Large language models (LLMs) have shown promising accuracy in predicting survey responses and policy preferences, which has increased interest in their potential to represent human interests in various domains. Most existing research has focused on "behavioral cloning", effectively evaluating how well models reproduce individuals' expressed preferences. Drawing on theories of political representation, we highlight an underexplored design trade-off: whether AI systems should act as delegates, mirroring expressed preferences, or as trustees, exercising judgment about what best serves an individual's interests. This trade-off is closely related to issues of LLM sycophancy, where models can encourage behavior or validate beliefs that may be aligned with a user's short-term preferences, but is detrimental to their long-term interests. Through a series of experiments simulating votes on various policy issues in the U.S. context, we apply a temporal utility framework that weighs short and long-term interests (simulating a trustee role) and compare voting outcomes to behavior-cloning models (simulating a delegate). We find that trustee-style predictions weighted toward long-term interests produce policy decisions that align more closely with expert consensus on well-understood issues, but also show greater bias toward models' default stances on topics lacking clear agreement. These findings reveal a fundamental trade-off in designing AI systems to represent human interests. Delegate models better preserve user autonomy but may diverge from well-supported policy positions, while trustee models can promote welfare on well-understood issues yet risk paternalism and bias on subjective topics.

Expert consensus plays a critical role in domains where evidence is complex, conflicting, or insufficient for direct prescription. Traditional methods, such as Delphi studies, consensus conferences, and systematic guideline synthesis, offer structure but face limitations including high panel burden, interpretive oversimplification, and suppression of conditional nuance. These challenges are now exacerbated by information overload, fragmentation of the evidence base, and increasing reliance on publicly available sources that lack expert filtering. This study introduces and evaluates a Human-AI Hybrid Delphi (HAH-Delphi) framework designed to augment expert consensus development by integrating a generative AI model (Gemini 2.5 Pro), small panels of senior human experts, and structured facilitation. The HAH-Delphi was tested in three phases: retrospective replication, prospective comparison, and applied deployment in two applied domains (endurance training and resistance and mixed cardio/strength training). The AI replicated 95% of published expert consensus conclusions in Phase I and showed 95% directional agreement with senior human experts in Phase II, though it lacked experiential and pragmatic nuance. In Phase III, compact panels of six senior experts achieved >90% consensus coverage and reached thematic saturation before the final participant. The AI provided consistent, literature-grounded scaffolding that supported divergence resolution and accelerated saturation. The HAH-Delphi framework offers a flexible, scalable approach for generating high-quality, context-sensitive consensus. Its successful application across health, coaching, and performance science confirms its methodological robustness and supports its use as a foundation for generating conditional, personalised guidance and published consensus frameworks at scale.

Machine learning offers an opportunity to leverage competition and new forms of collaboration in order to yield new products, services, and entire business models… but machine learning misconceptions run rampant. In such a nascent niche, it's helpful to gather expert consensus in how best to apply machine learning in business. As a company, it's tempting to see other businesses yielding tangible results from applying'machine learning' technologies and to want to immediately jump on the boat. But good business sense also dictates not getting involved in a new venture before having done due diligence in understanding what a technology is – the potential capabilities and limitations, risks and rewards, and relevance in context to what a particular company produces or provides. "What do you believe to be the biggest misconception that executives and businesspeople have in applying machine learning to business opportunities?"

When it comes to business applications of machine learning, marketing is always near the top of the list. Modern digital marketing offers a huge volume of quantifiable data for teams to work with, and marketing can be said to take precedent over other areas like customer service and business intelligence because of it's direct tie to driving revenue. Machine learning marketing applications are still relatively novel for most small and medium-sized business, but this may change drastically over the next five years. In this expert consensus, we reached out to over 50 executives running companies at the intersection of AI and marketing. Our goal was to determine the applications of machine learning and AI that are driving strong business value now, as well as the applications that would make the biggest different in the next five years.

In July of this year, we sent out a series of survey questions to past guests who have been featured on the TechEmergence podcast, including academic researchers, founders, and executives who are experts in the machine learning domain. "What industries do you believe to be most poised to take advantage of machine learning in a business context?" We received 58 total responses from 30 researchers and executives (the survey structure allowed respondents to choose from one to four relevant response categories, with an average individual response rate of 1.93 chosen categories). On the whole, the trend in responses aligns with what we might have predicted, based on previous proprietary editorials/research and external published media on the topic. For example, the apparent optimistic bent towards healthcare & pharmaceuticals, followed by eCommerce, aligns with CB Insights' tracking of popular areas for artificial intelligence venture capital deals in 2016 (an exception is robotics, though this may be a domain "on the horizon").

In addition to targeting customers based on inferred wants and needs, a compelling facet of personalization is something Forrester Research identifies as "Operationalizing Emotion", which alludes to customers making purchasing decisions based as much (or more) on emotional experiences than on rational conclusions. Today's market is all the more risky for companies that don't provide a stellar customer experience from start to finish, and it's becoming more common for businesses to suffer longer-term revenue losses for a single negative experience, whether directly experienced by the customer or based on empathy for others' experiences. A quick and personalized response to any customer dissatisfaction seems almost an essential application for businesses that want to stay afloat for the long-haul.

In July of this year, we sent out a series of survey questions to past guests who have been featured on the TechEmergence podcast, including academic researchers, founders, and executives who are experts in the machine learning domain. We received 58 total responses from 30 researchers and executives (the survey structure allowed respondents to choose from one to four relevant response categories, with an average individual response rate of 1.93 chosen categories). On the whole, the trend in responses aligns with what we might have predicted, based on previous proprietary editorials/research and external published media on the topic. For example, the apparent optimistic bent towards healthcare & pharmaceuticals, followed by eCommerce, aligns with CB Insights' tracking of popular areas for artificial intelligence venture capital deals in 2016 (an exception is robotics, though this may be a domain "on the horizon"). At the end of this article, we expand on insights received from our survey, specifically taking a closer look at trends in the industries where experts expressed the most optimism and touching on why some of these industries are more likely to be disrupted than others.

Unlike other components to an enterprises' technology mix, determining the ROI of machine learning is a less-than-obvious process, particularly when solutions are new and little by way of case studies or benchmarks exist. While we're far from a world where SMBs (small- and mid-sized businesses) outside of Silicon Valley integrate AI into their regular operations, we will undoubtedly see an explosion of novel uses in industry and enterprise over the next 5 to 10 years, and executives are rightly concerned with how to make the most of those technology, time, and staffing decisions. If you're a business who's new to the machine learning scene (and that's a vast majority), there are more burning questions than answers at present. "What are the criterion needed for a company to derive maximal value from the application of machine learning in a business problem?" Tapping into our hundreds of interviews (on our podcast and otherwise), as well as reaching out to other experts in the field, allowed us to glean valuable insight from researchers and executives across the globe.