Accelerating & upskilling your AI learning journey w/ Maher Hanafi #211
Mar 11, 2025
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Join Maher Hanafi, a seasoned engineering leader from Betterworks, as he dives deep into the transformative power of AI in performance management. He shares his journey of implementing generative AI solutions, advocating for continuous learning and upskilling. Discover how AI can boost productivity while ensuring human-centric approaches in tech workplaces. Maher also discusses strategies for integrating AI within compliance-heavy environments and emphasizes the importance of team autonomy and fostering individual growth in this fast-evolving landscape.
Engineering effectiveness relies on aligning investments with business objectives and enhancing developer productivity and experience through feedback.
Accelerating AI learning requires a shift from theoretical knowledge to practical application, emphasizing continuous learning and hands-on experimentation.
Successful AI strategy hinges on collaboration, fostering a culture of open communication among teams to navigate rapid advancements in AI.
Deep dives
Investing in AI for Business Growth
Engineering effectiveness is framed around three key pillars: business outcomes, developer productivity, and developer experience. It is imperative to assess whether engineering investments are truly propelling the business forward, made evident by evaluating how resources align with company objectives and key results. Furthermore, understanding how developer productivity is affected by potential bottlenecks or process inefficiencies is crucial for optimizing output. Finally, gathering feedback from developers to ensure their experience remains positive allows teams to ground their efforts in tangible metrics.
Navigating the AI Learning Journey
Accelerating the learning journey with AI involves applying knowledge to relevant use cases, promoting team experimentation, and adapting to evolving challenges. The speaker shares experiences transitioning from theoretical understanding of AI technologies to practical implementations within the enterprise context, addressing hurdles such as data governance and feature integration. It is emphasized that continuous learning and hands-on experimentation are vital for building confidence and fostering innovation in AI applications. This approach not only enhances personal growth but also empowers teams to tackle complex challenges effectively.
The Significance of Team Collaboration
Building a successful AI strategy requires collaboration and shared learning among team members, particularly in organizations without dedicated AI experts. An effective strategy is to leverage the knowledge and insights of the team, creating a supportive environment for questioning and experimentation. The speaker highlights that engaging the team in discussions about AI decisions and development processes is essential for fostering a culture of continuous improvement and adaptability. By encouraging teamwork and open communication, the organization can navigate the fast-paced advancements in AI more effectively.
Evaluating AI's ROI and Impact
Determining the return on investment (ROI) for AI applications is challenging yet essential for making informed decisions about technology adoption. Instead of focusing solely on cost, it is vital to consider user satisfaction metrics, such as Net Promoter Scores (NPS), to measure the impact of AI-enhanced features. Continuous feedback loops from end users regarding the efficacy of AI functionalities provide insights for improvement while validating the AI strategy's alignment with user needs. The journey to understanding the real value of AI is ongoing, requiring organizations to stay focused on delivering significant user experiences.
Frameworks for AI Implementation
Implementing AI effectively involves using well-defined frameworks to guide experimentation, optimization, and maturity progression. These frameworks help in identifying low-hanging fruit for initial AI applications while ensuring a strategic approach to increase AI complexity over time. By iteratively revisiting and refining early AI projects, teams can enhance their systems while ensuring compliance and risk management. This structured approach allows organizations to build trust in their AI capabilities and continuously adapt to the evolving demands of their market.
Maher Hanafi is a seasoned technology engineering leader, driving digital transformation and delivering impactful SaaS solutions. As Senior Vice President of Engineering at Betterworks, he leads the AI vision and applications for their AI-powered performance management software, overseeing the integration of AI tools that enhance HR functions like performance reviews, goal setting and employee development.
Maher's passion for technology centers on the transformative potential of AI, particularly Generative AI. He views it as a powerful tool capable of learning, adapting and solving real-world problems, and champions its responsible development to empower individuals.
Maher's vision extends beyond technology, aiming to revolutionize tech workplaces by fostering human potential alongside cutting-edge solutions. He employs a people-centric leadership style, building collaborative environments that empower teams to excel. This commitment to empowerment extends to mentoring fellow engineering leaders and sharing his knowledge through public speaking.
Corey Coto is a creative, data-driven, and innovative executive. He founded Kaizen Insights to help enterprises create business intelligence with their people. Corey was SVP of Product, Design and Engineering at a Vista Equity Partners portfolio company and held engineering leadership roles at Amazon, CoStar Group, and Liberty Mutual. He is a Founder Institute Mentor, an ELC Seattle Chapter Lead, and a startup advisor. Software is his favorite artistic medium because of its power to quickly move the needle on big ideas that can benefit people and the planet. He believes there has never been a better time to build. The future is bright!
SHOW NOTES:
When Maher realized he needed to rethink his approach to AI & upskill quickly (3:38)
Milestones across Maher’s AI knowledge progression (7:42)
Set aside time for your eng team to experiment & apply AI learnings (11:09)
Why intentionally building different use cases leads to better outcomes (14:22)
The importance of revisiting AI decisions as a team (16:53)
Frameworks for determining how deep to go into each learning area (19:37)
How to navigate the challenges of going from proof of concept to production (22:43)
Evaluating the ROI of AI applications (26:47)
Strategies for deciding which resources / operating expenses go toward AI use cases (29:24)
Tips for developing stakeholder confidence in your AI strategy (32:36)
How non-technical experts can build AI awareness & confidence (36:22)
Betterworks’ AI roadmap for 2025 (38:48)
Rapid fire questions (40:58)
LINKS AND RESOURCES
Drive: The Surprising Truth About What Motivates Us - Drawing on four decades of scientific research on human motivation, Daniel H. Pink exposes the mismatch between what science knows and what business does—and how that affects every aspect of life. He examines the three elements of true motivation—autonomy, mastery, and purpose—and offers smart and surprising techniques for putting these into action in a unique book that will change how we think and transform how we live.
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