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In the last twelve months, artificial intelligence transformed from a futuristic concept to a critical component of business strategy. As companies navigate this new landscape, it’s necessary to differentiate between hype and meaningful innovation. In this exclusive Q&A, Introhive’s Chief Product Officer, Leyla Samiee shares insights on what’s truly driving the AI revolution, the strategies that have proven effective at Introhive, key lessons learned from years of AI experience, and what AI developments hold for the future of the professional services industry.
About Leyla Samiee
Leyla’s technology leadership career spans over 15 years, during which she has led cutting-edge projects for some of the world’s leading companies, including Reuters, Reuters News, and Meta. As the Head of Technology for Reuters News, Leyla led the AI and technology advancement of Reuter’s content platform, Reuters Connect, the most comprehensive digital platform powering the news ecosystem. During her short time with Mozilla, as the Product and Engineering VP, she played a pivotal role in experimentation and diversifying Mozilla’s product offerings. At Meta, she was instrumental in developing unique AI infrastructure and development tooling products, enabling efficient and well-governed model development and management lifecycles.
Looking back at the AI revolution
The last 24 months have seen a surge of interest and rapid growth in AI. From your perspective, what key factors have driven AI to become such a focus of investment?
The last two years have seen unprecedented interest and growth in AI, primarily driven by increased accessibility. The open-sourcing of technologies like large language models (LLMs), which previously required massive investment, has invited more diverse innovation. Companies across different industries now have the means to experiment and build on these technologies without the traditional barriers of cost and resourcing.
However, with this rapid adoption came challenges. While the potential of AI is vast, I think we’re still in the early stages of understanding its transformative impact. Many organizations rushed to invest without a clear strategy, leading to mixed results. From my perspective, governance and thoughtful structure will be critical as AI’s role becomes more defined. As businesses navigate this evolving landscape, those that focus on the fundamentals—such as data quality and smart human-AI collaboration—are the ones I believe most likely to benefit from the opportunities AI offers.
Many companies rushed to embrace AI, some investing without a clear strategy. What are the key differences you’ve observed between organizations that realized meaningful AI outcomes and those that struggled to see a return on their investments?
I think the real difference between successful AI adopters and those that struggled has been a clear strategic framework. Organizations that saw meaningful outcomes put customer needs first and identified specific, measurable goals where AI could enhance speed, efficiency, or effectiveness. They didn’t just dive in—they focused on where AI could truly add value.
Leaders at these organizations also asked the right questions early on. For example, questions like “Do we have the right data?” and “Is our infrastructure ready for AI?” Those that invested in refining their data quality and aligning their tech stacks to real business objectives were better positioned to combine human expertise with AI capabilities for more tangible results. It’s this clarity of purpose that set them apart from companies that simply reacted to the AI buzz without a strong data foundation or infrastructure.
Defining a successful AI strategy
You’ve emphasized the importance of a thoughtful, mindful approach to AI. Can you elaborate on what this means in practice for organizations.
In my opinion, a mindful approach to AI starts with a deep understanding of how technology can best serve human decision-making. From day one, organizations should focus on how AI can complement rather than replace human intelligence. It’s not just about implementing the latest AI tools, I believe it’s also about ensuring those tools are strategically aligned with how your teams work and how they can ultimately deliver better outcomes for your customers as well.
In practice, this means taking a step back and evaluating where AI can make the biggest impact on your workflows. Start by analyzing the areas where AI can streamline the process of turning raw data into actionable insights. For example, machine learning can accelerate pattern recognition, allowing humans to focus on what really matters—the interpretive and decision-making steps. If businesses can empower their people with faster, more accurate insights, they’re effectively integrating AI into the decision-making process in a supportive, not overwhelming, way.
Central to this approach is the quality of data you’re working with. AI is only as good as the data it’s trained on, so I would emphasize that investing in data integrity is critical. Equally important is ensuring that the workflows AI touches are clear and well-defined. I believe this approach brings together high-quality data with streamlined, human-centered workflows to enhance decision-making in a meaningful way. Ultimately, the effectiveness of AI doesn’t just hinge on the technology alone; it really depends on how well it’s applied to specific, real-world use cases that matter for your business.
How should organizations evaluate and prioritize AI initiatives to ensure they’re focusing on high-impact areas?
When thinking about how to prioritize AI initiatives, I believe organizations need to start by identifying where workflows are most inefficient and prone to bottlenecks. Often, the most impactful AI investments are found where there’s a lot of manual interaction with multiple data points—those moments that slow down human decision-making. In my experience, focusing on areas where the integration of AI can streamline these touchpoints, businesses stand a better chance of seeing a clear return on investment.
I also think it’s important for companies to differentiate between AI that supports augmentation (i.e., enhancing human decision-making) and AI that targets automation (i.e., eliminating repetitive manual tasks). Augmentation can be especially powerful because it allows AI to do a lot of the heavy lifting, collecting and organizing data from disparate sources, and then presenting humans with the key insights they need to make smarter, faster decisions. For example, AI can pull together relevant meeting data, flag the right stakeholders, or preemptively offer insights that would normally require tedious human effort. In this way, AI becomes a partner or an ‘agent’ so to speak, tackling 80% of the data-processing so humans can focus on the final 20% that involves nuanced, strategic decisions.
On the automation side, organizations should look for areas where repetitive tasks or disjointed systems drain time and resources—such as data entry or scheduling processes. By automating these less valuable tasks, they can free up human talent for higher-order responsibilities.
At the end of the day, both strategies—augmentation and automation—help reduce inefficiencies and drive business growth, whether by reducing operational costs or enabling faster, more informed decision-making that can lead to higher revenue. Prioritizing AI initiatives that target these key impacts will ensure you’re focusing on what really moves the needle for your organization.
Unlock the full potential of AI by starting with data
To make the most of AI, organizations must address the foundation—high-quality, secure, and actionable data. Want to learn how Introhive has been navigating AI for over a decade? Read about our 12-year journey with AI here. Ready to strengthen your data and decision-making process? Contact us today to learn how Introhive can help you streamline your data, enhance decision-making, and set the right course for your AI journey.
