The technology industry stands at an inflection point where artificial intelligence (AI) is no longer a futuristic concept but a core driver of business transformation. Companies that effectively implement AI solutions must now demonstrate clear returns on investment (ROI) to justify their expenditures. To turn AI’s promise into reality and revenue, organizations must adopt AI-first strategies, optimize customer experiences, and realign business models with outcome-based pricing. Additionally, AI must be embedded within cybersecurity frameworks and legal functions to ensure compliance and risk mitigation.
AI as a revenue driver
As AI use cases and copilots become mainstream, businesses face mounting pressure to prove AI’s tangible value. The experimentation phase has ended, and companies must now showcase AI’s ability to enhance operational efficiency and customer experience. According to James W. Brundage, EY Global and Americas Technology Sector Leader, 2025 will be a pivotal year for the tech industry to deliver AI-driven ROI (EY, 2024). To meet these expectations, businesses should develop frameworks that measure the financial and operational impacts of AI implementations.
Agentic AI and customer experience
Agentic AI, a significant evolution in automation, enables AI systems to perform complex tasks independently, reshaping how businesses interact with customers. Unlike generative AI (GenAI), which relies on prompts, agentic AI autonomously executes sequences of steps without human intervention. This technology has the potential to transform customer service, marketing, and dynamic pricing models by analyzing vast datasets to personalize offerings. Geoff Vickrey, EY Americas Consulting Technology Sector Leader, notes that early adopters of agentic AI will gain a significant competitive advantage (EY, 2024).
Outcome-based pricing models
As customer expectations shift, traditional subscription models are under scrutiny. Customers demand pricing that aligns with the value they receive from AI-driven solutions. The transition to outcome-based pricing models ensures businesses charge based on the tangible benefits delivered rather than access or usage. However, this transition requires careful planning, data analysis, and stakeholder engagement. Barak Ravid, EY Americas Strategy and Transactions Technology Sector Leader, emphasizes the importance of assessing product value relative to acquisition and delivery costs to effectively communicate financial outcomes (EY, 2024).
AI-first operating models
Companies must rethink their business models and processes to maximize AI’s potential. Integrating AI into every facet of an organization fosters cross-functional collaboration and operational agility. Traci Gusher, EY Americas AI and Data Leader, suggests that rather than bolting AI onto existing models, businesses should redesign their operations from the ground up to stay competitive against AI-born startups (EY, 2024). AI’s capabilities extend to supply chain management, where AI agents can predict disruptions, optimize logistics, and enhance decision-making processes.
Data consolidation and AI scalability
A robust data strategy is crucial for AI adoption. Many organizations struggle with legacy systems that hinder AI’s full potential. Companies must modernize their data architecture to ensure scalability, trust, and usability. While large language models (LLMs) have dominated AI discussions, small language models (SLMs) and edge computing are gaining traction for real-time analytics. Businesses must reassess their cloud strategies and determine the best mix of public, private, and edge computing for their AI needs (EY, 2024).
Workforce empowerment through AI training
As AI reshapes job roles, businesses must invest in workforce training to enhance productivity. Employees recognize AI’s potential to automate repetitive tasks, enabling them to focus on higher-value work. By integrating AI-driven training programs, such as virtual and augmented reality environments, businesses can bridge skill gaps and ensure their workforce remains competitive. Moreover, AI-driven copilots can provide on-the-job support, increasing efficiency and learning outcomes (EY, 2024).
AI in Cybersecurity
AI’s role in cybersecurity is a double-edged sword. While AI-powered security solutions can enhance threat detection and response, cybercriminals also leverage AI to exploit vulnerabilities. Companies must proactively integrate AI into their security strategies to safeguard sensitive information. AI-driven cybersecurity solutions can automate stress testing, predict attack vectors, and enhance incident response capabilities. The EY 2024 Global Cybersecurity Leadership Insights Study highlights that AI increases cybersecurity teams’ efficiency, reinforcing its role as a strategic asset (EY, 2024).
Tax and legal considerations in AI adoption
Regulatory complexity continues to challenge AI adoption. Governments worldwide are refining policies around AI, data privacy, cybersecurity, and taxation. Companies must incorporate tax and legal professionals into their AI strategies to navigate compliance risks. Long Hua, EY Americas Technology Tax Leader, underscores the importance of embedding legal considerations into strategic decisions to mitigate unforeseen liabilities and regulatory hurdles (EY, 2024).
Capital allocation for AI investments
To sustain AI-driven growth, businesses must optimize their financial strategies. Many tech companies initially relied on contingency budgets for AI investments, but as AI becomes central to operations, long-term financial planning is necessary. Strategic divestitures of non-core assets can free up capital for AI advancements. According to the EY CEO Confidence Index, many tech CEOs plan to pursue divestments, spin-offs, or IPOs within the next year to reallocate resources toward AI initiatives (EY, 2024).
Shaping AI regulations
Regulatory engagement is essential for tech companies to shape AI governance frameworks. Historically, only the largest tech firms actively influenced regulations, but as policies expand to encompass a broader spectrum of businesses, proactive participation is critical. Bridget Neill, EY Americas Vice Chair – Public Policy, stresses the importance of advocating for harmonized global standards and responsible AI guidelines to balance innovation with societal concerns (EY, 2024).
Conclusion
As AI adoption accelerates, tech companies must transition from experimentation to value realization. By leveraging AI-first strategies, embracing agentic AI, adopting outcome-based pricing, and ensuring regulatory compliance, businesses can transform AI’s promise into tangible revenue streams. The companies that successfully integrate AI across operations will secure their place in the AI-driven future.