A recent CIO report shows that although Work hard to prove ROI. Business leaders are seeking productivity, but with the integration of new technologies, it is possible to refactor existing applications, update processes, and inspire workers to learn and adapt to the modern business environment.
Nate MacLeitchCEO QuickBlox 136 executives were investigated to reveal the reality of AI adoption, thus looking at leaders’ top priorities, key concerns, and trust information they seek in 2025 about their potential tools.
Have we sacrificed the trust of efficiency?
The survey found that ease of use and integration (72.8%) was the biggest driver when choosing business AI tools. However, when asked about their main questions in the selection process, 60.3% voted for privacy and security as their biggest concern. However, this emphasis on ease of use raises questions about whether security should be fully prioritized.
It has become easier for humans to communicate with machines, allowing AI users to do it more skillfully. Enterprises can automate tasks through user-friendly analysis, optimize processes and make better decisions.
API-driven AI and microservices will enable enterprises to integrate advanced AI capabilities into their existing systems in a modular way. Pairing it with a multimodal virtual assistant with no code solution, automatic ML and voice-controlled approach will speed up the development of custom applications without the need for extensive AI expertise.
Through continuous exploration and optimization, AI is expected to be added $4.4 trillion Global economy. The key and complex part to remember today is to verify that these pre-built solutions are in compliance with regulatory and ethical AI practices. In these AI systems, powerful encryption, strict access control and regular inspections ensure data security.
It is also worth checking what ethical AI framework providers follow to build trust, avoid harm and ensure AI benefits. Some famous ones include EU AI lawOECD AI Principles, UNESCO AI Ethical Framework, IEEE Ethical Consistent Design (EAD) Guidelines, and NIST AI Risk Management Framework.
What do leaders need and where do they get it?
While data privacy issues are the biggest concerns for leaders during the AI selection phase, only 20.6% ranked it as the main issue when asked about their integration challenges. Instead, 41.2% of leaders said that integration costs are the most important.
But, it is interesting that when asked “What other support do you need?” “More affordable options” responses were the lowest, with leaders focusing more on finding training and education (56.6%), customized solutions (54.4%) and technical support (54.4%). This shows that people are not only pursuing the cheapest options – they are looking for providers that can support them through integration and security. They want to find trusted partners to guide them through appropriate data privacy protection methods and are willing to pay for it.
External sources of information are preferred when studying AI applications that AI application leaders can trust. When asked to decide the tool, when asked to choose between social networking platforms, blogs, community platforms and online directories as their most trusted source of information, the majority of 54.4% said LinkedIn and X.
Both platforms are probably the most trusted, thanks to the large number of professionals available to connect. On LinkedIn, leaders can follow company pages, best practices, product information and interests shared through posts, review peer reviews, and even have public conversations with other peers to gain first-hand insights. Similarly, on X, leaders can follow industry experts, analysts and companies to keep up with the latest developments. The fast-paced nature of the platform means that if AI tools are trending, platform members will hear it.
Nevertheless, there is still the potential for misinformation and biased opinions on any social media platform. Decision makers must carefully consider the combination of online research, expert consultation and supplier demonstration when making decisions on AI tool purchases.
Can leadership develop rapidly?
The in-house expertise to manage AI is limited, 26.5% ranked its second biggest focus during the integration period, second only to integration costs. one Recent IBM research On AI in the workplace, 87% of business leaders expect at least a quarter of the workforce to need to be remade to cope with generated AI and automation. When finding the right partner is a good start, what strategies can leaders use to train their teams about the information they need and get the strategies they successfully adopted?
The slow and steady victory won the game, but was designed to make the number of calculations per minute. Business leaders must achieve regulatory compliance and prepare their business and workforce. This involves building effective AI governance strategies based on five pillars: interpretability, fairness, robustness, transparency, and privacy.
This helps when everyone is on the same page – share with you the employees you are eager to adopt a more effective strategy. First show them what’s in it. Higher profits? A less stressful workload? Opportunities to learn and improve? There is evidence to back up your statement. Prepare to provide some quick wins or pilot projects to address simpler pain points. For example, in a health care program, this could be a transcription patient’s call and auto-filling intake for doctor approval.
However, you can’t predict what’s in everyone’s mind, so it’s important to create a space for sharing ideas, attention and feedback that the team feels comfortable with. This also provides an opportunity to discover and resolve pain points you don’t know. Cultivating psychological safety is also crucial when adapting to new processes. Framework failure is a valuable learning experience, not a setback to help encourage motivation to move forward.
Adopting AI in a business involves not only increasing efficiency, but also achieving the right balance between availability, security and trust. While companies recognize the potential of AI to reduce costs and simplify operations, they face real challenges, including integration costs and the growing demand for AI-specific skills. Employees are concerned about work displacement and leadership must actively address these fears through transparency and skills improvement programs. Strong AI governance is crucial to driving compliance, ethical considerations and data protection. Ultimately, making AI’s work in the real world boils down to clear communication, tangible benefits, and a safety-first culture that encourages experiments.
