Other Areas of Interest:

• Pull together consumer signals from across sources into a picture that functional teams can act on; detect sustainability-driven consumer shifts before they reach mainstream demand
  Consumer views on sustainability are spread across social platforms, retail data, academic papers, regulatory filings, and NGO reports. There is no automated system yet to continuously collect, deduplicate, and classify these signals in one place, so the same signal gets missed, rediscovered, or read differently across teams, and no one has a shared view of what consumers are actually signalling when it comes to sustainability. Next-generation FTR with agentic workstreams can solve it, but we need dedicated resources to make it happen,
  
  Additionally, services like Nextatlas can identify weak signals 12–24 months before they become mainstream, but they are not yet focused on sustainability specifically. We can create a dedicated sustainability tracker, this also requires dedicated effort and resources.
  
  Most importantly, this initiative requires commitment from decision makers to regularly review the outcomes and act on recommendations.
• AI or digital enabled drop-in tools / solutions to support ESG reporting across juristication: analysis of alignment between different ESG regulations (make the interoperability transparent), regulation scan to analyze applicability of regulation to a company, draft compliant report (narratives) based on existing documentation provided (policies, strategy, etc)
• AI/ Digital enabled drop in solutions to help with ESG Data development, processing or ease of assurance: Data validation checks (variance, hygiene checks replacing manual reviews), Automation of data collection
• AI-driven ESG reporting PMO platform that integrates and automates the core consultancy workflow: Automated Gap Assessment, Roadmap & Timeline Generation, project manager, assigning tasks to internal owners, tracking progress, sending reminders, and escalating delays
• Solution to create within a DataLake for data ingestion from templated data files form external sources into the datalake applying data quality check (without human oversight).
• Solutions for realtime Datalake refresh every time a new file has been uploaded the Master gets uploaded realtime.
• Increase of seamless DataLake ingestion of variable data (hedonic, intensity, JAR, Y/N, Qual, same file with flexibility for absorption within limits.)
• Modelling of varied sources of data (TCR, HI, Marketing) to predict consumer liking, Preference and Repeat Purchase, Likelihood to win on market.
• Data Clusterization for Proxy Markets, a food scientist statistician and data modelling and analyst marriage, best approach in our industry and real live application with demonstrated business impact.
• Sensory digital twin. AI driven twin to assess sensory profile of food and beverages based on ingredient and analytical data.
• Consumer digital twin to predict product performance and shopping behaviour based on formula, ingredient, marketing strategy (end goal) and economical context.
• Advance AI-Driven/ machine learning to predict future market success based on consumer data, marketing investment, competitive landscape.
• Robust AI driven /machine learning models that bridge the gap between product ingredients, instrumental data, sensory profiles, consumer insights, and market performance to guide the formula creation and create optimum solutions.
• Dynamic modelling to optimize formula based on exogenous shocks (supply chain, cost regulation...).
• Advance AI-Driven predictive model to optimize resource between R&D, Supply chain and Marketing for optimum business output. Guide optimum R&D, supply chain and marketing investment to drive revenue growth
• AI-driven predictive analytics to understand evolving consumer behaviors, new consumer needs and tensions, machine learning models for dynamic segmentation and real-time feedback analysis, and sentiment analysis from diverse datasets. Agile VoTC (voice of the consumer), S-Curve on local and global trends and approaches beyond Sprinker for reliable data whilst reduced timing.
• Tools to continuously identify, capture and highlight low signal insight (consumer, competitive tracking...)
• AI-driven analysis among: (1) predicted consumer preference, purchase intent, likelihood to win (2) market execution data (3) Post-launch review data, to explore the gap, and help to evolve prediction model