectives and Key Results framework (OKRs) helps set and achieve goals.

Product+ also fosters the idea of the platform ecosystem – an important element in the AI-first organization. Core capabilities integrated as platforms result in faster product launches, more innovations, and system stability. These can manifest as business platforms and/or common capabilities (IT infrastructure/machine learning (ML)/data, etc.) consumed by product teams.

One example of this approach is SIMPLIFY, a business platform (or unified sales app) for customer acquisition and servicing, built by IndiaFirst Life Insurance. The platform enabled tracking of a sale from lead to policy conversion to ongoing policy servicing. This was amplified by AI, recommending actions to sales agents based on historical and transactional data.

This reduced login to issuance turnaround time by 25% and paper-based processing by 99.5%. The mobile-first solution has simplified sales and product rollout, is reusable through APIs, and ensures a better customer experience.

Amplify customer experience with AI

Design+ builds new experiences, processes, and AI products that exceed customer expectations. This is often done through design thinking, and increasingly, systems thinking, where designers collaborate with data scientists, AI specialists, and ethicists to create better experiences. The process uses machine-assisted design as well as augmented reality, virtual reality, and extended reality technologies.

“There’s no longer any distinction between business strategy and the design of the user experience,” says Bridget Van Kralingen, senior vice-president of a global business services provider.

Design+ orchestrates interactions with intelligent AI systems capable of learning, adapting, and evolving.

Design ethics will move beyond usability and accessibility to encompass AI fairness, safety, transparency, and data privacy.

AI Hilal Life, a Bahrain-based life insurance company, earlier relied on in-person sales. However, its research during the pandemic found that personally tailored life insurance products were the need of the hour. The requirement for immediate coverage and no in-person meetings meant transforming the customer journey. This trend drove a new focus on self-service AI tools, immersive experience, and a digital platform to generate referrals. Today, the firm holds a 10% share of a $3 billion market, and has increased its business from direct sales and brokers to 30% of the total volume.

Lay the data foundation

Only 26% of executives cite satisfaction with AI initiatives, according to our Data + AI Radar research. A big part of this is a lack of timely, relevant, and secure data to train AI models. In fact, new research by the Infosys Knowledge Institute (Generative AI Radar 2023: North America) found that half of firms state data privacy, security, and useability curtail adoption of AI, including generative AI models. Companies that want to go AI-first must get their data estate in order first, making sure data assets are available, accessible, discoverable, and of high quality.

An AI-first firm offers a range of “live” data products, across many data types (historical/analytical, machine-generated, and user-generated), for use by the product-led engineering teams (creators) and business units (consumers) who need it. A feature store is a centralized marketplace (or platform) to store, manage, and share data products. It serves as a hub for wide data access, promoting data availability that, in turn, fosters innovation.

The University of West London (UWL) established a robust live data foundation on its way to digital transformation. Connecting the university’s data pools via a one-platform approach means it can exploit its data and monetize it to deliver more processes, and ultimately, better experiences.

To create a data-first organization, Infosys Topaz provides an AI-first set of services, solutions, and platforms using traditional and generative AI technologies. These tools drive speed and velocity in execution, with responsibility by design baked in.

Engineering excellence

Engineering is also critical in a future-first operating model. As our Tech Navigator: Building the AI-first organization discusses, digital, cloud, and AI-first architecture should use the MACH approach (Microservices-based, API-first, Cloud-native, and Headless) to become an AI-first organization. This implies that new systems can be quickly plugged in while old ones can be easily removed.

Data scientists should be able to use their preferred tools for the task at hand, making use of both open and closed AI models depending on the enterprise use case.

Software engineering and operations processes should incorporate AI assistants to boost developer, tester, and operations team productivity. According to the annual State of AI report, using GitHub Copilot led to significant productivity gains for developers. In fact, less experienced users benefit the most, with a productivity gain of some 32%.

An engineering ecosystem to elevate developer experience is also important in the AI-first age. An emerging discipline, it is a proven way to manage product and engineering complexity and allows overworked developers to take on higher value assignments.

AT&T puts engineering excellence at the heart of its operating model, with the help from several hundred data scientists and thousands of citizen developers. The company has created an AI-first feature store containing commonly used data (reducing time to wrangle data significantly), along with more than 26,000 model-building features. The firm also has more than 3,000 conversational bots in production and has strategies for integrating robotic process automation (RPA) and machine learning into its operations.

Build talent with AI-led learning paths

AI talent is a key priority - one of the top four challenges for executives looking to transform their organization to AI-first, according to our research.

To get ahead, organizations need both creator community and consumer community AI skills. Creators encompass data scientists, econometrists, machine learning engineers, and other AI experts. In the consumer community, prompt engineering is a key skill, as elaborated in Tech Navigator 2023. Newer roles in the digital sphere will span data, engineering, and design. These roles, including product managers, experience designers, digital specialists, and platform engineers, must incorporate AI-driven learning paths to ensure their readiness for the future.

Workers in this new normal will discover, test, and document best practices for a wide range of tasks. Firms should seek people with a hacker spirit that love solving puzzles, have excellent communication skills to teach both human and machine technical concepts, and who are familiar with the architecture and operation of large language models.

Those who scale AI across the organization will be in high demand.

”While some worry that AI will take their jobs, someone who is an expert in AI will certainly do so,” Jensen Huang, CEO of NVIDIA, recently told Infosys.

According to our Generative AI Radar research in North American businesses, most companies plan to tackle the skills challenge by upskilling and reskilling employees (41% of respondents). The next most often cited plan is to partner with vendors to leverage their skills and talent (33%). A smaller number (26%) plan to recruit talent with generative AI skills.

As AI progresses from AI augmenting humans to humans augmenting AI and further to AI twins in a fully AI-powered business ecosystem, organizations will need AI evangelists. These individuals understand the power of data and AI, intend to leverage AI for business growth, are comfortable using AI-based systems, and invest time in business development. To get there, firms must invest significant effort in AI literacy, transparency, ethics, scalability, and reliability of AI systems.

Despite job cuts and hiring freezes, the banking sector persists in AI talent investment. Capital One sees itself as a tech company as much as a bank, with a focus on customer experience delivered through software, data, and algorithms. It has created The Lab, which aims to nurture and harvest AI talent. This lab is integrated with the rest of the organization, and engages with external experts, including academic institutions, and AI researchers.

Toward AI-first maturity

This digital operating model increases customer-centricity and creates a culture and ecosystem that support sustainable innovation.

Firms that do it right will also gain more agility and market share. Some are just starting on this journey, while others are further ahead.

The LEGO group’s five-year AI transformation started with a fundamental question: How can we secure the legacy of one of the world’s most-loved brands in an increasingly digital era? The group first focused on technology, implementing agile programs for their tech teams and migrated workloads to cloud. But technology on its own wasn’t enough (as our Digital Radar 2023 report explains). To get ahead, they needed to change their architecture, operating model, talent profile, and tech and analytics competencies.

They identified 10 business capabilities that AI could transform, and outlined the underlying solutions and corresponding technology, data, and platform needs for each. In their product-centric operating model, each team had an executive sponsor and a leader from both business and IT, to erase the distinction between business and tech. Talent was brought in to create more flexible, fast, self-service platform capabilities, along with a turn to DevSecOps, and they opened two design studios in Shanghai with 75 experts, and another one with 200 experts in Copenhagen.

LEGO is on the right path and cites strong e-commerce and omnichannel retailer partnerships. Its turn to an AI-first operating model is in the numbers too; revenue growing 17% in 2022, operating profit growing 5%, and net profit growing 4%.

Disrupt or be disrupted is the mantra at LEGO, AT&T, UWL, AI Hilal Life, Capital One, and IndiaFirst Life Insurance Company.

Firms that want to capture similar value and enjoy AI success should adopt the mentioned AI-first operating model – going product-centric for speed and innovation; use AI-assisted design for superior customer experience; prepare data for AI-readiness; build engineering excellence for agility and quality; and enhance talent with AI-led learning paths.

This effort must be steered from the C-suite, with OKRs filtering down from top to bottom. The good news is that CEOs and CIOs already sponsor advanced AI initiatives in over a quarter of firms in the US, and Europe isn’t far behind.

Success also requires an innovative approach to change management. This means breaking down complex transformation into bite-sized chunks, minimizing “the leap of faith required to reach the other side,” as Infosys’ Jeff Kavanaugh and Rafee Tarafdar write in Harvard Business Review. Over time, this leads to infusion of AI in the enterprise DNA, the ultimate goal in the journey to the AI-first digital enterprise.

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