It’s time for me to reflect on some of our top  AI  insights from 2025! Over the past year, our experts have shared their lessons learned, best practices, project wins, and industry perspectives, and I’m excited to recap some of that content below. You can also take a look at all of our  AI content from the past year here.  

Top AI Insights from 2025

1. Gaining Executive Leadership Buy-In for Your AI Strategy 

Many executives still lack a clear understanding of the AI technologies their organizations use, with only 29% of executive teams reporting sufficient in-house expertise to adopt generative AI, creating a gap that can stall sound decision-making and strategy. As companies move from experimenting with AI to embedding it into long-term business plans, executive leadership buy-in becomes essential to align AI initiatives with corporate vision and change management efforts. Without that support, AI projects often struggle to scale – read more from Saif Murad and Malik McFadden. 

How to gain executive leadership buy-in for your AI strategy >>  

2. Buying vs. Building AI Tools: Key Considerations for Developing AI Capabilities 

As AI use cases continue to expand, organizations must intentionally develop capabilities that align with their unique needs and maturity. Choosing whether to build AI in-house, outsource to third parties, or adopt a hybrid approach depends on each organization’s readiness, resources, needs, and strategic goals. In this article, Elise Watson and Jack Magee discuss these trade-offs and how leaders can make smarter investment decisions when deciding whether to buy or build AI tools. 

Should you buy or build your AI tools? >>  

3. Ensuring AI Due Diligence for a Private Equity (PE) Firm 

Clarkston’s digital team conducted a comprehensive audit of an AI product and its supporting software infrastructure for a private equity firm. Recognizing that the AI and its related intellectual property were central to the company’s value (and reliant on a small group of developers) the client engaged us to assess code quality, vulnerabilities, and scalability as well as provide recommendations on potential improvements. Erik Sandstrom and Aaron Chio share more about the project in this case study. 

Read the AI due diligence case study >> 

4. Understanding AI: Classical Machine Learning vs. Generative AI 

While generative AI has dominated conversations over the past year or so, classical machine learning (ML) remains a critical and widely used form of AI that powers capabilities like predictive analytics, clustering, and recommendation systems. As AI becomes central to operational efficiency and decision-making, organizations must understand the distinct roles of traditional ML vs. GenAI. With most senior data leaders increasing investment in generative AI, clarity across the full AI landscape is essential to drive productivity and improve customer experiences. Read more from Saif Murad in this piece. 

Explore ML vs. Gen AI >>  

5. Opportunities for AI in Drug Product Development 

Artificial Intelligence has rapidly gained traction in the life sciences, including in research and GCP environments, particularly with the FDA announcing plans to deploy AI across all centers following successful pilots that reduced review timelines. While many industry leaders remain cautious due to compliance and regulatory concerns, the FDA’s adoption signals growing acceptance of these technologies. In this article, Lilly Saiontz and Elise Waston discuss how this shift creates an opportunity for pharmaceutical companies to explore AI’s role in drug product development while establishing strong data governance, validation, and training frameworks. 

Explore opportunities for AI in drug product development >> 

6. Best Practices for AI Prompt Engineering in Consumer Products 

AI has become essential for consumer products companies seeking to better understand customers, analyze data, and stay competitive, with prompt engineering playing a critical role in driving meaningful insights. Well-designed prompts enable AI to support everything from personalized marketing and customer feedback analysis to chatbots and product development. To fully realize this value, organizations must follow best practices and avoid common prompt engineering pitfalls – Romeo Denghel shares more in this piece. 

Read best practices & common pitfalls for AI prompt engineering >> 

7. Explainable AI in the Life Sciences Industry: Understanding Your AI Tools 

As AI systems grow more complex, the lack of transparency behind their decisions can undermine trust, especially in high-stakes areas like life sciences. Explainable AI (XAI) seeks to make AI logic more understandable, improving decision-making and supporting ethical use. While XAI lacks clear standards and faces criticism for potentially limiting innovation, understanding how AI works remains critical for organizations deploying these tools in risk-sensitive environments. In this article, Saif Murad shares his thoughts on XAI and the larger push for greater transparency. 

What is Explainable AI? >>  

8. How Search Engine Optimization (SEO) is Changing with AI 

SEO, GEO, AIO, AEO…how can organizations manage their digital presence with the rise of tools like ChatGPT or Claude? SEO has continually evolved, but the rise of large language models and AI-driven search tools is driving a more fundamental shift. As users increasingly rely on conversational AI for direct answers rather than traditional search results, organizations must rethink how they manage visibility and digital presence. In this piece, Brandon Regnerus and Saif Murad explain how the integration of AI-powered search into browsers and enterprise tools is accelerating this transformation. 

Explore considerations for SEO in the age of LLMs >>