Are leaders ready to bridge the innovation-trust gap?

Nate MacLeitch, CEO of QuickBlox, collaborated with the AI Marketing Directory to survey 131 business leaders on their AI priorities for 2025.

The results indicate a dynamic tension: 72.8% of leaders cite ease of use as a top priority, but 60.3% are concerned about security vulnerabilities. This paradox underscores a critical challenge for leaders—ensuring seamless user experiences without compromising the integrity and safety of their AI tools. In this interview, MacLeitch delves into the survey insights, providing strategies for building trust, bridging readiness gaps, and leading teams into a transformative AI era.

QuickBlox’s survey highlights a tension between ease of use and security concerns. How should leaders address this trade-off without compromising trust?

Yes, our survey found that 72.8% of business leaders saw ease of use as a top priority for their 2025 AI investments. However, 60.3% expressed concern about the security of their tools.

Really, easy navigation and airtight security go hand in hand. Any tools your developers build should be simple to use, and to ensure that, you need a clear purpose so they can map out the user journey in as few steps as possible. When the app itself is streamlined, and workflows are straightforward, it is much easier to track and monitor performance and security. We know that a blank page can be daunting when scoping out app development projects. We’ve built customizable software development kits (SDKs) and application programming interfaces (APIs) to help developers get a head start. They can plug and play with the various features available to suit their specific use cases.

By using SDKs that have been developed and trialed by expert security teams—built with best practices, security protocols, and vulnerability mitigations front of mind—developers write less code themselves, which reduces time-to-market and the potential for introducing security flaws.

Nonetheless, we strongly advise that no matter which provider developers work with, they still review the code, understand how it handles sensitive data, how frequently it receives software updates, and implement security measures like input validation and authorization in-house. This way, business leaders can offer user-friendly communication tools that their customers can trust.

What specific measures can organizations take to reassure stakeholders about AI’s privacy and security features?

Explainable AI is a concept that’s been around for a long time, but it became a buzzword after the launch of mainstream generative AI in 2022. There was a lot of concern regarding overly complex algorithms and bias. Organizations must work closely with their AI partners to ensure they fully understand the decision-making process.

The World Economic Forum’s latest Future of Jobs Report 2025 found that 86% of organizations plan to fund upskilling programs from 2025 to 2030. Enhancing productivity, improving competitiveness, boosting talent retention, and evolving roles are all top drivers of training investments.

Let’s say they offer a website chat that provides conversational AI answers to FAQs. There are application features that can show users the source of information, for example. And for sensitive information, there are features that can automatically delete these files, or encrypt them to prevent unauthorized access. By being transparent about how data is collected, used, and protected, and offering this information in layman’s terms, stakeholders can feel confident their data is safe and secure.

The survey reveals gaps in AI readiness and resistance to change. What are the most common reasons behind this resistance, and how can leaders mitigate it?

Generative AI shocked the public; many felt threatened that these tools would take their jobs, they feared that AI was going to respond better to customer service queries, produce more engaging marketing material, or code products better than a qualified developer. It isn’t the case. In the same way that accountants do their job much faster with calculators, this is what we are seeing with natural language processing (NLP) and generative AI tools. They help us summarize meetings, transcribe audio files, respond to repetitive tasks, and brainstorm new ideas, but they cannot replace a human. They require oversight.

The second biggest fear is not understanding how to use these tools, which can lead to doubt, security risks, and lack of trust. Leaders must be really clear on what they wish to gain by introducing these tools, how they benefit their employees long-term, and what short-term changes their employees need to make to get to their new long-term positions. Showing success stories and providing dynamic training support will help equip employees with the knowledge and conviction they need to be AI-ready advocates.

Financial tools and task automation are seen as game-changers. Can you share examples of how these tools have successfully addressed business challenges?

That’s right, 60.3% of our respondents looked to integrate AI-powered finance tools, while 46.3% were interested in automating repetitive tasks. We believe the primary focus in the finance industry is because it is already very process driven. Financial institutions are data-rich, and monetary information is often well-structured, making it easier for AI algorithms to process and, accordingly, reduce the risk. More to the point, AI excels at identifying patterns and anomalies much faster than humanly possible, which banks can use to indicate costly expenses such as preventing fraudulent activity, so the ROI is really evident. Whereas industries with much more unstructured data or with less tech-savvy employees can be more hesitant to adopt, as there is a bit more prep work that needs to happen before implementation, in terms of data strategy and employee training.

What role does employee upskilling play in bridging readiness gaps, and how can companies foster a culture of adaptability?

The World Economic Forum’s latest Future of Jobs Report 2025 found that 86% of organizations plan to fund upskilling programs from 2025 to 2030. Enhancing productivity, improving competitiveness, boosting talent retention, and evolving roles are all top drivers of training investments.

Every day, people globally are learning, experimenting, and finding new and better ways of doing things. Technology is advancing at a rate faster than we’ve ever seen, and it’s our job to ensure we keep our eyes and minds open to address and adjust to these changes. Upskilling employees ensures they possess the capabilities to handle emerging challenges and empowers them with the knowledge to contribute fresh ideas and stay competitive. Business leaders must embed a culture of continuous learning in the company’s DNA. By making learning dynamic and engaging with workshops, online courses, and mentorship opportunities, celebrating the small wins, understanding that we learn best from our mistakes, and leading by example, companies can foster growth mindsets and protean company cultures.

Leaders face a clash between cost-effectiveness and team concerns. How can they balance technical demands with maintaining a supportive and empathetic work environment?

Yes, 47.1% saw cost-effectiveness as their top priority. However, 60.3% are concerned about resistance to change within the team, and 47.8% are worried about limited internal expertise to manage AI.

There really isn’t a shortcut answer, this balancing act is an ongoing tightrope that all leaders must walk, but it helps to be transparent with your team as much as possible. When teams understand the context and the bigger picture of workflow changes, they’re usually much more likely to back ideas. Establish channels for ongoing feedback so they can ask their questions and actually comprehend what is happening. The trick here is to ensure that, firstly, they feel safe enough to ask what is on their minds, and this comes with listening empathetically and knowing the personality types and communication styles within your organization. The second is actually engaging them enough to care. Usually, the expected ROI of projects and individual benefits help here. It’s important that teams see these changes as a way of making their lives easier and that timely rollout plans are created so that employees are not burning out trying to grapple with new tools while keeping business as usual. This is where frequent check-ins with line managers are critical.

What leadership qualities or practices are most critical for navigating the AI revolution successfully?

Strategic foresight: Stay informed about AI developments and speak with peers in the industry and your team in-house. What are their bottlenecks? How are others externally handling these? What changes need to be made internally, and how disruptive are they? Then it comes down to those factors we’ve already discussed, so encourage growth mindsets, embrace experimentation, and communicate often. Good communication skills and well-phased, digestible plans are paramount.

Could you elaborate on how airtight workflows can be designed without stifling creativity and innovation?

Creativity and innovation are building blocks to airtight workflows. Companies should get their team in a boardroom at least once a year and just brainstorm what is working, what isn’t, and how they can do it better. Then they can clean the slate, define their objectives, and draw out purpose-driven, streamlined workflows. Pinpoint what is repetitive or time-consuming and where automation can help. Clarity on roles, responsibilities, and decision-making authority will reduce ambiguity and allow individuals to operate more quickly, with authority in their designated areas.

What trends do you foresee in generative AI adoption by 2030, and how should organizations prepare for them now?

The Future of Jobs Report 2025 shows that more employers—60%—expect broadening digital access to transform their business more than any other trend (other trends listed include rising living costs, labor issues, and global warming). LinkedIn’s Work Change Report: AI is Coming To Work, mirrors this prediction: The report states that by 2030, 70% of the skills used in most jobs will change, with AI emerging as a catalyst.

Generative AI adoption is already happening: Salesforce indicates that 73% of the Indian population surveyed (over 4,000 respondents) uses generative AI. Figures for the US (45%) and UK (29%) are lower, although they rise among millennials and Gen Z.

AI is only becoming more mainstream and accessible. Organizations must ensure their teams have the skills, culture, and regulations in place to adjust to new competitive markets. Companies should investigate tried-and-tested generative AI use cases, starting at the basics, and let their teams experiment with what these tools offer, then build their way up to more advanced, tailored solutions.

If you were to advise CEOs making billion-dollar investments in AI, what top three questions should they answer before moving forward?

I would ask: What specific business problem are you trying to solve with your AI investment? Do you have the right data, infrastructure, and talent in place? And what is your change management plan?

Nate MacLeitch

CEO of QuickBlox, weighs in.

aanchalg@cybermedia.co.in