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Coverage of sessions from COLLIDE, the largest AI conference in the Southern U.S.
See the 2027 eventStalled AI initiatives usually suffer from unclear goals and weak alignment, not weak technology, says Analytic Vizion founder Nelson Davis. He lays out an impact-first planning sequence and five questions leaders can use to get projects moving again.
By The AI Enterprise Magazine Team ·
By The AI Enterprise Magazine Team ·
By The AI Enterprise Magazine Team ·
By The AI Enterprise Magazine Team ·
Data and AI leaders from Honeywell, Capital One, Synchrony, TelevisaUnivision and EXP Realty describe what it takes to move AI agents from prototype to production: real use cases, context engineering, agent memory, least-privilege access and auditable agent behavior.
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AI stalls on old data problems that need a new fix, argues Actian CTO Emma McGrattan: treating data as a product and governing it with data contracts. She cites survey results linking company-wide data products to AI in production and offers a one-project way to start.
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AI has no control plane yet, leaving agents loose in brownfield data estates, argues NexusOne CEO Anu Jain. He lays out three questions every leader should answer, the case for one identity across all systems, and why companies should own their ontologies.
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When an AI agent gets a refund policy wrong, the model often never saw the policy. Redis Senior Solution Architect Jessica Emmons breaks down context engineering, the four ways context fails, why basic RAG isn't enough and how context engines, agent memory and semantic caching help.
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Good AI ideas usually stall waiting on other teams, separate systems and security reviews, says Google Cloud's Shan Kulandaivel. Forecasting, prediction, document processing, graph and vector search inside BigQuery let SQL users take those ideas to production, he argues.
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Deepfake fraud attempts have surged 21,000% in three years, according to TrendAI Global Deputy CISO Sundari Parekh. She breaks down the Arup and Ferrari cases, why defenses must run at machine speed and what security teams should do in the next 90 days.
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AI is only as trustworthy as the data and governance behind it. Leaders from Bank of America, Synchrony, Alteryx and Truist make the case for fixing data at the source, measuring fitness for purpose, rethinking model risk governance and naming a single accountable owner.
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General-purpose AI tools often win analytics pilots and then break down at scale, says WisdomAI's Geetesh Iyer. The missing piece, he argues, is managed enterprise context, and the people best suited to own it, AI context engineers, are already on data teams.
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Building an AI agent is easy; running thousands of them is not. IBM's Ann Leach says agent sprawl creates security and cost risk, lists five questions an agentic control plane must answer and describes how Starlink coordinated procurement agents.
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Matillion's Maia agent builds, tests and documents data pipelines from plain-language requests. Matillion's Arawan Gajajiva argues that data engineers' work is shifting from writing code to governing, planning and evaluating what AI builds.
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Data quality is an input, not the goal, says Informatica's Peter Manta. What matters is whether AI agents make good decisions. He outlines six pillars of an agentic data contract and an experiment showing that even 10% duplicate data changed agent decisions.
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Wendy's FreshAi, built with Google Cloud, now takes 95% of orders at its AI drive-thrus. Product owner Will Croushorn explains how small behavioral signals, Reddit and TikTok posts, and a new 'sorry' metric shape the product, and how the team improved it for customers who stutter.
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Clients love charts but rarely know which ones they want. Broadband Insights CEO Patrick Hutto says his team uses vibe analytics with Plotly's AI Studio to explore data with clients in real time, then moves the best views into production.
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AI leaders from Walmart, AT&T, Kellanova and TelevisaUnivision describe how they build trust in AI agents with employees and customers, from AI boards and non-negotiable guardrails to the risks of model upgrades and Walmart's plan to anchor every agent to four super agents.
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IBM switched its HR system from Workday to SAP and most employees never noticed, because their HR interface was an AI agent. IBM's Melissa Hadley explains systems of intelligence, the problem of agent sprawl and how orchestrator agents route work across systems.
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AI governance and data science freedom don't have to conflict, say SAS's Sophia Rowland and Sundaresh Sankaran. Governed synthetic data generation protects personal information, produces representative and robust data sets, and is measured for both similarity and disclosure risk.
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Drawing on about 120 AI projects, Nanonets CEO Sarthak Jain says the few that succeed rely on long, hands-on iteration, human controls, clean data and bottom-up adoption, not budgets or executive mandates.
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Banks and payment companies get about 50 milliseconds to approve a card swipe. Aerospike's Sesh Seshadri says database speed decides how much machine learning, graph analytics and multi-stage inference fit in that window.
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Data and AI leaders from The Home Depot, Equifax and Mars Wrigley tie every AI use case to a business OKR, trade off speed, accuracy and cost, and argue that AI belongs in the middle of the process, not first.
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AI will make leadership more human, not less, argues Analytic Vizion CEO and founder Nelson Davis. He outlines three keystone habits for data and analytics leaders and a simple matrix for deciding when AI should automate, augment or advise on a decision.
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GenAI pilots stall because people don't trust them, not because models are weak, argues Snorkel AI Head of Solutions Dennis Panos. He says an evaluation-first process built with subject matter experts took two stalled projects to production in about two months.
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Infrastructure cost decides whether real-time AI pays off, argues Aerospike founder and CTO Srini Srinivasan. He explains the company's hybrid memory architecture, how faster lookups improve fraud and recommendation decisions, and why agents will multiply database load.
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IBM's Ryan Zombo lays out five questions enterprises must answer to move agentic AI into production, from avoiding agent sprawl to avoiding lock-in. Governance works like brakes on a race car, he argues, citing productivity results from IBM's internal client zero program.
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Google Cloud's data science agent can plan and build a demand forecast in a BigQuery notebook, even fixing its own error along the way. Google Cloud's Suda Srinivasan calls agents an exoskeleton for data scientists, not a replacement.
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Leaderboard scores rarely predict how a model will handle a company's own data. HumanSignal ML Evangelist Micaela Kaplan explains how enterprises can build custom AI benchmarks with rubric-based scoring, start small and bring domain experts into the process.
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No one is closer to enterprise data than the integration platform, argues SnapLogic Senior Solutions Engineer Dylan Vetter. He explains how IT teams can share governed integration assets across business units and build custom MCP servers that connect LLMs to company data.
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Most organizations struggling for AI returns lack readiness, not technology, says Elder Research's Jennifer Schaff. Using a football analogy and client stories, she identifies eight gaps that quietly erode ROI, from strategy misalignment and fragmented analytics to failed adoption and weak data literacy.
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AI agents raise the bar for data quality, say data and AI leaders from TK Elevator, Baptist Health South Florida and Novelis. They outline a five-step path from messy data to agent-ready data, governance for agents as digital employees and a hub-and-spoke model for scaling.
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AI pilots often fail in production because they run on siloed slices of untrusted data. Syncari Principal Solutions Engineer Jack Smith argues that more integration tools and data lakes won't fix that, and makes the case for a centralized, agentic master data layer.
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Machine learning, generative AI and agentic AI each solve a different problem in a legacy quoting workflow, says KMS Technology CTO Guy Merritt. He outlines a prepare, diagnose and implement path for enterprises and shares KMS's own results from AI-assisted software development.
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AI breaks traditional data governance, say leaders from Quest Software, ADP, WellStar Health System and Carter's. Their fixes include trust scoring for data, governance by design, fast risk assessments and team structures that scale governance without slowing delivery.
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Trust in AI comes from transparency, say leaders from The Coca-Cola Company, UnitedHealth Group, The Clorox Company and Randstad Enterprise. They make the case for measuring AI by business value instead of headcount, role-specific training and clear ownership of AI fluency.
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Grid Dynamics built an internal agent mesh for AI agent collaboration. Search and AI Architect Stas Stolpovskiy explains what a central team must control, why platforms need a few strict rules and lots of freedom, and why he thinks MCP falls short for agent-to-agent work.
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In a load test, Aerospike Solution Architect Stan Christ took two of eight database nodes offline and the cluster rebalanced without help from IT. Aerospike's graph-based fraud detection also blocked suspicious transfers by tracing relationships many hops away.
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In a COLLIDE 2024 keynote, Elder Research's John Elder described two crises: findings that do not replicate and data science projects that never reach production. He showed target shuffling, a simulation that adjusts for cherry-picking, and shared change management lessons.
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At COLLIDE 2024 in Atlanta, Informatica's Peter Manta said AI projects succeed or fail on data. He offered a three-part test of robust, relevant and responsible data, and urged teams to involve the data office early and pilot small.
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At COLLIDE 2024 in Atlanta, AI Connect co-founder Brian Mink outlined five themes from data executives: feeling behind on AI, board pressure, data strategy, human-centric AI and bias. His central point was that organizational practice has to catch up with the technology.
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Trust in AI depends on privacy, security and governance working together. Leaders from TD Bank, TelevisaUnivision, Vertex and Truist weigh in on LLM privacy risks, AI ethics boards, the limits of explainability and why companies should build the cake before adding generative AI as the cherry.
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Enterprises are using retrieval-augmented generation for more than chatbots. Leaders from UPS, TSYS and Shipt describe a digital twin operations copilot, internal use cases that build trust, the risks of customer-facing RAG and how to prioritize generative AI under top-down pressure.
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In a COLLIDE 2024 session, Esri's Brady Cline argued that location is an underused ingredient in enterprise analytics. He outlined business questions spatial data can answer and showed a recorded demo of GeoAnalytics Engine analyzing millions of customer records inside Spark.
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In a COLLIDE 2024 session, Vespa.ai's Kristian Aune ran a classification experiment using retrieved examples and a language model, reaching 94% accuracy in half a day. He compared text and vector search, model sizes and example counts, and described a data flywheel.
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Most companies still equate generative AI with chatbots. Leaders from Dollar General, Scripps, TelevisaUnivision, Vertex and PRGX describe content tagging, dynamic dashboards, code translation and survey analysis, and are candid about security risks, unreliable outputs and measuring ROI.
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AI and data leaders from Nike, ADP and Yum! Brands look three to five years ahead. They agree that humans stay in the loop, that data can no longer be an afterthought, and that governance only works when everyone who creates data takes part.
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At COLLIDE 2024 in Atlanta, Snowplow co-founder Yali Sassoon said AI advantage comes from feedback loops that generate proprietary data. He described Klarna's support agent as an example and listed five ingredients for building a data flywheel.
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How do you open data to non-technical employees without exposing sensitive information? Leaders from Piedmont, The Home Depot, Alteryx and BBDO point to certified data sets, business-owned data hubs, time-limited PII access and a break-the-glass log for health data.
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In a COLLIDE 2024 session, WhereScape's Patrick O'Halloran compared small, mid-sized and large companies building data foundations in the cloud. He recommended narrow scope, rapid prototyping and treating the work as a business process, with automation to cut testing and rework.
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In a COLLIDE 2024 session, Softcrylic showed AudienceMatchAI, an early-stage tool that runs unsupervised customer segmentation and lets marketers question the segments through a language model. The team said limiting the data the model sees reduces hallucinations and makes answers easy to check.
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CEOs want AI fast, but leaders who deliver it say fundamentals decide the pace. Leaders from Elder Research, Norfolk Southern, Equifax and Baptist Health weigh in on model drift, AI governance councils and talent, plus results from AI train inspections and ambient listening.
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AI strategies fail when the data underneath them isn't right. Leaders from Southern Company, Clorox, Morgan Stanley and Tecton explain how to tie data strategy to business value, break down silos with standardized features, and scale pilots that break at full volume.
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AI ROI is won or lost at every stage of a project. Leaders from Bank of America, Carelon Health, Lockton and PRGX break down business cases, the three E's of measuring value, data and training bottlenecks, and how unmonitored cloud and GPU spending erodes returns.
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How do AI's risks and rewards change from public health to hotels to telecom? Leaders from NORC at the University of Chicago, IHG and AT&T weigh in on hallucination and bias, proving business value, governance practices and why scaling AI too fast can sacrifice quality.
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At COLLIDE 2024 in Atlanta, Pure Storage's Chad Hendren said AI pilots often break when demand grows, because storage, metadata and power limits start to bite. He shared his company's claims on parallelized file tools, flash density and customer results.
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In a COLLIDE 2024 session, Fiddler AI founder and CEO Krishna Gade argued that enterprises need an observability layer for generative AI. He described how small fine-tuned trust models score hallucinations, toxicity and safety, and how regulation shapes governance.
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At COLLIDE 2024 in Atlanta, DataRobot's Jessica Lin said no single defense can govern generative AI, so enterprises should layer people, processes and auditability. She described model-agnostic governance, compliance documentation and guardrails that block risky inputs and outputs.
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At COLLIDE 2023 in Atlanta, Zilliz developer advocate Yujian Tang explained why vector databases matter for AI applications. He covered retrieval augmented generation, semantic similarity, and why SQL databases and search libraries fall short at scale.
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At COLLIDE 2023 in Atlanta, Georgia State University's Peter Molnar compared symbolic AI with neural networks and large language models. He said the approaches have complementary strengths, and that LLMs could help automate knowledge graphs and rule-based expert systems.
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In a keynote at COLLIDE 2023 in Atlanta, Stardog VP of Product Navin Sharma argued that enterprise knowledge graphs give generative AI the business context it lacks. He said the pairing can improve accuracy, cut hallucinations and make answers explainable.
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At COLLIDE 2023 in Atlanta, a panel of data consumers from Home Depot, Mobilewalla and Major League Baseball explained what makes data science useful to them. They described aligning on business goals, translating metrics into plain language, and building relationships between analysts and decision-makers.
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At COLLIDE 2023 in Atlanta, RelationalAI's Nikolaos Vasiloglou explained why cloud data platforms with hundreds of millions of columns need a semantic layer. He described how ontologies, human review and knowledge graphs help language models answer questions.
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At COLLIDE 2023, Redis principal solution architect Anton Umnikov explained how transformers led to embeddings and ChatGPT, and how a RAG system fits together. His advice: pick embeddings, vector database and LLM independently, and remember that vectors are easy while databases are hard.
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At COLLIDE 2023 in Atlanta, Cox Automotive's John Kang described how Manheim moved capacity planning from Excel to a Python app built with Dash Enterprise and Plotly Professional Services. The tool helps 50-plus auction sites plan technicians and equipment through scenario planning.
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At COLLIDE 2023, six data leaders from Warner Bros. Discovery, Inspire Brands, AT&T, Morgan Stanley, Kaiser Permanente and American Express discussed paths to leadership. They stressed soft skills, continuous learning, alignment with business goals and the ability to translate technical work.
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At COLLIDE 2023, Graft CEO Adam Oliner said a good AI strategy must use all of a company's data, support experimentation, lift the whole organization and empower everyone. His conclusion, from a vendor's perspective, was to adopt a modern AI platform rather than build one.
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At COLLIDE 2023, Morgan Stanley Wealth Management's Atul Dalmia described AI systems for personalized client content, adviser next-best-action and lead matching, with reported lifts in contact, engagement and conversion. His lesson: solve the business problem first and augment what already exists.
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At COLLIDE 2023 in Atlanta, AI Connect co-founder Brian Mink explained how economic decision makers differ from technical practitioners. He outlined why data executives are risk averse and five tactics for reaching them.
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At COLLIDE 2023 in Atlanta, Neo4j's Phani Dathar said knowledge graphs can address LLM inaccuracy and missing company knowledge. He described a connect, enrich and consume approach that pairs vector search and generated graph queries with an LLM.
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At COLLIDE 2023 in Atlanta, Dremio's Jeremiah Morrow demonstrated how branching in the Dremio Arctic Iceberg catalog gives data scientists isolated, zero-copy clones of production data. The demo showed a branch, a tagged change, a merge to main and a one-command rollback.
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At COLLIDE 2023 in Atlanta, Fiddler AI co-founder and CEO Krishna Gade outlined the risks of large language models, including privacy leaks, hallucinations, unreliable self-explanations and bias. He argued that AI observability is the missing link for deploying generative AI responsibly.
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At COLLIDE 2023, Bill Franks of Kennesaw State University said generative AI is effectively making up every answer, which is why it is right on well-documented topics and unreliable on obscure ones. He expected companies to differentiate by building application layers on commoditized models.
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At COLLIDE 2023, a panel of data and AI leaders from Kellogg's, Nike, Randstad Enterprise and Stardog, moderated by Carter's, weighed generative AI's opportunities against bias, hallucinations and privacy risk. They urged AI principles, testing, human oversight and caution with company data.
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At COLLIDE 2023 in Atlanta, Vida Williams of the Virginia Alcoholic Beverage Control Authority looked back on 25 years in data and enterprise architecture. She drew lessons on data modeling, executive attrition, data-driven culture and the slow ROI of data, then cautioned against tech for tech's sake in generative AI.
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At COLLIDE 2023 in Atlanta, H2O.ai founder and CEO Sri Ambati demonstrated open-source LLMs, RAG over private documents and AutoML. He also argued that prompts and community are the new moats and predicted data scientists will lead Fortune 500 companies by 2030.
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At COLLIDE 2023 in Atlanta, data science leaders from Best Buy, Equifax and Google discussed how generative AI and other emerging technologies would reshape roles and skills. They covered hard and soft skills, production trade-offs, regulatory risk and what hiring managers look for.
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At COLLIDE 2023 in Atlanta, Redis Field CTO Allen Terleto argued that data platforms are converging and that vector search is the newest capability vendors are adding. He explained how RAG and semantic caching can reduce LLM cost, latency and data risk, and what enterprises should weigh when choosing a vector database.
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At COLLIDE 2023, Ashani Johnson-Turbes of NORC at the University of Chicago argued that data democratization must cover who is in the data, who has the skills to use it and who shares in the power it creates. She offered six steps, from community advisory boards to data parties.
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At COLLIDE 2023 in Atlanta, dotData CEO Ryohei Fujimaki argued that generating features automatically from raw business tables is faster and more reproducible than hand-built features. He also described three ways generative AI is being applied to unstructured text, conversational feature creation and feature interpretation.
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At COLLIDE 2023, AnswerRocket CTO Mike Finley used a recorded demo to show how a generative AI copilot for analytics is taught new skills. A pasted JSON block and a short Python routine added e-commerce and cohort analysis, with numbers verified against the data.
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At COLLIDE 2023, Sol Madadha of Regions said businesses can serve customers better by combining the many signals they emit. He pointed to clean data, clear use cases, return on investment and governance as the prerequisites for enterprise adoption.
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At COLLIDE 2023 in Atlanta, Burtch Works' Beverly Wright and Crunchbase's Jess Ramos shared career advice for data science and AI professionals at three stages. Topics included choosing a good manager, ethics, soft skills, specializing and mentoring.
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At COLLIDE 2023 in Atlanta, Intuit Factory founder Roop Singh argued that the winners in enterprise technology will combine AI, blockchain and data. He predicted priced data marketplaces and cheaper models that let small firms compete.
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At COLLIDE 2023 in Atlanta, Datalogz CEO Logan Havern said AI copilots in BI tools will make dashboards easier to build and sprawl worse. He outlined a BIOps strategy to find unused, duplicate and risky reports and cut cost, citing his company's client results.
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In a session sponsored by Lenovo at COLLIDE 2023 in Atlanta, Mike Leach argued that AI teams should match hardware to the workload and treat workstations as an on-ramp to the data center. He also said IT, data science and finance need to plan AI spending together.
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At COLLIDE 2023 in Atlanta, dotData's Ian McKenna demonstrated a way to turn disjointed tables into a flat, model-ready feature table. He argued for letting the data surface features statistically, then reviewing them with experts.
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At COLLIDE 2023 in Atlanta, analytics leaders from Aflac, Carter's, DataRobot and RXA @ OneMagnify discussed why AI projects stall and how to choose ones that pay off. They described feasibility tests, governance and ethics, and what they expected from generative AI at the time.
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At COLLIDE 2023, AnswerRocket's COO and CTO discussed how to get past generative AI hype in business analytics. They argued for higher-order business questions, using models to plan analysis rather than write SQL, and starting with one recurring, labor-intensive task.
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