The advanced analytics market has seen significant growth in recent years, driven by the increasing need for data-driven decision-making across various industries. This case study explores the key trends shaping the global advanced analytics landscape, the technologies driving this evolution, and notable applications within diverse sectors.

The Growth of Advanced Analytics

Advanced analytics encompasses a wide range of techniques, including predictive analytics, data mining, statistical analysis, and machine learning. Businesses leverage these techniques to gain insights from vast amounts of data, enhancing decision-making processes and operational efficiency.

Market Overview

According to recent market research, the global advanced analytics market was valued at approximately $22 billion in 2023 and is projected to grow at a CAGR (Compound Annual Growth Rate) of 22% from 2024 to 2030. This rapid growth is fueled by:

1. Data Proliferation: The explosion of data generated from IoT devices, social media, and online transactions has necessitated sophisticated analytical tools that can handle complex datasets.
2. Technological Advancements: Innovations in AI and machine learning models are enhancing analytics capabilities, making them more accessible to businesses of all sizes.
3. Competitive Advantage: Organizations are increasingly recognizing the strategic advantages of using advanced analytics to improve customer experience, optimize operations, and identify new market opportunities.

Key Trends in Advanced Analytics

1. Integration of AI and Machine Learning: Companies are increasingly integrating AI-driven analytics into their operations. This trend allows businesses to automate decision-making processes and improve predictive capabilities.
2. Real-time Analytics: The demand for real-time insights is growing, particularly in sectors like finance and e-commerce, where quick decision-making is critical. Tools that provide instant analytics enable organizations to respond swiftly to market changes.
3. Cloud-based Analytics Solutions: The migration to cloud platforms is reshaping how businesses approach analytics. Cloud-based solutions offer scalability, flexibility, and cost-effectiveness, enabling organizations to harness analytics without heavy upfront investments.
4. Data Democratization: Businesses are striving to make analytics accessible to non-technical users. User-friendly interfaces and data visualization tools allow employees at all levels to interact with data and derive insights without requiring extensive technical expertise.
5. Focus on Data Governance and Compliance: As organizations become more data-driven, the importance of data governance cannot be overstated. Ensuring data quality, privacy, and compliance with regulations like GDPR is paramount.

Industry Applications

Healthcare

In healthcare, advanced analytics is transforming patient care. Predictive analytics are used to anticipate outbreaks, manage hospital resources, and personalize treatment plans. For instance, hospitals utilize data to predict patient admission rates, thereby optimizing staff allocation and inventory management.

Retail

Retailers leverage advanced analytics to enhance customer experience through personalized marketing strategies based on buying patterns. Machine learning algorithms analyze customer data to improve inventory management and forecast demand, reducing waste and maximizing profits.

Financial Services

Financial institutions are utilizing advanced analytics to detect fraudulent transactions, assess credit risk, and enhance customer interactions. By analyzing customer behavior patterns, banks can tailor their offerings and improve customer satisfaction.

Manufacturing

In manufacturing, advanced analytics helps optimize supply chain management and predictive maintenance. By analyzing data from machinery, companies can predict failures before they occur, reducing downtime and maintenance costs.

Conclusion

The advanced analytics market is evolving rapidly, driven by the increasing availability of data and powerful analytical tools. As organizations across industries continue to embrace data-driven strategies, the demand for advanced analytics solutions will only grow. By understanding these trends and effectively leveraging analytics, businesses can gain a competitive edge in today’s data-centric environment.

Recommendations

For organizations looking to enhance their advanced analytics capabilities, consider the following steps:

• Invest in Training: Ensure your teams are equipped with the necessary skills to utilize advanced analytics tools effectively.
• Adopt Cloud Solutions: Transition to cloud-based analytics to benefit from scalability and flexibility.
• Prioritize Data Governance: Implement robust data governance practices to maintain data quality and compliance.
• Foster a Data-driven Culture: Encourage all employees, regardless of their technical skills, to engage with data and analytics in their daily decision-making processes.

By embracing these recommendations, organizations can better position themselves to thrive in the evolving landscape of advanced analytics.

AI-driven innovation is supported by significant investments. Venture capitalists poured an impressive $42.5 billion into AI startups in 2023 alone, according to CB Insights, as reported by the Financial Times. These investments align with the growing importance of data analytics, a focus area for businesses worldwide. Projections suggest that the global data analytics market, valued at $61.44 billion in 2023, will skyrocket to an astounding $581.34 billion by 2033.

This surge in investment is not only reshaping the industry but also driving job creation within the data field. The increasing demand for data-driven insights presents a promising outlook for careers in data analytics and data science. With the exponential growth in data generation and the imperative to extract valuable insights, the demand for professionals is expected to outpace the available workforce. The U.S. Bureau of Labor Statistics projects a 35% increase in data scientist positions between 2022 and 2032, highlighting the need for skilled professionals in the field.

The Human Element in the Age of AI

Contrary to the fear that AI will replace human roles, the future of data analytics will see professionals thriving in collaboration with AI rather than being replaced by it. While AI can automate certain tasks, the human strengths of interpretation, strategic thinking, and nuanced analysis remain irreplaceable. This synergy between human expertise and AI-driven tools will shape the future of data science roles.

Analysts as Navigators: From Data Processors to Strategic Storytellers

Data analysts will no longer be limited to data processing roles. As the volume of data continues to grow, their role will evolve into that of storytellers who translate complex datasets into actionable insights. They will guide organizations through the sea of information, helping them make informed, data-driven decisions. To thrive in this new environment, analysts must sharpen their critical thinking and communication skills, as these will be key to unlocking the potential of AI-driven data analysis.

AI Collaboration: New Roles and Opportunities

The rise of AI and machine learning (ML) will introduce new roles and reshape existing ones. Tasks such as data cleaning, pipeline management, and basic analysis will be automated, allowing professionals to focus on more complex and value-driven activities. AI will assist in model training and provisioning, but human analysts will remain essential for problem formulation, feature selection, and model interpretation.

This human-AI collaboration will create specialized roles, such as AI trainers and AI operations specialists, who will ensure that AI systems function optimally. Additionally, roles like AI ethicists and data privacy experts will emerge, tasked with addressing ethical concerns and ensuring compliance with data regulations.

Specialization: The Future of In-Demand Skills

As AI continues to automate routine tasks, the demand for analysts with specialized skills will rise. Professionals with expertise in specific industries, such as healthcare, finance, or education, will be highly sought after to address industry-specific challenges. This shift will require analysts to develop deep domain knowledge, enabling them to tackle complex problems with data-driven solutions.

Communication and Collaboration: The New Skillset for Data Professionals

Technical skills alone will no longer suffice for success in data-related fields. Effective communication and collaboration across teams and departments will be critical in navigating the AI-driven landscape. As AI models become more sophisticated, they often function as “black boxes” with opaque decision-making processes. Human analysts will be needed to interpret and explain these models’ outputs, mitigating potential biases and ensuring that insights are actionable and aligned with organizational goals.

Interpersonal skills, cross-functional collaboration, and inclusive communication will become increasingly valuable, especially in environments with dispersed teams. Analysts who can tell compelling stories with data, foster collaboration, and bridge the gap between technical and non-technical stakeholders will stand out in the evolving job market.

Ethical Data Practices: A Growing Priority

As data privacy regulations evolve and concerns about AI bias grow, data professionals will play a key role in championing ethical and responsible data use. The rise of AI governance specialists and data security officers will reflect the growing emphasis on data protection, innovation, and regulatory compliance.

Data analysts will need to be well-versed in data privacy laws and security protocols to navigate the increasingly complex regulatory landscape. Ethical data handling will be paramount as organizations strive to build trust with consumers and regulators alike.

Continuous Learning: The Key to Future Success

The fields of data science and advanced analytics are constantly evolving, with new technologies and methodologies emerging regularly. To stay competitive, data professionals must adopt a growth mindset and prioritize continuous learning. This involves staying current with industry trends, mastering new tools and technologies, and adapting to the ever-changing demands of the data field.

Specialization will remain important, but it must be built on a strong foundation in core areas such as mathematics, statistics, and business analytics. Professionals who can combine this foundational knowledge with expertise in AI, machine learning, and data engineering will be well-positioned to lead data-driven advancements over the next decade.

Upskilling for the Future of Work

The “Great Resignation” of recent years has left many workers feeling undervalued and stagnant in their careers. A study from the University of Phoenix Career Institute® found that while workers remain optimistic about their future, they often lack growth opportunities within their organizations. Companies, on the other hand, struggle to find external talent to fill key roles.

The solution lies in upskilling existing employees. Investing in internal talent development is not only more cost-effective but also more sustainable than relying solely on external recruitment. Data professionals who embrace AI tools and continuously enhance their skills will be at the forefront of innovation, driving growth in the data-driven economy.

Conclusion: Embracing AI for a Data-Driven Future

The future of data and analytics lies in the collaboration between human expertise and AI technology. Data professionals who can navigate this evolving landscape by enhancing their skills, embracing continuous learning, and fostering collaboration will thrive in the data-driven world of tomorrow. As AI continues to reshape industries, the demand for data-related roles will only grow, offering exciting opportunities for those who are prepared to seize them.

However, the path to successful cloud migration is fraught with challenges, including poorly defined scopes, legacy infrastructure complexities, and ineffective cloud management models. For cloud migration to deliver on its promises, companies need to shift their mindset from traditional data center operations to modern platform capabilities, ensuring they adopt cloud-native tools like Infrastructure-as-Code (IaC) and automation.

Five Common Pitfalls:

1. Treating the Cloud as a Data Center: Replicating the current infrastructure in the cloud without leveraging modern platform capabilities limits productivity. Applications need to be modernized and rationalized for efficient scaling.

2. Failing to Manage Cloud Costs: Transitioning from a capex to an opex-based model requires careful planning and monitoring to avoid cost overruns.

3. Neglecting Decommissioning Timelines: Running in-house and cloud platforms simultaneously can inflate costs. Timely data center exit is essential.

4. Losing Control to Partners: Relying too heavily on cloud service providers without internal capability building can delay migration and lead to unmet goals.

5. Proliferation of Cloud Programs: Running multiple cloud programs with different vendors can result in integration challenges, reducing economies of scale and efficiency.

Success Factors: To overcome these challenges, companies must treat cloud migration as a business transformation, not just an infrastructure shift. A clear cloud strategy aligned with business objectives is critical, as is adopting a migration factory approach to streamline processes and maximize efficiency.

Key Steps to Ensure Success:

• Proactive Cloud Consumption Management: Implementing Cloud FinOps ensures costs are actively measured and aligned with business objectives.

• Vendor Management & In-House Skill Development: Ensuring third parties collaborate with internal teams builds in-house expertise and reduces dependency on external partners.

• Program Assurance: A consistent quality assurance framework, starting from the planning phase through to full cloud operations, helps identify and mitigate risks early.

By embedding these principles and practices into their cloud transformation journey, companies can maximize the cloud’s business value while minimizing risks.

by the weight of your own stockpiles.

While some have adopted smarter forecasting, the sector as a whole lags in fully leveraging data-driven solutions that can untangle this mess. Advanced analytics offer a lifeline—cutting through inefficiencies with real-time insights, predictive capabilities, and end-to-end supply chain visibility.

Analytics: The Competitive Edge

To crack this inventory challenge, companies need more than band-aid fixes—they need precision. Predictive analytics can forecast demand with striking accuracy, reshaping supply chains and enabling agile inventory management. Real-time insights empower firms to make split-second adjustments, avoiding both shortages and surpluses that cripple profitability.

Yet, industry-wide adoption of these technologies remains sluggish. Legacy systems, resistance to change, and underinvestment in analytics are roadblocks many haven’t yet overcome.

Survival of the Data-Driven

As supply chains become more intricate, those who embrace data analytics will dominate, slashing their inventory while maximizing efficiency. Those who don’t? They’ll be left suffocating under excess stock, outpaced by data-savvy competitors. The stakes are higher than ever, and the path forward is clear—cut through the noise with data, or risk being buried by the weight of your own stockpiles.

Fast-forward to today, when companies are often awash in data and trying to figure out if they can turn it into a business. The answer isn’t obvious. Building a data business is not for every institution, especially in a field where a few dominant players with massive advantages in data already exist. However, the potential rewards can be immense for companies that can unlock unique data, analytics, or organizational know-how to create a product that addresses an untapped market opportunity.

A European building-materials company identified a new-business opportunity with more than $500 million in enterprise value by turning an internal tool for tracking key performance indicators (KPIs) into a product it could sell externally. Similarly, a telecom company is on track to realize $200 million in new revenue in less than five years by using its data to build a digital lending business. And they’re not alone. McKinsey’s annual survey of business leaders on new-business building found that approximately 40 percent of them expect to create data, analytics, and AI-based in the next five years—the highest of any new-business building category.

How do you know if building a data business can create value for your organization? In this article, we share why now is the right time to consider it, how to assess whether it’s a good fit, and what the critical considerations are for getting started.

Why now

While leaders have sized up data businesses for over a decade, evolving technology capabilities and greater adoption worldwide of AI and analytics have increased the feasibility of monetizing data today. Four technology shifts, in particular, have enabled companies to create new data products faster and less expensively than ever:

Enhanced data-management efficiency: Companies can more efficiently process, manage, access, and reuse data in real time across different platforms thanks to greater sophistication of data tools and technologies. This efficiency is crucial for creating a scalable and sustainable data business.

Generative AI (gen AI): A few years ago, converting unstructured data, such as text, images, and videos, into a standardized form so it could be accessed and analyzed was prohibitively expensive for most companies. Gen AI has made structuring such data more cost-effective, enabling broader use. Combined with the emergence of low-code and no-code analytics platforms that democratize AI and analytics, data businesses can now derive more value from their data.

Increased access to real-world data: As Internet of Things (IoT) adoption accelerates, the costs and barriers associated with implementing sensor technology and capturing real-world data have significantly decreased. Companies can now gather real-world data more quickly and affordably and make it accessible to a broader range of applications.

Growing use of internal data products: Industry leaders are increasingly treating data like a product internally so that a given data set can support many different use cases (see sidebar, “What is a data product?”). This “data packaging” gives them a head start in monetizing their data.

What is a data product?

Additionally, we anticipate the thirst for data-driven decision making to intensify as leaders vie for their share of the up to $17.7 trillion in value potential from data and analytics.2 Add in another $2.6 trillion to $4.4 trillion from gen AI.3 This can create fertile ground for data and AI products. Consider Walmart Data Ventures, which launched a data solution to help suppliers better understand customers’ shopping behavior, among other insights. The company’s product, called Walmart Luminate, filled a gap in the market, enabling the company to achieve strong market adoption and 80 percent quarter-over-quarter growth during its first year.4

Because data businesses often require strong value propositions and unique data advantages to win, we expect a small group of data businesses to emerge and dominate industry-specific markets over the next decade. Those who come later may find it difficult to catch up.

Managing data security, privacy, and intellectual property rights

Data security, privacy, and ownership are significant concerns for any leader. But the potential impact these risks can have on a data company’s business models and ability to expand raises the stakes significantly. As a result, leaders should ensure that their business, technology, cyber, and legal teams collaborate often and early on assessing the opportunities.

Following are four issues that will require early attention:

• Understanding the rights you—and others—have related to data: What are the sources of your data—first parties, vendors, and so on—and how was the data acquired? Are there limits to how you may use the data or concerns about whether it is derived from underlying data sets that have issues (for example, training data for generative AI that may be copyrighted material)? Data businesses should assess their data and closely follow the evolving conversation over data rights, particularly as innovative technology collides with, and spurs, these conversations.

• Developing consistent data privacy principles at inception: Identifying how the business will collect, use, retain, delete, and protect personal data before products launch can shield data businesses from potential setbacks and time-consuming hurdles when introducing new products and features, as well as uphold trust with customers.

• Examining and tracking local laws: Varying country, regional, and sector laws may influence how a data business collects, shares, processes, stores, secures, and manages data. Additionally, some jurisdictions have more clearly defined regulations than others, which leads to greater predictability. Leaders will need to consider their appetite for the uncertainty and risk of operating in areas where regulations are not so clearly defined.

• Prioritizing data governance and security: This is typically the “weakest link” that prevents data businesses from scaling. Data governance and security capabilities, such as quickly identifying and resolving data issues and effectively managing data access and entitlements, are foundational to delivering a quality product to a growing user base.

Building a valuable data set and associated insights can take time, giving those that move first a sizable advantage in seizing untapped market opportunities. But institutions that enter this market should have a unique data set that addresses an unmet customer need and the right capabilities to scale their product. Those who do may not only build a scalable and profitable business but also potentially create an enduring brand.

AI gives telcos another chance to reinvent themselves. The goal is to become AI-native organizations that embed AI into every aspect of the business to help drive growth and renewal. If telcos act quickly, they can lead the way in deploying both generative AI (gen AI) to improve the customer experience and cut costs and analytical AI to optimize their back-end operations and infrastructure. Many telcos have already begun to implement gen AI, deriving significant cost savings in areas such as marketing, sales, and customer service. McKinsey research shows that gen AI could drive significant EBITDA gains for telcos, with returns on incremental margins increasing three to four percentage points in two years, and as much as eight to ten percentage points in five years.¹

However, telcos cannot transform themselves into AI-native companies without also focusing acutely on responsible AI (RAI), which is the practice of deploying AI in ways that are ethical, safe, transparent, and compliant with regulations. In the highly regulated telecom industry, RAI frameworks that govern accountability and transparency are critical to gaining consumer trust, protecting sensitive data, and safeguarding against security threats. All this makes RAI more than just an ethical exercise for telcos. It is also a business imperative.

To showcase the importance of RAI, we conducted an analysis of 100 critical AI use cases that telcos could develop. For each use case, we indicated the optimal level of RAI maturity, considering the inherent risks. For example, an advanced level of RAI would reinforce a use case that manages customers’ personal information. Our analysis indicates that telcos implementing the most advanced RAI practices could deploy use cases that collectively capture up to $250 billion in value worldwide by 2040, 44 percent of the full industry-wide value created by AI during that period

How exactly can RAI create value? For starters, just like all AI deployments, RAI can significantly improve business processes and streamline technology integrations to reduce costs. Effective RAI can also strengthen brand reputation, with higher customer acquisition and retention often propelling revenue growth. In addition, RAI can help reduce commercial and reputational risks across an organization’s full suite of AI tools and applications, ensuring that they perform at the highest levels of accuracy. For example, RAI practices could help ensure a company’s customer service chatbot doesn’t use biased, incorrect, or sensitive language and that it never recommends a competitor’s product or service.

In this article, we outline how telcos can design and implement an RAI framework that could generate significant bottom-line impact. A strong RAI framework includes maturity models that telcos can use to assess their current strengths and weaknesses, as well as best practices to move through foundational, evolving, emerging, and advanced stages of RAI implementation. As they deploy such a framework, telcos will clarify their individual RAI road maps, including how to structure and implement governance, technology, and operating models. RAI frameworks can ensure that a telco’s AI deployments remain aligned with revenue and business goals.

Industry-standard RAI frameworks are rare

Telcos can benefit from RAI in multiple ways: better business outcomes, competitive advantage, sustainable growth, increased customer trust, enhanced operational efficiency, stronger talent attraction, and, of course, financial gains. Forward-thinking telcos recognize that robust RAI governance serves as a set of “good brakes” that enable them to “drive faster” to harness the full potential of AI while mitigating risks.

Based on our interviews with senior leaders at telcos worldwide, few are currently at the advanced stage of RAI deployment, with a majority still at the foundational or evolving stages. One of the biggest roadblocks to telcos deploying RAI is the lack of industry standards. Telcos want to deploy RAI, but there is no single agreed-upon framework to aid them in their journeys. Thus, instead of proactively adopting RAI, telcos are reactionary, adding piecemeal governance standards as new regulatory requirements emerge. This approach helps telcos avoid legal and financial repercussions but does not result in the type of cohesive and strategic RAI deployments necessary to fuel innovation.

Many governments have proposed or passed legislation to ensure that AI deployments are fair, transparent, accountable, and secure (Table 1). International organizations have proposed global policies for RAI, but none of these policies have been adopted on a wide scale (Table 2). And none of these regulations or policies are specific to the telecom industry.

While telecom industry associations are making progress on defining RAI standards, there are still barriers to achieving near-term success:

• Limited leadership from central organizations. Telecom industry associations and standard-setting bodies can show more leadership in advancing RAI practices. While some organizations actively promote RAI, few have developed comprehensive frameworks or provided clear guidance to their members. This gap hinders telcos from adopting best practices and achieving consistent standards of AI responsibility.

• Limited availability of RAI industry benchmarks. The absence of RAI benchmarks in the telecom industry creates a significant challenge for telcos. Benchmarks serve as reference points that allow companies to evaluate their performance relative to industry standards and identify areas for improvement. Without these benchmarks, telcos lack metrics to gauge their progress in implementing RAI. This gap complicates efforts to foster transparency, as key stakeholders—including regulators, consumers, and partners—have no clear standards against which to measure a telco’s AI initiatives.

RAI frameworks for telcos have four characteristics

Despite a lack of clear standards for their industry, telcos have a strong desire to implement RAI. In our interviews , the majority of leaders expressed interest in creating and deploying RAI frameworks tailored not just to the telecom industry but to their individual businesses. Most hoped to begin their RAI journey with maturity models that assess where they stand and define the specific steps needed to become advanced users of RAI. Telcos see maturity models as a level-setting exercise to inform their strategic plans. They want to deploy easy-to-use modeling tools to score their levels of RAI readiness and translate these findings into executive-level summaries with calls to action. Unfortunately, few of the leaders we interviewed said they currently use RAI maturity models, mostly because of the tools’ limited availability. Telecom industry groups are working to define RAI maturity models specific to telcos, but it is still early days. For example, the Global System for Mobile Communications Association (GSMA) only recently created a tool for telcos to measure their RAI maturity.

What specific characteristics should an RAI framework for telcos include? Based on our interviews, a strong RAI framework for the telecom industry might encompass four key characteristics:

1. Industry-specific maturity models. These tools help telcos assess their RAI readiness and define specific benchmarks for each level. The models consider telcos’ unique goals and challenges with AI in light of a highly competitive market, interconnected networks, and extensive exposure to consumer data.

2. Clear RAI guidelines. These building blocks offer a comprehensive overview of the various elements that comprise an RAI strategy, including governance, risk management, data quality, and ethical guidelines.

3. Best practices. These practical strategies show telcos how to implement RAI effectively, including proven practices that advanced telcos have already successfully adopted and measured.

4. Progress metrics. These measurement guidelines outline practical steps telcos can follow to improve their RAI capabilities and progress through each stage of maturity: foundational, evolving, performing, and advanced.

Industry-specific maturity models for assessment and goal setting

An effective RAI framework should include an easy-to-use maturity-modeling tool to help telcos fully understand their baseline AI readiness and identify opportunities for growth and improvement. Maturity models help telcos capture their full AI potential at every stage of deployment.

Operators just starting on their RAI journey can use maturity models to establish and measure essential foundational requirements. These include adopting core RAI principles, defining key roles and responsibilities, and setting up initial governance structures. Foundational requirements also include establishing a code of ethics for AI, appointing a chief AI officer, and creating an AI governance board. Companies in this foundational stage of RAI adoption are mostly looking to enhance specific aspects of their operations, such as improving operational efficiency or automating routine tasks like customer service.

On the other end of the spectrum are companies in the advanced stage of RAI deployment. These telcos use AI to create high-impact, customer-facing use cases and integrate AI deeply into their strategic decision-making processes. Examples include using AI to create personalized customer experiences, to analyze vast amounts of data for strategic insights, or to spur innovation in product development. Thus, maturity models should integrate benchmarks and best practices for advanced users, including sophisticated risk management frameworks, comprehensive governance models, and continuous monitoring and improvement processes. For companies at the advanced stage, maturity models may also include AI auditing processes to ensure transparency and accountability.

Clear RAI guidelines to define overall strategy

An effective RAI framework outlines every step of a telco’s AI strategy and long-term road map. It includes definitions for governance, risk management, data quality, and ethics, with strategic and operational best practices to advance these policies at each stage of RAI maturity. The following are the essential components of an RAI framework.

Strategy. This defines the vision and principles for RAI governance in alignment with the organization’s values and strategic goals. Here is what a robust strategy should include:

• Vision. A clear articulation of what RAI means for the organization and how it aligns with the company’s broader mission

• Principles. Foundational ethical guidelines that steer AI development and deployment, ensuring fairness, transparency, accountability, and inclusivity

• Alignment. Guidelines to ensure that the RAI strategy is consistent with the organization’s strategic objectives

• Regulations. Rules to ensure adherence to local and international standards to mitigate compliance risks

Enablers. These activate well-defined best practices that are integral to a comprehensive RAI road map. Key features include the following:

• Tools. An inventory of the responsible AI tools the organization will use for model validation, bias detection, and interpretability to ensure ethical AI development

• Training. A plan for continuous education and training to help employees understand AI’s ethical implications and technical aspects

• Change management. A structured approach the organization will use to transition individuals, teams, and organizations toward RAI practices

• Communication. Clear, cascading communication channels to ensure that everyone in the organization is aligned with the RAI strategy and principles

Operating model. This helps ensure that the right talent, governance structures, team composition, and processes are in place to allow companies to implement RAI across all business activities. Critical elements include the following:

• Talent. The recruitment and development of professionals with the necessary skills in AI, ethics, and governance

• Governance. Robust governance structures that define roles, responsibilities, and decision-making processes related to RAI

• Team structure. Formation of cross-functional teams that include data scientists, ethicists, legal experts, and business leaders

• Processes. Implementation of standardized RAI development, deployment, and monitoring procedures to ensure consistency and accountability

• Culture. The creation of a culture of ethical awareness and responsibility regarding AI, which encourages employees to speak up about potential issues

Risk. This emphasizes the importance of proactively monitoring and mitigating risks associated with AI and involves the following:

• Measurement. Development of metrics and KPIs to evaluate the performance and risks of AI systems

• Monitoring. Continuous observation of AI models for signs of bias, errors, or other issues through techniques such as risk management and third-party solution auditing

• Reviews. Implementation of rigorous review-and-challenge processes such as “red teaming” (simulating attacks to identify vulnerabilities) and “war gaming” (stress testing models under hypothetical scenarios)

• Reporting. Regular documentation and communication to stakeholders to ensure transparency and to facilitate informed AI decision making

Best practices to deploy RAI in the telecom sector

An RAI framework for the telecom industry should provide specific best practices for each of the four key components described above. These best practices can help telcos apply general principles of RAI in a manner that acknowledges the industry’s unique challenges and opportunities, such as using AI for network optimization or customer churn prediction. A comprehensive RAI framework provides best practices for each maturity level—foundational, evolving, performing, and advanced—creating a road map that helps telcos advance from one level to the next.

What follows are sample best practices telcos could adopt at the foundational level of RAI deployment, though a full framework would provide a greater number of best practices and tailor them more specifically to an individual telco’s situation:

• Strategy:

o Vision. Write a high-level statement articulating the organization’s commitment to RAI that is aligned with the broader mission and serves as a guiding light for all AI activities.

o Principles. Define early ethical principles that provide a clear framework for RAI, touching on fairness, transparency, accountability, and inclusivity.

o Alignment. Develop a road map to incorporate AI principles into the organization’s overall business goals.

o Regulations. Conduct initial research to understand the regulatory requirements at both the national and international levels.

• Enablers:

o Tools. Find and adopt a first set of RAI tools to help with model validation, bias detection, and interpretability.

o Training. Conduct an initial training program to educate employees about AI’s ethical implications to build awareness of RAI practices across the organization.

o Change management. Start basic processes to help the organization adapt to RAI, such as creating a simple road map to describe the transition process.

o Communication. Choose channels—for example, a newsletter, blog, website, or social media platform—to disseminate information to employees and external stakeholders about the company’s RAI principles.

• Operating model:

o Talent. Create a plan to obtain the necessary talent to deploy RAI, including required new hires and plans for upskilling existing employees.

o Governance. Set up an initial governance structure that defines roles and responsibilities for key stakeholders and outlines basic procedures.

o Team structure. Take a high-level inventory of existing employees—including data scientists, developers, IT staff, legal experts, product managers, and business leaders—who could potentially join a cross-functional team to support RAI initiatives.

o Processes. Research standardized procedures for AI development, deployment, and monitoring, and introduce an initial set of unified procedures.

o Culture. Create an initial internal communications campaign to introduce the concept of RAI.

• Risk:

o Measurement. Develop basic metrics and KPIs to evaluate the performance of early RAI systems and set up initial measurement frameworks.

o Monitoring. Establish basic processes to continually observe AI models to detect signs of bias, errors, or other issues.

o Review. Create initial review-and-challenge processes, including plans for red teaming and war gaming.

o Reporting. Set up reporting mechanisms to document and communicate RAI findings, including basic reports to keep stakeholders informed about the state of AI initiatives.

Progress metrics to evolve from a foundational to an advanced maturity level

An effective RAI framework should offer a structured pathway for telcos to improve their RAI maturity over time. At each maturity level, the framework should include specific progress metrics a telco needs to meet to advance to the next level.

For example, at the foundational level, one progress metric might be: “Share the company’s RAI vision with all employees through an internal communications campaign and test their knowledge of the program through a feedback mechanism.” At the evolving method, a metric might be: “Finish a multipart risk training program for the RAI cross-functional team.” And at the performing level, a metric might be: “Develop and launch an RAI innovation lab to foster continuous improvement and experimentation.”

Each telco will have different goals for RAI deployment, so their progress metrics will differ based on the unique objectives defined in their initial road maps.

Telcos with structured RAI practices are not just leaders from an ethical standpoint; they also stand to generate billions of dollars in additional value. Investing in RAI may position today’s beleaguered telcos for a competitive edge in the next decade, providing a new avenue for them to compete alongside technology companies. By prioritizing RAI, telecom operators can capture the full potential of AI for their businesses and build trust with customers, leading to new innovations and new revenues. Emphasizing RAI can also help telecom companies attract and retain the best talent, fostering a culture of continuous improvement. Put simply, implementing AI responsibly makes good business sense.

How Manufacturers Can Become Data Champions

Today’s manufacturing companies have a great strategic opportunity: the chance to use advanced data gathering and analytics to drive productivity, improve customer experience, and foster societal and environmental benefits. Those that embrace the opportunity are turning their supply chains into hyperconnected value networks for sharing data across company boundaries and collaboratively managing the flow of goods around the world.

One catalyst for this change is uncertainty in the economy and the world at large brought on by megatrends such as rising global temperatures and COVID-19. Increasingly, business leaders see the innovative use of data as a stabilizing force in their industries. The benefits include increased productivity, improved quality, reduced environmental impact, and more effective innovation. 

Although interest in such advanced methods is high, many companies do not yet have the technological or organizational enablers in place to achieve data excellence. In a BCG survey conducted in 2019, 72% of manufacturing executives said that they considered advanced analytics to be important. But only 17% said that they had captured satisfactory value from it. 

A Data Excellence Framework

Manufacturing Data Excellence Framework focuses on the three main challenges that manufacturers say hold back their efforts to implement data and analytics solutions at scale: a lack of data applications, insufficient technology, and organizational hurdles.

To meet these challenges, manufacturers have developed and deployed data and analytics applications in various business functions, such as new product launches, engineering, logistics, maintenance, and health and safety. In support of these applications, they have invested in technological enablers—the digital and analytic foundation necessary for capturing data, comprehensively processing and visualizing it, and protecting it from intrusion and cyber theft. They have also introduced organizational enablers—managerial practices and processes related to functions such as legal compliance, skill building, and governance.

Companies can use applications, technological enablers, and organizational enablers to assess their current proficiency in manufacturing data excellence and to develop a plan for further progress. In 2021, ten leading manufacturers aligned with the WEF Platform for Advanced Manufacturing and Value Chains used the framework in exactly this way. Analytics at 18 plants associated with these companies yielded a composite benchmark of the manufacturers’ self-assessments. 

Stages in the Journey

Our research suggests that the journey toward data excellence comprises three successive stages of digital prowess: actionable insights, predictable outcomes, and self-optimizing systems. Each stage features an increased degree of collaboration and data sharing across company boundaries, as well as a higher level of analytic prowess.

Actionable Insights. In the initial stage, companies use operational data to track the performance of current processes and production lines, deriving actionable insights for optimizing production and reducing errors. Sensors in factories and distribution centers, along with core software applications such as enterprise resource planning, gather the data, which is then presented in the form of reports and digital dashboards. Managers use this data to discern patterns and trends and to inform decision making. 

Predictable Outcomes. The second stage of the journey involves ensuring predictable outcomes. Machine learning (ML) algorithms receive large sets of operational performance data from historic and real-time sources, which they use to train themselves and to predict the manufacturing system’s future behavior, thereby providing guidance on ways to prevent mishaps and prepare for growth. 

Self-Optimizing Systems. In the third stage of the journey toward data excellence, the applications take autonomous action. Self-learning algorithms get smarter as their experience with historical and real-time data grows. At this stage, companies might develop a digital twin—a simulation of a specific production asset or manufacturing plant. This digital representation can correct and optimize itself in real time, responding to changes in factors such as employee availability and scheduled production assets. Fully digitized plants of this sort often have significantly lower environmental footprints, too, because they can reduce resource consumption or improve efficiency in response to fluctuations in demand. 

Companies that master the use of data, successfully deploying advanced technological and organizational enablers in data applications across their ecosystems, are considered data champions. They generate consistently high scores on measures of productivity and customer experience, as well as of society and the environment.

Accelerating Progress

Almost all manufacturing companies have started their journey toward data excellence, but so far none have reached the end of the path. Because of the ever-evolving nature of technology and the ongoing introduction of new practices, companies must continuously innovate and improve. To accelerate this process, the Manufacturing Data Excellence Framework, can help companies make progress.

The framework and the DAI survey provide a solid understanding of a company’s status. Decision makers can also use them to compare their results to peer-group or industry benchmarks, and thus set transformation and growth priorities.

Companies should then develop or obtain the specific applications they need to raise their degree of mastery, either at a local (facility-based) level or at a global (company-wide) level. They can also set up community-style interactions among collaborative organizations, learning from one another’s best practices and innovating together. These are critical steps to take in moving toward a shared goal of manufacturing data excellence—a world of globally connected business ecosystems that continually improve their performance, their environmental record, and the value of what they produce.

• Mixed Recovery Trends :While deal values in private equity (PE) rebounded by 20% in 2024, transaction volumes remain below 2021- 2022 levels, with holding periods elongating.
• Fundraising & Concentration: PE fundraising grew 10%, yet remains 20% below 2021’s peak. There’s growing concentration as top PE funds dominate capital allocation.
• Value Creation Imperative: With heightened market uncertainty, value creation strategies—beyond traditional levers—are necessary for return generation.
• Private Credit Expansion: Direct lenders held 57% of buyout loan volumes in early 2024, driven by high returns and bank lending constraints.
• Talent Challenges: Retention issues persist due to low carried interest and slow fundraising, while portfolio companies struggle with talent shortages.

Strategic considerations for ceo’s in pe

• Exits and Liquidity: Limited exits and elongated holding periods are a global issue, requiring liquidity solutions across different regions.

• Secondary Market Growth: Growing in Europe and APAC, the secondaries market is providing avenues for monetization and exits globally.

• Sector-Specific Opportunities: Infrastructure, energy, and digital assets are becoming attractive investment areas in Europe, Asia, and Latin America.

• Geopolitical Tensions: Ongoing geopolitical uncertainties (e.g., in Europe and Asia) are key risks that global PE firms must navigate.

• Regional Real Estate Risks: Real estate sectors in Asia and Europe face unique risks, similar to the US, particularly in commercial properties.

• Regional Consolidation: Global capital concentration is driving consolidation trends, with large funds dominating in North America, Europe, and Asia.

Tech-driven value creation across global markets

• 10-45% AI-Driven Sales Growth: Consumer businesses leveraging AI globally see up to 45% sales growth, driven by better customer insights.

• Up to 60% IT Cost Reduction: Cloud migration strategies achieve up to 60% cost savings for portfolio companies across regions.

• Global Cybersecurity Spend Rising: PE firms globally are increasing cybersecurity investments, with 25% annual growth forecasted to mitigate rising threats.

• Data Analytics as Revenue Driver: Global portfolio companies using data analytics to create new products have seen 15-20% margin increases.

• Customized Tech for M&A: Strategic buyers in APAC and Europe are prioritizing tech scalability in acquisitions, driving up demand for proprietary technologies.

Key Risks in Global PE Technology Initiatives

• $4.7 Trillion Cybersecurity Threat: Global cybersecurity breaches projected to cost $4.7 trillion by 2025, creating significant risks for underprepared portfolio companies.

• Cultural Resistance: 40% of global tech transformations in PE firms fail due to resistance in regional markets, especially in emerging economies.

• Governance Failures: Insufficient oversight globally has caused 30% of tech initiatives to miss ROI targets, particularly in high-growth regions.

• Regulatory Pressure: Rising regulations in Europe and APAC are increasing compliance costs, impacting 20% of ongoing tech projects.

• Misaligned Investments: PE tech transformations misaligned with regional exit strategies result in 15-20% valuation loss at exit.

• Tech Talent Gaps: Global tech talent shortages have increased by 35%, impacting implementation timelines and cost optimization efforts.

Leveraging Technology for Competitive Global Advantage

• Global Cost Efficiency: Rationalizing global IT operations and leveraging offshoring can deliver cost savings across different regions.

• Proprietary Tech for Global Firms: Building region-specific proprietary technologies can create competitive advantages and drive higher exit multiples.

• Capital Optimization Across Markets: Global firms should manage capital deployment efficiently across markets, especially in technology investments.

• Mitigating Global Risks: PE firms must navigate global tech and cybersecurity risks to ensure successful exits in key markets.

Strategic Takeaways for Global PE in 2024

• Capitalizing on Distressed Assets Globally: As debt issues emerge in key regions, firms should prepare to capitalize on distressed opportunities.

• Digital-First Global Approach: Leveraging digital tools and automation across global markets will drive efficiency and enhance deal flow worldwide.

• Diversifying Across Regions: Infrastructure, energy, and real estate sectors offer opportunities across developed and emerging markets.

• Agility in Global Value Creation: Firms must adopt region- specific strategies to optimize value creation amidst global market volatility.

Conclusion:

Advisory Corp is a leading firm serving traditional PE firms as well as Family Office PE / Holding Company PE divisions. We deliver Transaction Advisory, Investment Analytics, and Fractional CFO services. Our end-to-end transaction support covers M&A, capital restructuring, and strategic business reforms. We specialize in financial analysis and portfolio-level decision-making, ensuring value creation for stakeholders.

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