A recent CIO report revealed that enterprises are investing up to $250 million in AI despite struggling to prove ROI. Business leaders are on a quest for productivity, but with new technology integration comes the need to potentially refactor existing applications, update processes, and inspire workers to learn and adapt to the modern business environment.
Nate MacLeitch, CEO of QuickBlox surveyed 136 executives to uncover the realities of AI adoption—looking at leaders’ top priorities, primary concerns, and where they seek trusted information about their prospective tools in 2025.
Are We Sacrificing Trust for Efficiency?
The survey results found ease of use and integration (72.8%) to be the top driver when selecting business AI tools. Yet, when asked about their primary concerns during the selection process, 60.3% voted privacy and security as their biggest worries. This emphasis on ease of use, however, raises questions about whether security is being adequately prioritized.
It is becoming easier for humans and machines to communicate, enabling AI users to accomplish more with greater proficiency. Businesses can automate tasks, optimize processes, and make better decisions with user-friendly analytics.
API-driven AI and microservices will allow businesses to integrate advanced AI functions into their existing systems in a modular fashion. Pair this with no-code solutions, auto-ML, and voice-controlled multimodal virtual assistants and this approach will speed up the development of custom applications without requiring extensive AI expertise.
Through continued exploration and optimization, AI is projected to add USD 4.4 trillion to the global economy. The crucial and complex part to keep in mind today is verifying that these pre-built solutions comply with regulatory and ethical AI practices. Strong encryption, tight access control, and regular checks keep data safe in these AI systems.
It’s also worth checking what ethical AI frameworks providers follow to build trust, avoid harm, and ensure AI benefits everyone. Some noted ones include, the EU AI Act, OECD AI Principles, UNESCO AI Ethics Framework, IEEE Ethically Aligned Design (EAD) Guidelines, and NIST AI Risk Management Framework.
What Do Leaders Need, and Where Do They Go To Get It?
Although data privacy concerns were leaders’ biggest worries during the AI selection phase, when asked about their integration challenges, only 20.6% ranked it as a primary issue. Instead, 41.2% of leaders stated that costs of integration were top of mind.
Interestingly, however, when asked “What additional support do you need?” the response “More affordable options” was ranked the lowest, with leaders more focused on finding training and education (56.6%), customized solutions (54.4%), and technical support (54.4%). This suggests that people aren’t just going after the cheapest options—they are looking for providers that can support them with integration and security. They would prefer to find trusted partners to guide them through proper data privacy protection methods and are willing to pay for it.
External information sources are the go-to when researching which AI applications leaders can trust. When asked to choose between social networking platforms, blogs, community platforms, and online directories as their most trusted source of information when deciding on tools, an equal majority of 54.4% said LinkedIn and X.
It is likely that these two platforms were most trusted due to the vast amount of professionals available to connect with. On LinkedIn, leaders can follow company pages, best practices, product information, and interests shared via posts, review peers’ comments, and even open conversations with other peers to gain insights from firsthand experiences. Similarly, on X, leaders can follow industry experts, analysts, and companies to stay informed about the latest developments. The platform’s fast-paced nature means if an AI tool is trending, platform members will hear about it.
Still, the potential for misinformation and biased opinions exists on any social media platform. Decision-makers must be mindful to consider a combination of online research, expert consultations, and vendor demonstrations when making AI tool purchasing decisions.
Can Leadership Evolve Fast Enough?
Limited internal expertise to manage AI was listed by 26.5% as their second biggest concern during integration, second only to integration costs. A recent IBM study on AI in the workplace found that 87% of business leaders expect at least a quarter of their workforce will need to reskill in response to generative AI and automation. While finding the right partner is a good start, what strategies can leaders use to train teams on the required information and achieve successful adoption?
Slow and steady wins the race, but aim to make every minute count. Business leaders must realize regulatory compliance and prepare their operations and workforce. This involves creating effective AI governance strategies built upon five pillars: explainability, fairness, robustness, transparency, and privacy.
It helps when everyone is on the same page—with employees who share your eagerness to adopt more efficient strategies. Start by showing them what’s in it for them. Higher profits? Less stressful workloads? Opportunities to learn and advance? It helps to have evidence to back up your statements. Be prepared to deliver some quick wins or pilot projects that solve more simple pain points. For example, in a healthcare scenario, this could be transcribing patient calls and auto-filling intake forms for doctors’ approval.
Nevertheless, you cannot predict what is on everyone’s minds, so it’s important to create spaces where teams feel comfortable sharing ideas, concerns, and feedback without fear of judgment or reprisal. This also offers the chance to discover and solve pain points you didn’t know existed. Fostering psychological safety is also crucial when adjusting to new processes. Frame failures as valuable learning experiences, not setbacks, to help encourage forward momentum.
Adopting AI in business isn’t just about efficiency gains—it’s about striking the right balance between usability, security, and trust. While companies recognize AI’s potential to reduce costs and streamline operations, they face real challenges, including integration expenses, and a growing need for AI-specific skills. Employees worry about job displacement, and leadership must proactively address these fears through transparency and upskilling initiatives. Robust AI governance is critical to navigating compliance, ethical considerations, and data protection. Ultimately, making AI work in the real world comes down to clear communication, tangible benefits, and a security-first culture that encourages experimentation.
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