Tech companies are pouring record capital into artificial intelligence. They’re not doing this because they can. They’re doing this because the opportunity cost of failing to invest is existential. The cost of getting it wrong is existential, too. Meanwhile, those who get AI right stand to redefine tech as we know it.
This is all taking place at a time when it is getting increasingly more difficult for organizations to map their AI investments to clear outcomes. KPMG data shows 44% of tech companies across the world have yet to establish concrete ROI measures, and another 44% are unable to measure AI scalability.
With stakes this high, two types of battlegrounds have emerged — competitive environmental battles and technical battlefields — determining who will succeed at AI and who may fail. Simultaneously, market trends are influencing these battles and fundamentally changing how tech enterprises operate, innovate, and create value.
Competitive Environmental Battles
Stakeholder trust and support. Securing the trust and support of stakeholders — employees, suppliers, customers, investors, markets and regulators — is one of the greatest challenges for companies.
KPMG and the University of Melbourne captured the views of more than 48,000 people from 47 countries, including 1,019 people from the US on their trust and expectations regarding AI. While 41% of respondents are willing to trust AI, 75% remain concerned about negative outcomes from the technology. Further, 44% of workers said they use AI at work in inappropriate ways, and 58% indicated they rely on AI output at work without evaluating its accuracy. The proliferation of AI in the workplace represents one of the largest change management efforts in recent history, requiring innovative strategies to win trust and confidence with employees.
Regulatory complexity. Evolving AI regulations on ethics, data privacy and bias pose significant compliance challenges. With no universal governance framework, global corporations must navigate a complex regulatory landscape that shapes product design and usage across regions. Differences in competition law, data privacy, AI integrity and accuracy testing add complexity.
Resource constraints. A shortage of resources — from talent to hardware to energy infrastructure — is pressuring innovation and growth. Nations and states are offering competing incentives to attract investment and create jobs as engineering advancements enable products that consume less resources than established peers.
Technical Battles
The race to market. The first of these technical battles is about access and speed. Specialized hardware architectures must advance in sync with AI breakthroughs. Access to custom chips, data ecosystems and scalable platforms is critical. Some companies have inherent advantages here. As organizations scale and enter untapped markets, previous competitors are likely to become new collaborators.
Rapid evolution of architecture. Second, enterprise architecture is undergoing a profound transformation. Companies are fundamentally reconsidering how hybrid cloud environments, modern data platforms, enterprise SaaS solutions, AI models, and intelligent agents should integrate to serve core business objectives.
This is occurring while consumption and outcome-based SaaS models become the new norm. Companies that moved to traditional SaaS spent the last decade or more devoting significant effort to redefining their lead-to-cash processes and systems. These companies now must readjust lead-to-cash through the lens of consumption and outcome-based pricing models.
Scale versus specialization. The third battle is more of a choice. The choice is not binary. As companies decide between leveraging foundational AI models to drive widespread adoption or developing specialized, high-value AI-powered solutions tailored to specific needs, the answer “both” is becoming clearer.
These Base LLMs and AI agents will also co-exist with their human workforce. Just as people are measured based on their performance, it will be up to organizations to evaluate the decision rationale and error correction of their AI agents and platforms.
Open source versus proprietary. Lastly, and perhaps the most philosophical of the technological battles is the tension between open-source AI and proprietary solutions. Open-source models democratize access and offer more transparency. Proprietary solutions offer specialized capabilities and more control.
Strategic decision-makers must carefully weigh considerations of cost, transparency, security, and control. This isn’t simply about software licensing — it’s about determining whether AI’s future will be built on shared foundations or if the advantages of retaining exclusive IP are worth the increased cost.
To win both the technical and environmental battles, companies must fundamentally reevaluate their operating models. Success will require aligning functional capabilities to enable rapid experimentation and iterative learning while developing foundational enablers that support continuous evolution.
Only organizations that structure themselves for agility — both organizationally and technologically — will remain positioned to capitalize on emerging opportunities as these systemic shifts unfold. The winners won’t simply be those with the most advanced technology but those who can most effectively integrate these innovations into cohesive solutions that deliver tangible business value.
The future of technology is being decided today on two different battlegrounds. The companies that navigate them successfully will not just participate in the AI revolution, they’ll define it.