Emerging technologies—particularly AI—are reshaping industries. Seeking to boost productivity and remain competitive, C-level leaders are often eager to adopt the tools they’re reading about. But enthusiasm at the top doesn’t always translate into clear-eyed risk assessment or well-considered usage plans, leaving organizations vulnerable to operational, compliance and security challenges.
Tech experts say bridging the divide between impatient enthusiasm and smart implementation requires more than just new tools—it calls for cultural alignment, shared language between technical and executive teams, and governance frameworks that translate technical risks into business terms. Below, members of Forbes Technology Council share strategies to bring leadership vision and frontline realities into sync.
1. Treat Cultural Adoption As Seriously As Deployment
Emerging tech doesn’t just disrupt workflows—it reshapes culture. A major gap is C-level enthusiasm outpacing change management. Without guiding teams through mindset shifts and skill building, powerful tech can stall, creating real operational risk. Leaders must treat cultural adoption as seriously as deployment, with training, incentives and change champions. – Subasini Periyakaruppan, Biotechnology Innovation Organization
2. Align AI To Established Business Objectives
A gap I’ve observed is confusion on how to align the right AI for specific use cases. First define the business objective, then align tech to support the use case. A convergence strategy, using multiple forms of AI technology together, is often the best way to achieve the objective. – Sharon K Daniels, Arria NLG PLC
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3. Reimagine Processes And Data From The Ground Up
C-suite leaders often assume that any new technology can be bolted onto legacy architecture as a shiny new feature. They don’t fully grasp the operational risks of fragmented data, brittle workflows and unprepared teams, all of which hinder companies from seeing real value. IT leaders need to reset the enterprise foundation by reimagining processes and data from the ground up with AI at the core—not as an afterthought. – Lenin Gali, Atomicwork
4. Involve Engineering And Legal Experts Early
One of the biggest gaps I’ve seen is leaders who have a lot of enthusiasm for AI without fully understanding the associated risks. Many companies launch pilots without addressing data quality, governance or compliance. We bridge this gap by involving engineering and legal experts early so solutions are built to scale responsibly, not just to impress. – Shubhangi Srivastava, NEP SERVICES
5. Adopt Strategic Flexibility
Think outside the box and adopt a mindset of strategic flexibility. While your vision may stay consistent, new threats and technologies like generative AI require product evolution to meet customer needs. Sometimes that means throwing out ideas you’ve been working on or refining existing solutions. Though it may feel like wasted time, it balances current revenue with future potential. – Javed Hasan, Lineaje
6. Embed Security Across Data, Identities And Applications
Over two-thirds of businesses say fast-moving AI is their greatest security concern, exemplifying the gap between excitement at the top and hesitation on the ground. Frontline experts recognize that to bridge this gap, security must be embedded across data, identities and applications, ensuring AI innovation moves forward with the right governance and policies in place. – Todd Moore, Thales Group
7. Provide Ground-Level Clarity On Mission, Vision And Objectives
In my opinion, the gap often lies in alignment: C-level enthusiasm reflects vision and appetite for risk, while teams see value in new tools but may lack context on strategic fit. Bridging this gap requires translating mission, vision and objectives into ground-level clarity, ensuring risk appetite matches execution reality. – Nihar Malali, National Life Group
8. Avoid Going ‘All In’ On A Single Tech Paradigm
C-level leaders can sometimes be overzealous—they may not understand that an emerging tech tool isn’t the silver bullet vendor marketing portrays it as. AI is a good example: Even though it’s evolving rapidly, it does not have the general intelligence a human has yet. C-suite decisions should not result in going “all in” on any one tech paradigm. Historically, technology has never delivered 100% of what was promised. – Eoin Keary, Edgescan
9. Translate Technical Risks Into Business Language
C-suite leaders are drawn to emerging tech for its potential value, but they often overlook how these tools fit into the current organizational tech ecosystem. A clear framework that translates technical risks into business terms—cost, revenue, reputation and strategy—ensures risks are communicated in a language executives understand. – Asif Mujahid, Quartz Health Solutions
10. Track Data Lineage And Enforce Governance
Many companies overlook the risk of unclear data lineage, especially with AI systems. Without knowing where data originated or how it has been processed, organizations face risks like inaccurate outputs, regulatory violations and data leaks. Automated auditing and strict data governance can help track data flows and ensure accuracy, privacy and compliance at every step. – Dave Albano, Diliko
11. Treat AI Agents As Identities
Agentic AI is racing ahead in the boardroom, but most teams don’t think of agents as identities. Traditional identity access management was built for humans, not autonomous code, leaving blind spots in access, governance and compliance. We need to treat agents with the same security rigor as human identities, using identity orchestration for real-time visibility and policy enforcement for every agent and app. – Eric Olden, Strata Identity
12. Communicate Clearly With All Users
In our business, we often hear the only thing officers hate more than change is the status quo. In public safety, while executives and frontline officers have the same end goal, their use cases can differ. How can a tech company bridge that gap? Through clear communication, training, and feedback at every step to ensure new technology works for all users, not against them. – Matthew Polega, Mark43
13. Apply Technology-Driven Governance To No-Code Platforms
Many leaders are backing the use of no-code platforms to accelerate application delivery, but they may be overlooking the risks: unsanctioned connectors and data exposure in apps built outside the purview of IT security. Bridging this gap means applying technology-driven governance—automated discovery, continuous scanning and policy enforcement—to let innovation scale without creating hidden vulnerabilities. – Yair Finzi, Nokod Security
14. Keep Tech Jargon Out Of Boardroom Discussions
Communication is key, and it needs to be pragmatic and jargon-free. The board wants to hear, “Yes, we can do it!” but IT needs to communicate the risks and opportunities in business speak so the board can make strategic decisions to move forward. – Mike Kiersey, Workato
15. Explain The Necessary Steps From Pilot To Production
One big issue, most notably with AI, is the difficulty in getting C-level leaders to understand why a pilot demo can show incredible results in just a few days, while deploying the same application in production, using real data, would require months of hard work. Make sure to always explain the “steps to production” and the anticipated difficulties (such as data quality) when showing a sexy proof of concept to your C-level team. – Stephane Donze, AODocs
16. Build Cross-Functional Teams To Translate Risk
C-level leaders often embrace emerging tech with enthusiasm but underestimate risks like data quality, integration or compliance. The gap can be bridged by creating cross-functional teams that translate risks into business terms, embedding governance into adoption, and giving executives hands-on exposure to limitations as well as opportunities. – Paul Kovalenko, Langate Software
17. Foster A Trusting Culture Of Data Sharing
Executives get excited about AI capabilities, but they may overlook the fact that teams often resist sharing data due to internal politics and the fear of mistakes being exposed. Fix this reluctance by first building trust and a data-sharing culture, then deploying the technology—not the other way around. – Stoyan Mitov, Dreamix
18. Use Structured, Use-Case-Driven Tools
C-level enthusiasm often runs ahead of operational understanding. Leaders are inspired by potential, but teams often get buried in complexity. Bridging the gap requires structured, use-case-driven tools that “see the whole picture” and surface both insight and risk without overwhelming the user. – Mike Conover, Brightwave
19. Bring Engineers And Risk Officers Together
The C-suite often celebrates new tech as transformation, while frontline teams see the actual cracks: bias, compliance gaps and fragility at scale. The gap isn’t vision; it’s translation. Leaders must collapse that divide: Put engineers in boardrooms and risk officers in design sessions. When vision meets ground truth, innovation doesn’t just inspire—it endures. – Aditya Vikram Kashyap, Morgan Stanley
20. Connect Vision With Readiness
The gap isn’t just excitement versus caution—it’s how leaders and teams view the horizon. CXOs often embrace emerging tech only when data proves business value, yet at times they also push adoption to gain a strategic edge. The real bridge is connecting vision with readiness—using the right data, predictive analytics and transparent dialogue to align leadership goals with on-the-ground realities. – Arun Goyal, Octal IT Solution LLP