Navigating the AI Evolution: The Future of Work & Talent

In a rapidly evolving landscape, founders face a pressing question: how will AI reshape the workforce, redefine roles, and dictate the future’s in-demand skills?

StackedSP CEO, Ilan Saks, sat down with CTO and AI Advisor, Arnold Liwanag, on the Unstacked Startups podcast. Together they got into the heart of this transformation, exploring the synergies between human ingenuity and AI’s capabilities, setting the stage for dialogue on harnessing AI’s potential responsibly and creatively.

AI’s Role in Shaping Work & Relationships: Insights from Arnold Liwanag, CTO and AI Advisor

Hire For Critical Thinking

As AI becomes more prevalent in the workplace, it becomes important that leaders look for talent with specific skill in critical and computational thinking. Liwanag believes that the ability to analyze tasks and solve problems with a clear, logical approach is becoming increasingly important. 

Making implicit activities explicit is not just beneficial but necessary for the future of [AI in] work. It’s a skillset that’s already becoming crucial and, I predict, will evolve into formal job requirements within the next five to ten years.

AI can’t be properly managed without human interaction and oversight. Where AI has its limitations, humans must be able to fill the gaps:

• Contextual Understanding: AI lacks the ability to fully grasp the subtleties and nuances of human language and context.
• Accuracy and Value: While AI can generate solutions based on existing patterns and data, it struggles to generate new data or information with accuracy or value.
• Ethical Compass: AI lacks human values and moral intuition, making it difficult for it to navigate ethical problems effectively.
• Emotional Intelligence: AI lacks emotional intelligence and the ability to understand human emotions and motivations.
• Adaptability: While AI can be trained on new data, it lacks the flexibility and adaptability of human thinking.

I find the ability to understand in-depth what it is you’re trying to do, break down problems, ask questions effectively, step back: critical thinking is a super important skill. And some universities are already starting to teach it. One I’ve seen, which I like a lot, is called computational thinking. And so what it does is understand how to break down tasks, which you need to explicitize, because we often do activities and things implicitly. And so that kind of course and that mindset is important in terms of making everything explicit, codifying it, so that then you can understand what’s better to apply humans against or better to apply automated technologies against. And I think that’s how the future of work is going to be designed.

While it seems like AI is taking over routine tasks at a breakneck pace, there is still a lot that can be done with it. Employers are adapting their mindsets and hiring practices to optimize for employees that understand how to get the most from AI tools. 

• The need for educational systems and professional development programs to foster critical thinking skills, preparing the workforce to complement AI’s capabilities effectively.
• The value of employees increasingly lies in their ability to break down complex problems into smaller elements that can be tackled with AI.
• As AI becomes more embedded, critical thinking skills will become increasingly important to navigate and leverage this integration effectively.
• Employers are looking for individuals who can not only work alongside AI but also provide the creativity, emotional intelligence, and nuanced understanding that AI cannot.

The Importance of Communication in AI-powered Teams

AI is the ultimate lazy person, even as it amazes us by delivering more than any human could (capacity-wise). Liwanag highlights a crucial point: AI’s focus on efficiently delivering a response to a query without a great deal of critical thinking underscores the growing importance of clear, explicit communication, both in tech-based interactions and our human relationships.

AI is a tool. And right now, as the AI stands, it actually has no intent. There is risk that as it’s cascading or even the initial AI agent, if it’s just a one-step process, could misinterpret what you’re saying. I think it is incumbent upon us as people to be more understanding of what it is we want [from AI].

Between AI’s evolution and human ingenuity, the specifics of our commands shape the future of technology’s role in our lives. Humans must clearly define what they expect from AI to minimize misunderstandings and maximize its utility. This responsibility underscores the need for clarity and precision in our interactions with AI technologies.

The keys to great communication with AI – and humans:

• Develop Clear Communication Protocols: Establish specific guidelines on how information is shared and interpreted within teams.
• Explitize Instructions: Use clear, direct language, and look at various ways that information and instructions could be interpreted.
• Implement Regular Feedback Loops: Ensure there’s a consistent and structured process for providing and receiving feedback.
• Foster a Positive Environment: Encourage team members to understand and appreciate diverse perspectives and backgrounds.
• Leverage AI to Facilitate Understanding: Utilizing AI tools to analyze and improve communication patterns within the team.

Marriage – when you’re talking to your partner, how do you communicate to each other about what it is you want and what you’re feeling and then the outcomes that you’re trying to drive? And as a leader in an organization, same thing, you have this policy framework that you put in place, sort of the structure of what you think, or you give commands of what you think people will interpret them as, and then they may or may not align with what you think.

An often overlooked aspect of AI’s contribution to enhancing team communication is its ability to kill the “Communication Silo” that often exists unchecked in most companies and can lead to major communication breakdowns among cross-functional teams. Although company silos are often the result of continued growth and the formation of specialized departments and teams, they can lead to internal problems and also the end-user experience for customers. Using AI for process improvement, digital experience analytics and intelligent data management can help break down the silos that affect collaboration, communication, and employee engagement. 

Technical Systems Knowledge Still In Demand

There are still a lot of technical skills that are required to make AI systems work effectively. Most widespread use of AI is still very much out of the box. You just go to chat.openai.com and do your thing and it’s very much a separate experience. Integrating it within an enterprise workflow and the tool sets still require a lot of traditional engineering skills. And that has given the evidence that this can be a very useful tool, but to fully integrate it with proper security, proper governance around the information, and then effective integration with the actual enterprise systems. That is still very much a very strong engineering exercise that needs to be done.

While there’s a great deal of hype around what AI tools can do, they will have the greatest impact when integrated with a business’s existing processes and systems. Technical skills remain an indispensable force in unleashing AI’s full potential.

Indeed, AI’s widespread usage often begins with user-friendly interfaces, like ChatGPT. However, the real magic happens when these systems seamlessly integrate into enterprise workflows, and this integration is far from plug-and-play.

It’s an important reminder: in our eagerness to look for talent that uses AI to achieve an outcome (rather than having the true experience and expertise to build it themselves), we can’t forget that the infusion of AI into the complex fabric of businesses demands technical prowess. The need for traditional engineering skills is evident when we envision the amalgamation of AI tools with stringent security measures, robust data governance, and seamless integration with existing enterprise systems.

In essence, AI isn’t a standalone marvel but a dynamic force that requires the guiding hand of skilled engineers. The future of work and business is undoubtedly AI-driven, but the architects of this future are the skilled technicians who wield the knowledge to navigate the intricate interplay between technology and enterprise requirements.

How AI Can Enhance Your Engineering Team:

• Pattern Recognition: AI is a great pattern detection tool, allowing for more informed decision making by highlighting potential opportunities and threats in the outcomes of group decisions. 
• AI Assistants: AI tools can free-up time normally consumed by mundane and repetitive tasks, allowing for more time to be spent on creative and strategic endeavors. For example, leverage AI to automate routine code generation tasks.
• Predictive Maintenance: Implement AI-driven predictive maintenance systems for equipment and machinery. By integrating AI with IoT sensors, technical teams can predict when equipment is likely to fail, allowing for proactive maintenance. This not only minimizes downtime but also optimizes resource allocation by focusing efforts where they are needed most.
• Algorithm Optimization: Use AI algorithms to optimize the performance of existing solutions. Highly specialized technical teams can employ machine learning algorithms to analyze and enhance the efficiency of complex engineering processes. This could include optimizing parameters, reducing energy consumption, or improving overall system performance based on real-time data and feedback.
• Anomaly Detection: Implement AI-powered anomaly detection systems for intricate technical systems. Highly specialized teams dealing with complex machinery or intricate processes can benefit from AI algorithms that continuously monitor data streams for anomalies. This early detection enables rapid response to potential issues, preventing failures and ensuring the reliability of critical systems.
• Customized Simulation and Testing: Develop AI-driven simulations tailored to the specific needs of highly specialized engineering projects. Instead of using generic simulation tools, technical teams can harness AI to create simulations that precisely replicate the unique conditions of their projects. This allows for more accurate testing, analysis, and optimization of complex systems, ultimately leading to more robust and reliable engineering solutions.

Closing Thoughts

One thing I’ll say about AI moving forward too, what it does is force us to work better together as people. That’s really it. So we have to learn how to understand each other more. That’s what it’s going to force on society. I’m hoping that’s a positive change and a force to this whole transformation.

Arnold Liwanag gives us a clear picture: AI’s role in our work lives isn’t about phasing us out but rather about enhancing our capabilities. It’s a journey towards an AI-driven future with a focus on the human elements of creativity, ethics, and critical thinking. It’s a call to action for us to evolve alongside AI, ensuring it serves as a tool that amplifies our potential and not as a mechanism for our replacement.