From sessions to strategy: Revealing long-term and emerging innovation trends in data analytics, intelligent systems and automation

The Society for Mining, Metallurgy & Exploration (SME) MINEXCHANGE SME Annual Conference & Expo hosts innovation-related content across division sessions each year. The development and integration of automation, robotic systems, artificial intelligence (AI), machine learning (ML), internet of things (IoT), digital twins, drones, remote monitoring and other intelligent systems have regularly been showcased as advancements that can be used to improve safety, efficiency and sustainability across the industry. Although the presence of these conference topics is evident, it is unclear if they are just an expressed interest of industry visionaries and experts or if they may be early indicators of emerging trends. To date, there has not been a systematic analysis of the presence, fluctuation and focus of conference topics over the years. Some sources support the analysis of conference data to help identify emerging trends, opportunities and interests. For example, the International Technology and Engineering Educators Association (ITEEA) completed an analysis of conference programs from 1978 to 2014 to highlight historical milestones and emerging trends within their profession — an effort that assisted with future conferencing planning and other professional events (Reed and LaPorte, 2015). These authors identified several topics that appeared prevalent in the peer and gray literature that were not as prominent in conference offerings. They also indicated that the conference theme had minimal influences on the submission topics (Reed and LaPorte, 2015). In another effort, creators made links between tweets that used conference title hashtags during the Association of College and Research Libraries conferences from 2011 to 2019 as a form of trend analysis (Hennesy et al., 2021). Finally, related efforts have occurred in the text mining of journal article titles to identify latent topics and unique terms in the scientific literature (Ravikumar et al., 2023). Analyzing paper and conference titles has received attention in recent years to help propose and predict research topics (Wang et al., 2022). For example, Wang and colleagues (2022) indicated that trends revealed in paper titles can be useful for proposing more authoritative sessions at major conferences to align with industry trends. Consequently, an abbreviated activity was undertaken by SME’s Industry Innovation Strategic Committee in 2025. The goal was to (1) reveal stable and shifting priorities around innovation, (2) inform respective division planning for subsequent conference sessions and (3) ascertain whether the association is meeting members’ expectations around innovation-related content throughout the year. SME’s Industry Innovation Strategic Committee focused on the identification and dissemination of these tools in the field, and reviewed technical session titles from annual conference programs from 2016 to 2026. Using keyword frequency, theme clustering and division-specific tagging, the committee identified themes that seemed to be gaining traction, fading, remaining consistent, or changing in detail and scope regarding emerging technologies. This short article serves as both a retrospective view and possible planning tool for shaping future conference programming and, if applicable, inform impactful and relevant initiatives. Conference theme frequency analysis Using publicly available PDF annual programs and Excel files outlining the four most recent conference programs, members of the Industry Innovation Strategic Committee reviewed session titles across the program and its eight technical divisions. The focus of analysis remained on the prominence of innovation-related themes. The following keywords were identified by the committee and used to find and filter content: digitalization, data analytics, machine learning, remote sensing, drones, robotics, digital twins, automation, artificial intelligence (AI), process control, smart sensors and autonomous vehicles. Two separate approaches were used to find and filter sessions by keywords, particularly for the 2023 to 2026 programs for which Excel files were available. In one effort, using the Excel files for the 2023 to 2026 programs, NVivo (Lumivero, 2023) was used to thematically analyze the prevalence of topics across session titles. Once the files were uploaded into NVivo, session titles underwent text preprocessing focused on stop word removal to filter out common, uninformative words to aid the downstream analysis (Chai, 2022). Common stop words (such as “and”) as well as domain-specific stop words (such as “mining” and “technology”) were removed to improve topic modeling and reduce keyword distraction to focus on semantically meaningful keywords across the programs. Then, content analysis techniques were used to assess prevalence, relevance and saturation across the keyword topics (Elo and Kyngäs, 2008). Importantly, sessions with “Innovation” and “Technology” (such as Innovations in Dust Control) were not automatically included since these terms do not always denote advanced or novel technologies and processes. As a supplemental exercise, annual programs for the period 2016 to 2024 were analyzed using Microsoft’s Copilot AI tool (Microsoft, 2026) to evaluate and compare the outcomes from the above approach. Copilot’s Word cloud generator was used to generate relative occurrence of identified keywords in these conference programs, by year, to uncover trending subjects. The trends generated from Microsoft’s Copilot AI tool were then thematized from carefully bucketed related terms and plotted against years to observe the changing landscape. Taken together, these two approaches by the authors provided complementary lenses on program content. Specifically, one emphasized systematic thematic prevalence and the other was able to pinpoint longitudinal trends. This combined effort strengthened the committee’s confidence in the findings through triangulation of results. Insights from both approaches were integrated to reconcile overlaps, validate key themes and inform final determinations to reflect thematic depth and temporal context. Insights The analysis provided insights for divisions and members to consider. Based on session titles, content around automation and generative AI, specific to data integration and decision-making, were prominent. A recurring emphasis on health, safety and environmental, social and governance (ESG) innovations and technologies were also observed. More details on some of these aggregated trends are described below, organized by the objectives presented earlier around stable and shifting innovation priorities; division planning; and meeting members’ expectations around innovation-related content. Figure 1 also shows word clouds generated from Microsoft’s Copilot AI tool. Observed trends. Overarching references to automation and smart mining systems encompassed terms like digitization, automation, AI, digital transformation and smart systems. Across programs, some of the topics conveyed included: Use of autonomous systems (such as haulage and drilling). Generative AI for predictive maintenance. Robotics applications in underground operations and inspections. Use of digital twins for operational modeling and training. Sessions also discussed how automation has been applied to the design and construction of infrastructure and equipment, including autonomous fleets, modular and adaptive materials-handling systems, reliability engineering and digital diagnostics. In more recent programs, however, there appeared to be a noticeable shift toward more tailored, next-generation technologies focused on specific sectors or stages of the mining and mineral recovery process, with an emphasis on improving operational efficiency. Examples from the most recent programs include (1) enhanced recovery through sensor-based sorting, (2) AI-driven optimization of milling and flotation circuits and (3) recovery of critical minerals from waste streams. Somewhat relatedly, ESG topics emerged around developing and piloting new technology across site locations. Trends in this area were more recent, especially around environmental innovation (such as smart tailings and water management) including technologies for carbon capture and net-zero mining, water management systems, life-cycle assessments and automated ESG reporting tools. Emerging innovations related to energy systems and equipment design were also increasingly visible. Topics included advances in electrification, low-carbon power solutions, and technologies supporting net-zero mining objectives, alongside the development of next-generation sensors, robotics and adaptive materials handling equipment.Together, these innovations reflected a broader digital and physical transformation of mining operations, extending beyond data and automation to encompass environmental performance, energy efficiency and infrastructure modernization. Over time, sessions have increasingly emphasized scalability and cross-site integration, reflecting a shift from isolated digital tools toward enterprise-level decision support and system-wide optimization. Specifically, data integration, modeling and simulation emerged as foundational enablers of several innovation themes, rather than standalone technologies. These topics overlap with the findings of the committee’s previous CEO Innovation survey (Haas, 2024), suggesting alignment between executive priorities and conference-level technical discourse. Overlapping themes included interoperable platforms across original equipment manufacturer (OEM) systems, the use of edge and cloud computing to support remote and real-time decision-making, and AI-assisted scheduling, modeling and geospatial analytics. This redundancy reinforces the potential value of consistent analysis as a means of tracking how data-driven capabilities mature and diffuse across the industry. Finally, considering human factors specific to worker safety and health was also prevalent regarding the integration of advanced technologies. Over the years, sessions that focused on advancements in real-time dust exposure monitoring, proximity detection and collision avoidance systems, and wearable sensors for fatigue and vital sign monitoring received attention across divisions. Further, the role of behavioral science to support technology integration was also consistent. Figure 2: Visual depiction of themes and categories from 2016 to 2024. Trajectory summaries. Based on this subjective analysis of content, subthemes and examples, topics were organized in one of four categories: Evergreen themes (2016 to 2026): Topics that were consistent across multiple years, indicating stable and enduring areas of industry focus. Emerging themes (post-2022): Topics that subjectively grew in terms of prevalence, reflecting increased attention. Occasional themes: Topics that were more sporadic with a lower overall frequency. These could have corresponded to short-term technical initiatives or regulatory responses. Somewhat fading themes: Topics that subjectively showed a declining frequency over time, suggesting a shift in industry emphasis, maturation of the subject matter, or absorption of the topic into broader thematic categories including those in emerging themes (hence labeling this “somewhat” fading). Figure 2 visually shows themes and categories by year for a quicker grasp of the trends over time. Table 1 shows the subthemes and example topics that were tagged throughout the analysis. Notably, the frequency of session titles specific to automation and related topics seemed to decline over time; however, this trend is not necessarily indicative of shifting priorities. Instead, it may reflect increasing familiarity with these technologies and greater precision in how AI-focused topics are described. To illustrate, in the 2023 conference program, broader titles such as “Automation” and “Smart Mining” were more common but have declined in subsequent years. This shift may suggest that adoption in these areas is maturing and that automation is no longer perceived as novel, but rather increasingly embedded within more detailed process- or sector-specific AI use cases. For example, the 2025 and 2026 conference programs featured more targeted examples of AI adoption and case studies that focused on processes or operational contexts (such as automation in mineral recovery systems). At the same time, the current trend analysis identified gaps in the prevalence of certain topics that have received growing attention in literature, particularly workforce development and human-centered impacts of technology adoption (for example, Togara et al., 2026; Flores-Castañeda et al., 2025; Osei et al., 2025). This observation does not imply these topics are absent from conference programming but rather reflects a subjective limitation of relying solely on session titles to assess thematic emphasis. Division snapshots. Innovation topics were not absent from divisions though innovation-focused content was presented in different levels of specificity. While themes like automation and remote sensing were included across divisions, there were some division-specific trends: Mining & Exploration (M&E): Explicitly plans innovation programming with dedicated tracks (such as AI, automation and digital twins). Environmental: Notable emphasis in 2025 and 2026. Health & Safety (H&S): Hosts a variety of tech-enabled topics (such as AI for hazard recognition) but often uses broader language to describe sessions. Bulk Material Handling (BMH) and Industrial Minerals & Aggregates (IM&A): More recent emphasis on robotics and digitization. The Industry Innovation Strategic Committee perceived session titles becoming more specific over time (such as shifting from “Smart Mining” to “AI in Flotation Optimization”), suggesting maturity in industry focus. The expanding range and increasing specificity of innovation-related topics across divisions might imply growing member interest and engagement, particularly among those proposing and shaping technical content. See Table 2 for previous session titles by year and division. Overlap with CEO Innovation survey and next steps To provide additional context to the observed trends, the committee compared these findings with results from its previous CEO Innovation survey (Haas, 2024). While the session analysis highlights how innovation-related topics are framed and emphasized within professional programming over time, the CEO Innovation survey offers complementary insight into executive perspectives on opportunities, challenges and constraints associated with technology adoption. A few areas of overlap and divergence were identified that may help inform more forward-thinking content during future conferences and throughout the year. The conference themes helped ascertain what members are exploring on behalf of industry interests. The SME’s CEO Innovation survey focused on whether organizations felt ready to implement these innovations. Both efforts revealed an increased focus on data integration, analytics and interoperability. In the survey, specifically, respondents noted challenges around change management, metrics and organizational readiness, to name a few (Haas, 2024). This contrast suggests that while innovation priorities are well aligned, implementation capacity likely remains uneven across organizations. This dichotomy tracks with other environment, health and safety (EHS) 2026 predictions: technological innovations are advancing faster than organizations can adapt and integrate them (HSI, 2026; McGowan, 2025). This is where an interesting dichotomy emerged: there seems to be alignment on what matters but diverging focus on how to operationalize it in the field. Consequently, although conference presentations provide essential knowledge transfer, opportunities remain to complement this content with sessions focused on implementation pathways, systems integration, and lessons learned from deployment. Another interesting dichotomy is, while conference sessions have been trending toward more specific, technical topics, CEO Innovation survey respondents emphasized more sociotechnical interests to support innovation in the field such as workforce development and leadership capability (Haas, 2024). Although advancing technical expertise may be critical for boots-on-the-ground personnel, rebalancing programming to also include aspects of implementation science, systems and culture, may be more relevant for mine executives and senior leaders. This is particularly important given that not all operations face the same constraints. For example, the committee discussed at the 2025 SME Midyear Meeting that greenfield sites may adopt new technologies more readily, whereas brownfield operations often contend with legacy infrastructure, higher retrofit costs, and integration complexity (personal communications, 2025). Systems-level thinking has been touted as a critical skill for working with generative AI and integrative technologies (Brown, 2025). This also aligns with survey respondents’ sentiment around the need for coherent and integral systems to accommodate technological advancements. Cross-division sessions, interoperability-focused case studies, and examples drawn from both large and small operations could help bridge this gap by translating technical advances into actionable, context-specific implementation strategies. In this regard, supplemental indicators such as session attendance may also help distinguish emerging areas of high member interest from topics that have reached a plateau, further informing future programming priorities. Summary This preliminary evaluation was an initial, enlightening mechanism for revealing perceived, emerging priorities and facilitating knowledge exchange across the industry. At the same time, there are limitations of such analyses and findings to consider. First, program planning leadership changes annually, so the presence or absence of certain topics does not necessarily reflect division-level priorities or sustained strategic intent. Rather, since division program chairs turn over annually, and these individuals may recruit session chairs who select topics based on personal interests or subject-matter expertise, final program content can be somewhat subjective. Relatedly, there may be limited knowledge in emerging topics that are important to members and therefore, session chairs may not propose or recruit advanced content. Additionally, session titles alone do not fully capture the depth or focus of content delivered during presentations. Despite these limitations, examining trends over multiple years still provided areas of continuity that can inform future planning. Looking ahead, these findings raise considerations for how SME and the Industry Innovation Strategic Committee, specifically, might further enhance member value. Opportunities exist to more systematically review, measure attendance or engagement during sessions, and tag recent sessions aligned with innovation themes to better understand coverage and identify gaps. Greater coordination across divisions could also support interdisciplinary programming that reflects the increasingly interconnected nature of innovation, such as content that integrates ESG considerations with AI, automation or robotics. In addition, members may benefit from more applied case studies that highlight real-world implementation experiences, particularly from those who face unique resource and infrastructure constraints. Finally, intentionally engaging early-career professionals as speakers, organizers and session chairs for technology-forward content may help support relevant and responsive programming to the evolving workforce. Collectively, these efforts could enhance SME’s role in not only showcasing innovation but also supporting its effective translation into practice across operational contexts. (Note: This article is primarily focused on professional education and the education opportunities available through professional conferences such as the Society for Mining, Metallurgy & Exploration’s MINEXCHANGE SME Annual Conference & Expo.) Disclaimer The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention or Nalco Water. Generative AI disclaimer A public version of Microsoft Copilot was used with publicly available conference program documents to produce visualizations and data trends. The content has been reviewed and corroborated with another data analysis tool but should be treated as supplementary information rather than authoritative. References Brown, B. (2025). Strong Ground: The Lessons of Daring Leadership, the Tenacity of Paradox and the Wisdom of the Human Spirit. Random House.Chai CP. Comparison of text preprocessing methods. Natural Language Engineering. 2023;29(3):509-553. doi:10.1017/S1351324922000213Elo, S., & Kyngäs, H. (2008). The qualitative content analysis process. Journal of advanced nursing, 62(1), 107-115.Flores-Castañeda, R. O., Olaya-Cotera, S., López-Porras, M., Tarmeño-Juscamaita, E., & Iparraguirre-Villanueva, O. (2025). Technological advances and trends in the mining industry: a systematic review. Mineral Economics, 38(2), 221-236.Haas, E.J. (2024). SME strategic committee shares: Challenges and opportunities in technology innovation and integration. Mining Engineering, 76(9), 73, 78-79.Hennesy, C. K. (Creator), Hanson, M. (Creator), Adams, A. L. (Creator) (Mar 17 2021). ACRL Conference Twitter IDs and Session Descriptions, 2011-2019. Data Repository for the University of Minnesota. https://experts.umn.edu/en/datasets/acrl-conference-twitter-ids-and-session-descriptions-2011-2019/HSI (2026). What EHS Leaders Are Prioritizing — and Dropping — in 2026. Retrieved on February 10, 2026 from https://hsi.com/blog/ehs-strategy-2026-guide.Lumivero (2023). Nvivo (Version 14) www.lumivero.com.McGowan, B. (2025). The Safety Revolution Arrives in 2026: How AI and Data Will Redefine Workplace Risk. Retrieved on February 10, 2026 from https://ehsleaders.org/2025/10/the-safety-revolution-arrives-in-2026-how-ai-and-data-will-redefine-workplace-risk/.Microsoft (2025). Copilot (GPT-4). https://copilot.microsoft.comOsei, R., Frimpong, S., & Venkat, A. (2025). Human-machine collaboration in mining: A critical review of emerging frontiers of intelligence systems in the mining industry. The Extractive Industries and Society, 24, 101746.Ravikumar, S., Boruah, B. B., & Gayang, F. L. (2023). Text Mining of Journal Article Titles: An LDA-Based Topic Modeling Approach. Journal of Information and Knowledge, 60(5), 289–295. https://doi.org/10.17821/srels/2023/v60i5/170707. https://www.srels.org/index.php/sjim/article/view/170707Reed, P.A. and LaPorte, J.E. (2015) ‘A Content Analysis of AIAA/ITEA/ITEEA Conference Special Interest Sessions: 1978–2014’, Journal of Technology Education, 26(3), p. 38-72. https://jte-journal.org/articles/10.21061/jte.v26i3.a.2.Togara, T. P., Madahana, M., & Olugu, E. U. (2026). Overcoming barriers to the adoption of technology in the mining industry to enhance safety and productivity: A scoping review. In EPJ Web of Conferences (Vol. 347, p. 03002). EDP Sciences.Wang, J., Xu, J., Chen, W. et al. When Research Topic Trend Prediction Meets Fact-Based Annotations. Data Sci. Eng. 7, 316–327 (2022). https://doi.org/10.1007/s41019-022-00197-1

From sessions to strategy: Revealing long-term and emerging innovation trends in data analytics, intelligent systems and automation

Authored by: Emily Haas and Rajesh Raitani

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