Introduction

Ever since generative artificial intelligence entered the business conversation late last year, it’s become impossible to separate the idea of being “future-ready” from adopting AI. With this powerful technology, businesses can attain the speed, agility and accurate decision-making they need to compete in today’s fast-changing environment.

Even before generative AI came along, AI adoption was growing. In a recent study by Economist Impact, AI and machine learning (ML) were among the top six technologies respondents had already adopted or planned to adopt in their quest to be future-ready (see Figure 1). Cognizant partnered with Economist Impact on the study—which surveyed 2,000 global executives across industries—as part of our ongoing work to define what it takes to be future-ready and how close businesses are to reaching a future-ready state. (For the full study, see “Ready for anything: what it means to be a modern business.”)

In our analysis of the data, nearly seven in 10 named AI/ML as a top investment area, indicating their belief that it‘s essential for doing business effectively today.

AI is a top-6 most-used technology

Q: Which of the following technologies has your business adopted, or plans to adopt?

1. Cloud computing (89%)

2. Internet of Things (83%)

3. Big data analytics (80%)

4. Robotic process automation (74%)

5. Advanced cloud computing (69%)

6. Artificial intelligence/machine learning (68%)*

*Tied with crowdsourcing and 5G (68%)
Base: 2,000 senior executives
Source: Economist Impact study
Figure 1

At the same time, however, just 39% of respondents said their AI/ML had already contributed significant business value. In fact, even though it was the sixth most-adopted technology, it was second to the bottom on the list in terms of its perceived value (see Figure 2).

The fact is—whether it’s generative AI or another type of AI—succeeding with AI/ML initiatives requires businesses to make a variety of unforeseen adjustments. The good news is, many businesses are beginning to attain the full value of their AI implementations. It starts with moving past the AI hype and understanding what it takes to succeed with this technology.

Difficulty achieving full tech value

Q : To what extent are the technologies you've adopted delivering strategic value to your operations? (Percent of respondents saying significant value)

Mastering the five elements to be AI-ready

Drawing on our analysis and experience, we have crafted five recommendations that enable traditional enterprises to proactively embrace AI. These recommendations center on orchestrating AI within a broader ecosystem encompassing data, technology, talent, responsible AI practices and organizational culture. By integrating these elements, enterprises can pave the way for a future in which AI becomes a driving force of their success.

Jump to a section:

1. Modernize data infrastructure

2. Overhaul legacy technology

3. Invest in AI talent

4. Prioritize responsible AI

5. Foster an AI-friendly culture

1

Modernize data infrastructure

Many businesses face the daunting challenges of inaccurate, incomplete and inconsistent data. Low-quality data threatens to sabotage the performance of AI models, resulting in wasted resources and missed opportunities.

Investing in AI without addressing data quality challenges is akin to constructing a house on an unstable foundation. AI systems heavily rely on high-quality data to generate accurate insights and help you make informed decisions. Neglecting data quality compromises the very essence of AI's potential. It undermines the trust in AI systems, erodes confidence among stakeholders and jeopardizes desired outcomes.

Data modernization is the key to effectively managing and leveraging data assets, paving the way for AI-driven insights and transformative decision-making.

>60%

of AI and ML projects fail due to too many data sources and inconsistent data

Source: O’Reilly

Here are the essential elements involved in modernizing the data infrastructure that fuels the AI endeavors of forward-thinking companies:

Prioritize data quality over data quantity:

The value lies not in the size of the dataset but in its quality. By curating a high-quality dataset, organizations lay the foundation for valuable insights and outcomes that drive innovation and success.

Establish a robust data governance framework:

Clear policies, procedures and guidelines establish a foundation for consistent data governance. According to the Economist Impact study, the top three data governance guidelines followed by companies include data quality metrics and thresholds (54%); procedures for data privacy management (53%); and guidelines for data collection, storage and use (53%).

Embrace the power of data virtualization:

Data virtualization creates a consolidated view of disparate data sources, enabling real-time access and analysis without the need for complex data integration processes. Because it breaks down data silos, data virtualization enhances agility, enables faster decision-making and amplifies the impact of AI initiatives.

Augment data with external sources:

Integrating additional information, such as demographic data, social media insights or industry-specific datasets, enriches the depth and breadth of data. This augmentation empowers organizations to develop more robust AI models, enabling smarter decision-making and driving innovation in the process.

These industry use cases exemplify the potential of a modernized data infrastructure in unleashing new opportunities with AI, fostering innovation and yielding substantial advantages:

Healthcare

By prioritizing data quality and leveraging AI, healthcare organizations can enable more accurate diagnoses, precision medicine approaches, proactive disease management and improved clinical decision-making.

Financial services

Establishing a robust data governance framework ensures regulatory compliance, protects customer privacy and mitigates data breach risks. AI models trained on financial data adhere to standards like GDPR or PCI-DSS, safeguarding sensitive information in the financial services industry.

Insurance

By integrating external data sources and leveraging AI models, insurers can enhance risk assessment, streamline claims, personalize experiences, improve fraud detection, and optimize underwriting and pricing strategies.

2

Overhaul legacy technology

As AI adoption grows, organizations must confront a critical challenge: their outdated and rigid IT infrastructure. Legacy systems, with their integration complexities and lack of standardized APIs, struggle to keep pace with the computational demands and real-time data processing requirements of AI.

Legacy infrastructure also pose a significant security risk. Built on foundations that predate the digital era, these systems were not designed to withstand the sophisticated attacks of today's threat landscape.

By embracing the future and committing to modernizing their infrastructure, organizations unlock heightened efficiency, accelerated innovation and the ability to redefine what's possible.

$1.14T

US businesses’ annual spend on maintenance of existing IT investments, including legacy systems

Source: Mechanical Orchard, US-based cloud consultancy

These recommendations will help businesses build an agile and resilient IT infrastructure that safely and securely supports the computational intensity and vast data volumes needed for AI:

Make cloud core to your infrastructure strategy:

Migrate legacy systems and applications to the cloud to leverage the power of scalable computing resources, rapid deployment and pre-built AI services. By harnessing the cloud, organizations can unlock new levels of agility and tap into the full potential of AI.

Adopt agile development practices:

Agile practices promote collaboration, continuous integration and rapid deployment, enabling organizations to stay nimble and responsive in the fast-evolving AI landscape.

Upgrade networking and connectivity:

Embrace high-speed networks, software-defined networking (SDN), and reliable connectivity to facilitate the smooth exchange of data between AI systems, data sources and users. By upgrading networking capabilities, organizations can ensure optimal performance, responsiveness and reliability.

Implement robust security measures:

Because AI relies on sensitive data, it is crucial to implement robust security measures to protect data and AI models from unauthorized access. Embrace encryption, access controls and advanced threat detection to safeguard your AI initiatives, ensuring compliance with privacy regulations and fostering trust among stakeholders.

These industry use cases demonstrate the power of modernization efforts in unlocking improved outcomes, driving innovation and gaining a competitive edge in the AI-powered digital landscape:

Enhance patient outcomes

By migrating legacy systems to the cloud, healthcare providers can leverage scalable computing resources to support AI-driven diagnostic models. Real-time insights derived from vast amounts of patient data empower healthcare professionals to make more accurate diagnoses, create personalized treatment plans and improve patient outcomes.

Enable retail modernization

With agile development practices, retailers can rapidly deploy AI-driven solutions, such as demand forecasting models, recommendation engines and dynamic pricing algorithms. This enables retailers to respond swiftly to changing market dynamics, provide personalized shopping experiences, optimize inventory management and increase customer satisfaction.

Secure AI in banking

The banking and finance industry heavily relies on AI for fraud detection, risk assessment and customer authentication. By implementing encryption, access controls and advanced threat detection, financial institutions can build trust among customers, ensure regulatory compliance and mitigate risk.

3

Invest in AI talent

Many businesses are struggling to find AI talent, including machine learning, natural language processing and computer vision, as well as soft skills like creativity and problem-solving. Those that do find it are often forced to pay top dollar.

The rapid evolution of AI necessitates professionals who not only possess existing skills but also have the capacity for continuous learning and adaptation.

Retaining these professionals proves to be equally arduous. Talented AI professionals are constantly courted by new and exciting opportunities.

62%

of business leaders lack critical skills to fulfill their AI strategy

4%

believe they have all the skills needed for their AI goals

50%

annual growth in demand for AI skills

20%

annual growth in supply of AI skills

Source: Google

Source: World Economic Forum

Overcoming these challenges requires a multi-faceted approach to acquire and nurture AI talent, ensuring a strong workforce capable of driving innovation and achieving AI excellence:

Foster internal talent development:

Identify individuals who show promise and passion for AI and invest in their development by offering comprehensive training programs, mentorship and other resources to enhance their AI skills.

Unlock the power of diversity:

A diverse workforce fosters creativity and innovation, promoting a collaborative environment where different perspectives thrive. It helps overcome the scarcity of multidisciplinary talents by expanding the pool of potential candidates.

Gain access to vast talent pools through strategic partnerships:

Strategic partners offer expertise and networks that extend beyond your organization's boundaries. By collaborating with partners, you tap into their extensive talent pool, gaining access to a diverse range of AI professionals with specialized skills and experiences.

Here’s how three companies are working to overcome AI talent challenges:

LinkedIn launched an internal AI Academy to provide employees with the opportunity to learn and develop AI skills. By empowering individuals within the organization, the company is addressing the talent shortage challenge from within.

GitLab has successfully built a diverse and global team of AI professionals by tapping into talent regardless of location, harnessing the power of remote work.

Tesla offers AI professionals the opportunity to work on cutting-edge projects related to autonomous driving and AI-based manufacturing, which attracts top AI talent seeking impactful opportunities.

4

Prioritize responsible AI

From biased algorithms to privacy concerns, companies find themselves facing a new frontier of ethical dilemmas, from how to ensure AI systems are fair and unbiased, to preventing them from being used for harmful purposes, to ensuring they are transparent and accountable.

Failure to address biased algorithms, privacy infringements and ethical decision-making by autonomous systems can unleash a torrent of consequences. Embracing the ethical dimensions of AI is not an option—it is an imperative.

84%

believe responsible AI should be a top management priority

25%

have a fully mature responsible AI program in place

Source: MIT Sloan Management Review

Businesses should view these ethical challenges as catalysts for growth rather than obstacles. By taking a firm stance on responsible AI, companies can lead the way toward a safe and secure future. Here are the essential steps that organizations can take to forge a powerful alliance with AI:

Make use of AI models:

By harnessing AI models, businesses can assess the uncertainty inherent in their AI systems. This approach not only empowers organizations to gauge the reliability and confidence levels of AI predictions, but it also enables business leaders to make informed decisions based on a comprehensive understanding of the associated uncertainties.

Utilize transparent and inherently explainable AI systems:

Approaches such as rule-set evolution can help businesses understand and explain the AI system’s decision-making processes by ensuring the models are transparent and explainable to begin with. This approach not only addresses concerns surrounding the "black box" nature of AI but also positions organizations as pioneers in responsible and ethical AI adoption.

Define and measure unwanted biases and set a goal to minimize them:

It is essential to establish clear guidelines and metrics to quantify biases within AI models and ensure fairness in outcomes. By setting the goal of minimizing bias as a primary objective for AI system optimization, businesses demonstrate a commitment to ethical practices and equitable decision-making.

The following companies are integrating responsible AI practices into their operations and demonstrating a commitment to achieving positive outcomes for their customers, employees and the broader society:

Johnson & Johnson is leveraging responsible AI in drug discovery and development. The company uses AI algorithms to analyze vast amounts of clinical trial data, identifying potential drug candidates more efficiently and accurately. By prioritizing ethical considerations, such as patient privacy and fair data usage, the company aims to improve treatment options and patient outcomes.

Mastercard has embraced responsible AI to enhance fraud detection. The company’s AI-powered systems analyze transaction data in real time, detecting fraudulent activities and protecting customer assets. Mastercard's commitment to transparency and ethical data practices ensures the accuracy and fairness of its fraud detection algorithms.

Walmart utilizes responsible AI to personalize customer experiences. The company leverages AI algorithms to analyze customer preferences and behavior, offering personalized product recommendations. Walmart's focus on transparency and privacy protection ensures that customer data is used responsibly.

5

Foster an AI-friendly culture

The path to AI-readiness stretches far beyond technological prowess. It demands a cultural metamorphosis that embraces change and dispels misperceptions and fear.

For example, fear of job displacement and uncertainty can spur doubt regarding the potential of AI, while a limited understanding of AI can also stifle its adoption because of skepticism or a lack of trust in its insights.

Businesses also need a strong cultural foundation to address the complex ethical dilemmas associated with AI, including establishing guidelines and frameworks for responsible AI use, and promoting open discussions and diverse perspectives.

77%

of consumers are concerned AI will cause job loss within the next 12 months

Source: Forbes Advisor

With the following steps, businesses can cultivate an organizational culture prepared for AI success:

Lead from the top:

Leadership sets the tone for an AI-friendly culture. By championing AI initiatives, fostering open dialog and sharing success stories, leaders can inspire their workforce.

Embrace transparency:

Communicate with clarity and purpose. Paint a vivid picture of why AI adoption is vital, showcasing its potential to augment rather than replace employees. Assure them of the organization's unwavering support during the transition.

Promote AI literacy:

Conduct training programs, workshops and knowledge-sharing sessions to demystify AI concepts and terminology. Nurture employees’ understanding and provide them with the skills to collaborate seamlessly with AI systems.

These companies are effectively tackling cultural challenges in the adoption of AI:

Moderna, a biotechnology company that makes mRNA therapeutics and vaccines, partnered with Carnegie Mellon University to create the AI Academy, which is intended to educate and empower employees at all levels to identify and integrate AI and machine learning solutions into every Moderna system and process to bring mRNA medicines to patients. AI training is now mandatory for all team members.

Johnson & Johnson launched a talent marketplace that allow employees to list their skills and be matched with personalized learning, gigs and mentorships. Employees can list their proficiency levels, gaps they may have and skills they hope to acquire. The talent marketplace has grown by a few thousand employees every month as new learning content is added and made available to J&J’s 150,000-person workforce.

Siemens aims to create modern learning environments with its “digital industry academy,” which implements knowledge-building in the industrial sector and supports the much-needed transition toward a learning company. The academy offers a comprehensive range of knowledge on Siemens industrial products to external and internal customers—directly from the manufacturer, for all industries and applications, for beginners and experts.

What to do on Monday?

As the transformative power of AI becomes increasingly evident, forward-thinking companies are recognizing that AI is not just a buzzword but a strategic tool that holds the key to unlocking innovation, efficiency and growth. The question is how to harness AI to get the full value out of this powerful technology.

Here are our recommendations to ensure your readiness for the future:

Amplify your differentiation

The starting point lies in understanding what differentiates your organization in the market. What sets you apart from the competition? What distinctive value proposition can AI help magnify? By identifying these crucial differentiators, you can lay the foundation for a clear and compelling vision of how AI can elevate and accentuate your strengths. This vision will empower you to outshine competitors, meet evolving customer demands and pave the way for unrivaled success.

Focus on value and KPIs

With a bold vision in place, the next step is to create a roadmap that outlines the strategic steps and milestones necessary to bring your AI-powered future to fruition. Begin by identifying key performance indicators (KPIs) that align with your organizational objectives and AI-enabled differentiators. These KPIs will serve as beacons, illuminating your path and enabling you to measure the success and impact of your AI initiatives.

Transform business processes with AI

By leveraging AI technologies, companies can seamlessly integrate intelligent automation, data-driven insights and advanced analytics into their workflows. AI-enhanced processes enable data-driven insights, empowering decision-makers with real-time information for agile and informed decision-making. By embracing AI-driven efficiency, businesses can unlock a new era of productivity, agility and unparalleled success.