It’s a cliché by now, but nonetheless true: Technology changes fast. That means that the issues tech leaders have to tackle evolve and are added to just as quickly. Further, there are always “evergreen” challenges every leader must learn to navigate.
Unsurprisingly, many tech leaders are wrestling with artificial intelligence—how to leverage it, how to teach it and how to manage the drawbacks (among other details). But while AI is top of mind for most leaders, there are other compelling issues that also must be addressed. Below, members of Forbes Technology Council discuss the top challenges they’re facing right now and how they’re tackling them.
1. Complying With Varying Security Requirements
Security compliance requirements for our multiple products present a significant challenge. We are getting requirements for a variety of certifications from different clients and prospects. I have done a thorough analysis to identify the requirements and create a compliance plan for each product, along with establishing clear requirements for documentation, certifications, training and processes. – Ruchir Brahmbhatt, Ecosmob Technologies Private Limited
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2. Encouraging Accountability
My biggest challenge is in the area of encouraging accountability and ownership. I don’t see the impact of this directly myself, but I am increasingly hearing concerns from teams that junior and entry-level staff lack these qualities. To address this, I am encouraging leadership to practice the principles of empowerment to increase motivation and help more junior staff build purpose. – Jason Penkethman, Simpro Group
3. Navigating The Hype Surrounding AI
It’s a challenge to avoid letting the hype around AI distract us from its real value. AI comes into its own as a specific, rather than a general, tool. It can be trained to solve problems particular to individual businesses. Helping businesses understand AI’s diversity is key to unlocking the AI mindset we all need. – Burkhard Boeckem, Hexagon AB
4. Integrating AI Into Long-Term Roadmaps
The biggest challenge is integrating AI effectively into our long-term strategy and roadmap. While AI holds immense potential, it’s crucial to understand its impact on different aspects of our business before implementing it. Further, there is an AI skills gap. We’re taking a multistep approach to these challenges that includes strategic assessment, future-proofing the roadmap and upskilling the workforce. – Aparna Prabhakar, Schneider Electric
5. Upskilling And Reskilling The Workforce In The Age Of Automation
As automation increases, I emphasize the need for reskilling and upskilling the workforce. To ensure that artificial intelligence becomes a valuable assistant, employees need to learn how to use it effectively, starting with properly defining tasks. This way, AI can help with routine tasks and free up time for strategic work. – Sergii Malomuzh, Rewump
6. Meeting Society Where It Is In Terms Of Technology
One of my biggest challenges is meeting society where it is technologically. It’s crucial to understand society’s tech readiness and determine the right time to introduce new technology. We conduct pilots and tests to gauge adoption rates, identify target personas and align our strategies. This ensures innovations resonate with users and allows society to adopt new technologies gradually. – Morgan Shuler, Tapplix Applications & Web Design
7. Determining Use Cases For AI And Measuring Its Impact
With the rapid growth of AI, identifying which use cases to target and how to measure business impact is top of mind. Creating clear processes across the organization for how we identify, assess, evaluate, implement and ultimately measure AI-based solutions against outcomes is critical. We need a process that allows us to move quickly in adopting and benefiting from AI while also keeping the company safe. – Kim Huffman, Workiva
8. Deciding Whether (And When) To Build Or Buy AI Solutions
The rapid evolution of AI presents a challenge: deciding whether to build proprietary solutions, use off-the-shelf tools or wait for more clarity. There’s uncertainty around the quality and cost of future AI models, as well as the development of new applications, which makes it crucial to avoid investing in solutions that may soon become obsolete or offer a low ROI. We’re being very intentional in what we decide to build. – Rami Zeidan, Life House
9. Integrating Developed Technology Into Diverse Platforms
The biggest challenge for us is integrating our lidar technology into diverse automotive platforms while ensuring its cost-effectiveness and reliability. To tackle this, we invest in R&D, stay aligned with evolving regulations, optimize our supply chain and focus on targeted marketing. We also prioritize talent retention through incentive programs, ensuring our team remains innovative and engaged. – Brunno Moretti, Cepton
10. Advocating A Human-First Approach To Technology Implementation
My biggest challenge is evangelizing that technology sobriety is essential to confronting a #techfirst bias in business, government and society as a whole. How many breaches, ransomware attacks, software malfunctions and other threats will it take to realize that human-first control and implementation are essential to reaping technology’s many benefits? – Wayne Lonstein, VFT Solutions, Inc.
11. Deciding How To Optimize AI Models
Making informed decisions about how to optimize AI models in terms of cost effectiveness and model complexity is one of the biggest issues in enterprise AI deployments. Large-scale generative AI models can handle a variety of tasks, but their complexity and cost are a concern. A wise direction is to develop specialized foundational AI models that are adapted to handle domain-specific use cases. – Igor Kiselev, Accenture
12. Finding Engineering Talent
Despite a lot of recent layoffs in the tech industry, it is still very difficult to find engineering talent, especially for new developing areas such as AI and Web3. The best avenue for solving this problem in our company has been through industry-specific hackathons. This is a very underrated and unexplored way of finding new talent. – Alain Roberto Berwa, Seal
13. Scaling A Growing Company
Scaling a company is challenging and requires significantly more oversight than running a small tech business. It necessitates hiring top-level individuals worldwide who can anticipate and address issues. While delegating tasks, it’s crucial to maintain a holistic overview and vision for growth. This approach enables quick and efficient scaling. – Benjamin Claeys, QR TIGER
14. Dealing With Industry And Economic Uncertainty
As a technology leader, my biggest challenges are the uncertainty surrounding upcoming AI advancements; the pressure to attribute productivity improvements to AI or GenAI, even for simple automation; and postponed buying decisions due to the uncertainty about technology and the economy. I’m addressing these by educating our clients on taking incremental steps and planning iteratively to make the most of this AI evolution. – Prajeet Gadekar, Salesforce Inc.
15. Fostering Consistent Communication And Collaboration
Given the role and scale of our company, a challenge is ensuring consistent and effective communication and collaboration across all offices. To tackle this, we focus on several strategies, including implementing unified communication tools, standardizing processes, fostering a strong company culture, holding regular check-ins and gathering feedback, and investing in training and development. – Christudas Philipose
16. Balancing Technological Advancement With Human Needs
The biggest issue I am currently facing is balancing technological advancement with the human elements of empathy, mental health and societal impact. In the rapidly evolving landscape of artificial intelligence, maintaining this balance is difficult. I aim to ensure that AI advancements contribute positively to both organizational success and the well-being of individuals. – Sam Sammane, TheoSym
17. Overcoming Resource Constraints
My biggest challenge is navigating resource constraints for new growth initiatives. To tackle this, I’m fostering cross-functional partnerships to leverage broader skills and resources. Personally, I am stepping into multiple roles to fill critical gaps; adopting a nimble, iterative approach to scaling initiatives; focusing on customer needs; and refining our solutions to be agile and efficient. – Sagar Ganapaneni, Intuit
18. Managing The Rapid Adoption Of Cloud Technologies
As a solutions architect leader, one of the biggest challenges I’m currently facing is effectively managing the rapid adoption of cloud-native technologies while ensuring security, compliance and cost optimization. This challenge stems from balancing the increasing pressure to innovate quickly and scale efficiently with the need to maintain robust security measures and control cloud spending. – Vaibhav Malik
19. Wisely Leveraging GenAI
The biggest challenge is managing the rapid adoption of generative AI while ensuring its reliability. I focus on continuous learning and upskilling for my team, fostering a culture of innovation, and implementing robust AI governance frameworks. This approach helps us leverage AI’s potential while mitigating risks, ensuring we stay competitive and responsible in a fast-evolving tech landscape. – Abishek Viswanathan, Apollo.io
20. Gaining Consensus On Tech Investments
The biggest issue I’m facing as a tech CEO right now is gaining consensus on the one thing the organization should focus on to achieve the most significant impact. I am tackling this challenge by facilitating open discussions, gathering input from key stakeholders and using data-driven insights to guide decision-making. – Nick Damoulakis, Orases
