Thank you so much for joining us in this interview series. Before we dive into our discussion our readers would love to “get to know you” a bit better. Can you share with us the backstory about what brought you to your specific career path?
Growing up in a family of engineers meant learning to take things apart and put them back together at a young age. This early start led me to excel at taking complex topics and conveying them in terms that resonate with people, opening up a world of career paths. Leveraging these skills has led me through the ranks from inside sales, regional sales, national and international sales, to leadership and strategy responsibilities I currently oversee. Along the way I benefited from a rare opportunity to transition to a market analyst within an industry leading research firm, working with the key vendors on product and go to market strategy.
What do you think makes your company stand out? Can you share a story?
Behind great companies there is always great leadership, it’s a necessity for sustained growth. The main reason I chose to join ClickShare was because of the leadership I saw from our CEO, Charles Beauduin. During our first meeting, prior to ever considering joining ClickShare, Charles joined a meeting between myself and ClickShare leadership, and during the meeting he left a lasting impression. Throughout our interaction, Charles didn’t comport himself as the most important person in-room, but rather as a visionary who was motivated by the team outcome instead of his own agenda. When we moved locations, Charles recognized the value of the meeting and made sure those of us that needed to speak were the focus, to the extent he stepped in to serve those of us at the table. This style of ‘lead by example’ was so refreshing to see, and stayed with me for well over a year until the opportunity came to join the ClickShare team. I didn’t hesitate to join ClickShare, knowing the company’s leadership is decidedly different from most technology companies.
You are a successful business leader. Which three character traits do you think were most instrumental to your success? Can you please share a story or example for each?
Being extraverted, curiosity and perseverance are the three character traits that are most instrumental to my success. My extraverted side is a crucial part of my identity and is also pivotal for my professional career. I’m always willing to share ideas and I’m never afraid to speak up which has created quite some new opportunities on my career path. In general people describe me as outward facing, engaging and very comfortable to voice opinions and ideas.
My curiosity and my eagerness to learn and ask questions brings me to new places, helps me to meet new people and explore new experiences. Having a healthy sense of curiosity perfectly aligns with my interest in international business. I do love to travel, explore how organizations work in an international context and how cultural differences make contacts fascinating. I’m careful not to take a position of a predetermined outcome, but to always be open to new ideas.
Never let fear stop you from doing anything. I’m not fearless, but I do not want to give up or give in. I might fail, but I’m always willing to give it a shot. And this has brought me many new experiences, technologies and insights in my professional life as well.
Let’s now move to the main point of our discussion about AI. Can you explain how AI is disrupting your industry? Is this disruption hurting or helping your bottom line?
AI is disrupting the way the IT industry looks at the future of hybrid meetings. Through the data AI shares, IT leaders can determine the best setup for a conference or meeting based on what people want to do, while also ensuring a positive user interaction. AI has the potential to create a meaningful workplace, which helps our bottom line as our goal is to create a hybrid-meeting experience with the user in mind.
AI has been implemented for years already, from audio algorithms that can filter out dynamic noises (dogs barking, typing) to video algorithms that can focus on a presenter as they move throughout a room. More recently LLM (large language model) AI algorithms are being incorporated into meeting rooms through integrations with unified communications and collaboration (UCC) platforms like Microsoft Teams and Copilot.
These newer models are leveraging multiple types of AI to attribute who’s speaking on the call when multiple speakers are in the same room, transcription apps with translation capabilities for record keeping, and advanced capabilities for prioritization and meeting summaries. All of these applications are currently being adopted across the world, but still in the early days of realizing the potential.
These advancements create many opportunities for a solution like ClickShare, where creating a great in-room experience is our top priority. Thinking towards the future, the ability to walk into a room and seamlessly dive into deep strategy and product discussions within a few seconds is only a couple years from being reality. Through AI technologies, a simple prompt could yield a fully articulated, complex process — such as preparing a SWOT (strengths, weaknesses, opportunities, and threats) analysis or product roadmap — without employees spending hours preparing slides.
Which specific AI technology has had the most significant impact on your industry?
AI isn’t new to the IT industry as it has become an integral part of analyzing vast amounts of data, recognizing patterns and making complex business decisions. But AI technology such as Microsoft Co-Pilot has made the most significant impact in the IT industry when it comes to creating the ultimate in-room experience. For instance, IT managers that want to maximize AI platforms into their existing software can incorporate AI with Microsoft to identify failure points, how much is going on, and make decisions.
Can you share a pivotal moment when you recognized the profound impact AI would have on your sector?
The hype around AI is recent, but those in product circles have been leveraging the tech for a decade, if not more. These models were far simpler, but they opened the door to what we’re now seeing in the market. The potential of AI has been discussed and explored for years, but the moment we started to see predictive analytics I began to realize there would be a revolution. With LLM AI in its renaissance, the most disruptive and innovative applications are just now coming into focus, and the in-room experience of employees is poised to take a huge step forward in the coming years.
How are you preparing your workforce for the integration of AI, and what skills do you believe will be most valuable in an AI-enhanced future?
Within ClickShare we are constantly exploring both how we adopt AI based tools internally, as well as how we incorporate them into our products. From these investigations, there is clearly a new skill set emerging in the workforce, and it is tied to prioritization. Employees that lean into AI tools and can identify when best to leverage them are at a clear advantage over those that continue to leverage manual processes to complete work. This gap is already growing and will only continue on an exponential track, it is vital that employees and management thoughtfully engage and determine what processes are safe to automate and which still require manual oversight. Furthermore, the lack of clarity around IP (Internet Protocol) is especially dangerous currently, as many employees leverage ‘free’ tools (outside corporate policy) to complete work, accidentally exposing information to the world without understanding the implications.
What are the biggest challenges in upskilling your workforce for an AI-centric future?
Training is the biggest challenge in upskilling for an AI-centric future. For instance, what tools to use, when to use them and when not to use them. These complex issues are evolving regularly, which means the workforce needs frequent updates on what is being used, and what workflows are ‘safe’ to accelerate with AI based tools.
Beyond standard training, there also needs to be training around why not to use AI tools that are not onboarded by the company. With offerings like ChatGPT’s copilot and Microsoft’s copilot sharing similar offerings, it makes it even tougher for employees to understand what is and isn’t acceptable practice. Many companies are playing with fire by not having clear standards.
What ethical considerations does AI introduce into your industry, and how are you tackling these concerns?
There are a few ethical considerations AI introduces to the IT industry, including AI bias, privacy and security, public versus private LLMs, and the perception of the technology. AI bias is the top ethical consideration in the IT industry. Privacy and security, such as shadow issues, are also a massive risk. For instance, AI can have access to sensitive information, so there is a threat to intellectual property.
Public LLMs can be dangerous when dealing with NDA information, whereas private LLMs lack actual data privacy. The perception that technology is more accurate than humans, like how a company weighs a human response versus an AI generated response also poses challenges around whose opinion carries more weight, especially when the models being used are still prone to inaccuracies.
What are your “Five Things You Need To Do, If AI Is Disrupting Your Industry”?
- Research: Management needs to be constantly assessing and investigating apps, bots, and models to understand how the algorithm is constructed, not just what the output is. It’s crucial to understand the chain of custody for information, the IP rights and what data is shared.
- Test: When a business case is compelling, the next step is testing the potential solution from both an information security perspective as well as a user experience. These two perspectives are at odds with each other in many aspects of AI — and only considering one will have major implications; either exposing valuable IP or leaving an employee locked out of needed information.
- Iterate: The first method is likely not the best, only through testing multiple workflows can you start to understand the implications across all stakeholders. These policies may change as the organization evolves and adopts more AI-based tools. This process is never ending and requires feedback loops to the testing and research stakeholders; the outcomes of this step will help to inform the next steps.
- Train: Once a team has onboarded an application, bot, or tool, there needs to be clear concise training for employees. This step is arguably the most important, as consideration needs to be given to how the workforce will leverage the tool and what information is ‘safe’ to share with the AI based algorithm. Equally as important to communicate is what not to share and how not to use the tool — this is a change from most software in the past as data entry systems couldn’t make inferences off the data, where these new tools can.
- Track/Report: Once enabled, there needs to be multiple forms of feedback loops to ensure the workforce has the resources available to report bugs, track any data changes, and propose other areas ripe for change. Additional training should also be made available for the portion of the workforce struggling to adopt.
These steps stay consistent regardless of a product team building a new feature/capability around AI or an organization adopting an AI tool.
What are the most common misconceptions about AI within your industry, and how do you address them?
Common misconceptions of AI would be that AI can solve everything — there’s been so much hype behind it, but we’re just in the first days of landing the hype into solutions. The idea that AI is new is a common misconception, too. ML (machine learning) and other precursors have led us here and been applied for years. The advancement is around how the data is processed and the addition of generative capabilities.
Another misconception is that AI yields ‘truth’. An AI model is thankfully still bound by the data it has access to, it can generate new ideas off that data, but it still requires a level of understanding before it can create an output. As we’ve seen already this year with Google’s challenges, AI models are dependent on the inputs, which means bad inputs will yield bad outputs. The danger is management will decide to onboard a tool without properly considering the inputs for the tool, leading to an inherent bias in the model. The old CRM adage ‘bad inputs = bad outputs’ still rings true.
Can you please give us your favorite “Life Lesson Quote”? Do you have a story about how that was relevant in your life?
‘You miss 100% of the shots you don’t take’ — this was something a basketball coach would tell us regularly when I was young, and it always stuck with me. Coach regularly would tell us to just shoot the ball and see what happens, which worked to get all of us comfortable with missing a shot. By pushing us to make the uncomfortable, comfortable, it taught me at a young age that when you think you have something it’s best to ‘give it a shot’. This has translated into my career as there have been many times where the anxiety of ‘taking the shot’ could have stopped me, but instead I come back to ‘I’ll miss 100% of the shots I don’t take’ and push forward. Given that you’ve never heard my name in the NBA or on ESPN, I’ve never had a 100% success rate, but the willingness to ‘take the shot’ has always put me in a better position to rebound and lay-up the next shot.
Off-topic, but I’m curious. As someone steering the ship, what thoughts or concerns often keep you awake at night? How do those thoughts influence your daily decision-making process?
I tend to look inwards about my performance so many of my concerns are around how I’ve comforted myself and how I can improve my skill set, but regularly I’m kept up at night about people — our team, our partners and our customers. How are we driving innovation to ensure growth? How are we investing in our people and partners, do they have what they need and are we supporting them with the right strategy, narrative, training and content? Is our product team close enough to our end customers? There are also the obvious ones like ‘are we making the right choices to address the market’ and ‘will this decision have an unintended consequence’.
You are a person of great influence. If you could start a movement that would bring the most amount of good to the most amount of people, what would that be? You never know what your idea can trigger. 🙂
If I were to start a movement to impact society, I think it would be around building generational wealth, like what a close friend and fellow Forbes Technology Council member Phil Barrar is starting with Future Money. Educating the masses on how to easily create generational wealth to combat the consolidation within the same few families would be my ultimate goal. Once working families learn the playing field they quickly adapt, but lack of understanding and clarity on ‘when is the right time’ stops folks from making the best decisions for themselves. Companies like Future Money are working to break down the barriers to financial expertise and democratize generational wealth for kids and grandkids.
How can our readers further follow you online?
https://www.linkedin.com/in/danielcroot/
Thank you for the time you spent sharing these fantastic insights. We wish you only continued success in your great work!