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Generative AI Strategies: Key Approaches for Entrepreneurs and Executives to Boost Competitiveness

Immagine del redattore: Andrea ViliottiAndrea Viliotti

Generative AI strategies represent a critical opportunity for leaders who seek faster innovation, greater cost efficiency, and more robust customer engagement. In “The CEO’s Guide to Generative AI: What You Need to Know and Do to Win with Transformative Technology, Second Edition,” authors Jonathan Adashek, Salima Lin, and Mohamad Ali, in cooperation with key figures from IBM, analyze how businesses can embed these Generative AI strategies into daily operations and strategic initiatives. Their work draws on global data, managerial studies, and practical guidance to demonstrate how Generative AI can transform organizational structures, strengthen competitiveness, and spur innovation across multiple fronts. The study highlights four main drivers of value: product and service development, cost optimization, business growth, and an enhanced relationship with both customers and employees.


A Strategic Overview of Generative AI Strategies for Entrepreneurs, Executives, and Technical Teams

Generative AI stands out as a powerful catalyst for competitiveness. According to IBM’s research, it can quickly accelerate the creation of new products and services, optimize costs through intelligent automation, expand reachable markets, and upgrade the overall customer experience. Leaders who integrate Generative AI into their business plans see it as pivotal to maintaining a competitive edge. The data shows how strategic implementation can:

  • Accelerate product development by up to 31% in certain industries, opening possibilities for highly customized offerings and better profit margins.

  • Deliver substantial cost savings thanks to smart automation, such as a 40% drop in unplanned machine downtime and faster handling of IT and administrative processes, where ticket-resolution times can be cut by 65%.

  • Broaden market presence, with 85% of surveyed executives recognizing Generative AI as a lever for reaching new customer segments and offering advanced interaction channels powered by language-based models.

  • Strengthen governance and cross-department collaboration through well-structured hybrid cloud architectures and dedicated roles (AI ethicist, prompt engineer), ensuring reduced risks and fewer biases.


To maximize Generative AI’s potential, companies must pursue an integrated plan that encompasses workforce upskilling (some estimates suggest 35% of the global workforce needs retraining) and an unwavering focus on security, data protection, and regulatory compliance. Success hinges on uniting transformative technology, human expertise, and strong data ethics.

Generative AI Strategies
Generative AI Strategies

How Generative AI Strategies Are Transforming Business Models

Many companies struggle to achieve sustainable growth when decision-making is rigid, processes are siloed, and information flows are fragmented. IBM’s research underscores how Generative AI can accelerate transformation by breaking down organizational barriers. As markets grow more complex and demand rapid responses, executives find they must rethink traditional governance structures. Interview data from over 10,000 CEOs and C-Suite leaders reveals that most feel pressured to adapt their business models, recognizing that older compartmentalized practices are insufficient.


A major advantage of Generative AI strategies is the capacity to handle vast volumes of data from diverse sources. By synthesizing text, code, images, and other unstructured inputs, Generative AI enables dynamic data-driven workflows. This fosters quick and accurate decisions, leading to lower operating costs and shortened time-to-market. IBM’s evidence points to an average 31% performance improvement in certain sectors—especially B2B—through the adoption of machine learning and deep learning approaches. Notably, 88% of surveyed executives believe both traditional AI and Generative AI are critical for product and operational innovation, while roughly 85% see them as an effective means to expand market reach.


Generative AI strategies also impact industries once thought resistant to AI-based solutions, such as heavy manufacturing and logistics. By automating maintenance schedules, some businesses report up to a 40% decrease in unplanned downtime, highlighting the transformative potential of these strategies across multiple sectors. Additionally, sophisticated modeling of supply chain data allows for anomaly detection, predictive alerts on potential delays, and better resource allocation. In environments where many variables shift unpredictably, the speed and detail of these insights can translate to significant cost and reputational benefits.


Executives who seize the advantages of data-oriented and Generative AI-driven strategies can pivot more swiftly when confronted with market volatility or geopolitical challenges. Rather than chasing incremental gains, businesses can redesign workflows to sharpen growth strategies. Scenario modeling using Generative AI reveals both risks and openings before they become obvious to competitors. In this context, governance structures must be reimagined to unite Strategy, M&A, Technology, and Communications under shared objectives. Cloud-based infrastructures with agile “hub and spoke” models help coordinate AI solutions while containing expenses. The recommendation is to start small, demonstrate quick successes, and then scale across the broader partner and client ecosystem, all while carefully managing resources.


Hybrid Cloud and Supporting Tools for Generative AI Strategies

The effectiveness of Generative AI depends heavily on the underlying information systems. Hybrid cloud architectures—which combine private and public cloud environments—are often key to achieving scalable, cost-effective solutions. Companies can tailor workload placement for optimal data security, latency reduction, and processing capacity. Drawing from IBM’s substantial experience in AI deployments, many organizations adopt a hub-and-spoke structure to coordinate Generative AI models and cloud services, preventing fragmentation and uncontrolled growth in operational costs.


Large Language Models (LLMs) can require immense compute resources. Average spending for compute is projected to grow by 89% between 2023 and 2025, raising costs and environmental impact. Yet targeted models, combined with hybrid cloud strategies, help organizations manage financial and sustainability concerns. FinOps practices—where finance and operations teams collaboratively optimize technology expenses—are crucial to monitor cloud usage and move workloads on-premises when it makes economic sense. This approach ensures a stable balance of performance, cost, and eco-friendly operations.


Interestingly, not every AI model must be massive in scope. Smaller, specialized models can be more than sufficient for tasks like demand forecasting within fragmented production chains. By pairing internal historical data with external market information—often collected from web sources—companies gain near real-time forecasting capabilities. This precision reduces waste and improves inventory management. The text also warns about the importance of data governance: if corrupted or malicious data contaminates the training set, the model’s outputs could become misleading, risking reputational or financial harm. Thus, a robust governance strategy must cover every phase of the data life cycle: acquisition, cleaning, processing, validation, and continuous monitoring.


Ultimately, the IT infrastructure should make it easy to choose the right AI solution for each business need, coordinating systems and data sources. Proper orchestration of information flows through hybrid cloud environments can amplify returns on investment over a five-year horizon—an essential point for executives weighing initial deployment costs against long-term gains.


Rethinking Core Processes with Generative AI Strategies

Generative AI offers a path beyond minor process improvements, enabling deeper redesigns of core business functions. Many firms automate existing procedures but rarely explore broader restructuring that could yield more impactful benefits. For leaders who aim to accelerate growth, identifying processes where data and AI can remove operational bottlenecks is vital. Such transformations lower maintenance expenses and enhance both customer and employee experiences.


Customer service is a prime example. Generative AI can automate responses to frequently asked questions in a more natural, personalized tone, freeing human agents to manage complex queries that require empathy and nuanced understanding. Surveys of CEOs and C-Suite members indicate that 64% plan to adopt generative chat or voice bots in the next two years, aiming to boost customer satisfaction and encourage more cross-selling opportunities. Yet this shift demands rigorous oversight to ensure responses remain accurate and well-calibrated.


Supply chain operations similarly stand to benefit. Generative AI can spot hidden correlations in large sets of data, predicting disruptions before they occur and recommending alternative sourcing routes. Compared to older analytics systems, these models can incorporate non-traditional inputs—like weather forecasts or geopolitical news—into the same predictive framework. Some companies have cut production-plan update times by up to 80%, lowering inventory costs and avoiding missed shipments.


Asset management in industrial settings also undergoes substantial improvements. Beyond well-known IoT sensors and predictive maintenance tools, Generative AI can combine data on operating parameters, historical maintenance logs, and image analyses to suggest optimal repair schedules, thus curtailing downtime and extending equipment lifespans. At the same time, this continuous data-driven approach cuts resource consumption, with tangible financial and environmental benefits.


IBM’s research highlights how data generated at customer touchpoints—feedback, reviews, or complaints—can inform internal departments like Marketing or Product Development. The challenge is coordinating these insights. Leaders must adopt an integrated approach so that Generative AI does not remain confined to a single department. Measuring the real impact is best done with key metrics tracking cost reduction, product development speed, and strategic direction for new product lines. Success depends on a roadmap that encourages cross-functional collaboration rather than isolated AI experiments.


Ensuring Ethical Governance and Skills for Generative AI Strategies

Technical infrastructure alone does not guarantee a successful AI integration. The human side—skills development and ethical oversight—is equally critical. Training must expand beyond specialized teams, since research estimates that up to 35% of the global workforce needs skill upgrades to use AI effectively. This transformation changes AI from a niche tool to an everyday asset across the organization.


New positions such as prompt engineers and AI ethicists are emerging in response to the growing influence of Generative AI. A prompt engineer ensures model outputs align with business objectives, while an AI ethicist enforces fairness and avoids discriminatory outcomes. Because Generative AI often deals with sensitive data, companies must ensure robust privacy safeguards. The text notes that 63% of executives view accidental data exposure as a top concern, signaling that secure data handling procedures and strong encryption standards are urgent priorities.


Regulatory frameworks like the European AI Act are also on the horizon, mandating impact assessments and traceability for AI systems. Business leaders must anticipate legal requirements by setting clear principles and testing procedures that reveal biases or potential misuse. Some organizations form internal AI ethics boards, encouraging a socially responsible approach that protects corporate reputation. Verifiability of outputs—ensuring that AI recommendations are correct and well-supported—remains a cornerstone of trustworthy Generative AI. Even the most plausible results can be incomplete or flawed, making ongoing audits essential. Ultimately, accountability still lies with managers, who must not relinquish critical decisions to opaque algorithms.


Another critical dimension involves external collaboration. Generative AI thrives within ecosystems that include technology partners, startups, and universities, often employing open innovation strategies. For companies exploring this approach, robust governance frameworks clarify ownership of intellectual property, how benefits are shared, and who maintains models over time. Without a cultural shift that embraces structured oversight and open collaboration, efforts to scale Generative AI may stall.


Risks and Opportunities: The Future of Companies Embracing Generative AI Strategies

The concluding section of the study highlights multiple metrics showing that companies fully adopting Generative AI can achieve significant operational efficiencies and revenue growth—potentially over 35% in sectors offering personalized, higher-margin products and services. Likewise, AIOps (AI for IT operations) has cut technical support times by about 65% while spotting imminent network bottlenecks, reducing downtime and technology spending.

Generative AI can also unlock global market opportunities, thanks to automatic content creation in multiple languages and real-time localization. Yet the text warns against scattershot investments; real gains emerge when companies target use cases with clear strategic benefits. It is equally important to manage risks like model drift—the gradual decline in model accuracy as data or conditions change—and cybersecurity threats, where adversaries exploit AI to create deceptive messages or synthetic voices.


Leaders who move swiftly to incorporate AI into long-term planning will likely reap the highest competitive advantage. However, building out hybrid infrastructures and establishing data governance takes time and capital. Once these foundational pieces are in place, organizations can amplify collaboration between humans and AI across every function. While traditional AI solutions have already improved repeated or statistical tasks, Generative AI adds a level of creative potential that carries both new benefits and new risks. An iterative approach—guided by continuous monitoring and expert oversight—remains the safest path in an ever-changing business landscape.


Conclusion: Embracing Generative AI Strategies for Sustainable Growth

This study on Generative AI underscores its potential to reshape business beyond short-term gains. The hallmark of this technology is its ability to create content, forecasts, and simulations at exceptional speed, opening the door to a fundamental rethinking of business models. Simultaneously, it challenges governance structures at all levels. Far from simple chatbot experiments, Generative AI can unify core functions like supply chain management, industrial maintenance, IT security, and customer experience.


Past generations of AI showed how analytics could optimize certain tasks, but Generative AI promises a more integrated approach, merging insights from multiple data streams. Nonetheless, the outcome depends on setting up a robust hybrid infrastructure and regularly refining the models to avoid cost overruns and misalignment with business goals. Leaders focused on product or service differentiation can seize new opportunities, such as personalizing offerings at scale or shrinking time-to-market windows.

Of particular importance is the human factor. While efficiency is vital, the ultimate objective should be broader value creation. Introducing AI into any production or service chain requires investment in training, changes in organizational hierarchy, and a firm ethical code that protects individual rights. Unlike prior technologies, Generative AI can directly impact social dynamics, so executives must manage reputational and legal risks with care.


Given how fast the industry evolves, CEOs must balance caution with bold innovation, staying flexible enough to pivot if market conditions shift. Generative AI stands out for its creative capabilities, a step beyond what earlier AI could achieve. This capacity brings fresh challenges, including the potential for mistakes if model configurations are flawed. The path forward demands well-structured, adaptable plans supported by dedicated experts.

For those seeking to implement Generative AI strategies, technical and ethical sustainability are twin imperatives. Preserving customer trust and forging open partnerships can transform Generative AI into a true strategic level. Ultimately, the authors encourage companies to embrace change, designing these technologies for tangible benefits to both the enterprise and society, while carefully managing risks. By doing so, business leaders can reshape their organizations for the long term, standing out in a competitive environment that prizes swift, data-driven innovation.


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