LHP’s Synopsis on the 2025 Gartner Data and Analytics Conference

Introduction

Most in the industry know that Gartner is one of the leading research, conference, and digital market frameworks, and consulting companies in the United States. But is what’s presented from industry behemoths and prevailing buzz in the market what is resonating with the attendees and audience? LHP had an opportunity to be an Integrator Exhibitor at this year’s Gartner Data and Analytics Conference in March in Orlando, and this is what we heard and experienced from the four days we were there drilling into the actionable tactics currently needed to deploy the insights and strategies of the conference where everyone had to spell or mention, AI.

Main Themes of the 2025 Gartner Data & Analytics Conference

This year, the main themes at the Gartner conference were Data Governance and, of course, AI. Within AI, most of the discussion centered around AI Agents and Large Language Models (LLMs). AI Agents are still in their infancy, and some of the large vendors brought their newest and latest concepts to the conference. While our experience was that these agents are not quite there yet to be classified as autonomous, perceptive, or multi-functional, the potential and fast pace of evolution is exciting, so much so that internally we are developing AI agents to communicate to relational databases, NoSQL, APIs, cloud repositories, and others with an enterprise (not consumer) centric vision. What we tell our customers when they ask us about AI and if they should consider it is simply the following: “remember, this is the dumbest these tools will ever be; they will only improve.” The future is exciting, and we want to be a part of that future deployment. From a Data Governance perspective, the Gartner Framework pillars of accountability and transparency seemed to dominate another corner of the room from the attendees’ questions (directed to LHP at our booth) and from the many vendors trying to provide products that solved this problem.

LHP Demos Presented at the Gartner Conference

LHP brainstormed prior to the event and brought three demos to our Gartner booth to attract attention and create conversations and thought opportunities. We had 1) a camera vision system that replicated a manufacturing vision detection model via detecting direction of motion and image characteristics of the people walking by the booth, 2) an LLM business card reader that extracted all the text off a business card and paired it with LLM knowledge of the business/individual, and 3) an LHP unique take on Retrieval-Augmented Generated (RAG). One demo caught 95% of the attention and drill-down conversations - RAGs, after all, this was not a conference that talked about OT or IoT at all, and as mentioned above, AI was the thought leader.

Conference Attendee Feedback – What We Heard

We engaged everyone that we captured (kidding) at our booth to talk for at least 5 minutes to understand their pain points, what they were ultimately trying to address, and, in general, their opinions on the data and AI market and environment, and where LHP could help. Finally, here is what we heard, generally categorized, from the 150+ corporate individuals we talked to.

1. Modernization and Integration

The most common disillusionment or “just being honest” comments we heard went something along the lines of: ‘Everyone is touting AI or some magic product to solve your problems when I am still running on old systems and can barely get those systems or teams to talk to each other.’ Or ‘corporate is demanding we use or adopt AI, and they have no idea how the data enterprise is currently structured, but they said move.’ As we strived to help bridge the gap, considerable discussion engaged around putting in more modern approaches and technology, such as: rapid prototype development (to help show AI use cases), cloud and modern data and event architectures (yes, lots of companies are still trying to move workloads and workflows to the cloud), and general use cases and data transparency to all shareholders of the data. Lots of data still lives in silos and how to best connect disparate data sets and ultimately how to integrate the data and workflows that solved individual needs for their internal customer base or case studies that truly work.

2. Data Governance

Data solution investments, projects, and/or digital transformation almost always come back to, or have a component of, security and risk, a key data governance component. With AI being the newest all-consuming buzzword, security immediately followed as the next adjacent or downstream thought. In our collection of voices, data security was the number one reason why AI wasn’t yet deployed or, more so, in active planning. In addition, it seems to be a 50/50 split between voices of: A) some are still taking a wait and see approach before getting involved with AI without knowing who is going to win the AI arms race or B) the need to address security in their deployment of AI and how do they address it.

The above two concerns are where our RAG demo elicited a lot of discussion, and how we were able to get lots of direct general feedback. We tailored our RAG solution by A) using modern front end web-sites, connected to cloud event triggers, B) illustrated how the demo sent no company specific or internal data to the LLM (trying to address some of the data security fears), C) only sending the data schema (and it is always the first time the LLM has any knowledge of the schema as well), D) the option to use a cloud LLM (like OpenAI’s ChatGPT-o3) or an internally hosted LHP LLM, and E) the resulting solution is code based that can be rendered internally by an integrator or resident data engineer. This prompted follow-ups ranging from ‘can this work on our GraphDB?’ to ‘what do I need to do to bring in LLMs or AI to our processes?’

3. Realism and Smaller Steps

Finally, a common thread was noted where individuals and companies are trying to take small and realistic steps to enable GenAI inside their four walls or for their customers. A measured approach to AI is the path forward they were adopting, especially for the B2B corporations. The landscape is changing and improving so quickly that tailored, well-integrated solutions, with thought-out architectures and right-sized budgets for AI, seem to be the logical answer.

LHP believes AI and GenAI specifically will provide vast efficiencies in an organization’s four walls and while some may not yet see the hype or the financial use cases are not quite matured, we see the potential and limitations currently evidenced in the field and strive to collaboratively approach a technology implementation that leads to improved AI deployment.

With the Data Governance and Integration concerns along with needing to be real and “prove it”, LHP took the feedback and immediately enhanced our RAG and other technical integrations and went to work on our own internal hosted LLM, air-gapped from the internet. It is now successfully deployed and being used by our legal department, which is one of the most data privacy conservative departments in our company. Our next step is creating automated end-to-end testing for our engineering consulting division to attempt to improve their efficiencies by 50+%.

Conclusion

If you made it this far, thank you. Probably a lot of information that is not shocking to read as a technologist. However, we hope this confirms (maybe even challenges) or coalesces information from a lot of informal data we obtained from the event. When you go to the Gartner conference as an exhibitor, you obviously want to sell your product or, in our case, services, but with four ten-hour days, you try and engage in as many meaningful discussions as possible to help pass the time, and hopefully you receive some benefit from reading this.

Next Steps: Let’s Turn Conference Insights Into Actionable Results

The future of AI and data governance isn’t just in keynote slides, it’s in well-integrated, secure, and proven solutions that meet your teams where they are. At LHP, we don’t just talk about modernization, we deliver it through realistic, cross-system integrations and enterprise-grade AI enablement. Whether you’re struggling to align legacy systems with new mandates, secure your data for AI deployment, or simply want to take smarter, smaller steps forward, our team is ready to guide you. Let’s architect the right-sized path forward for your organization. Contact us today to start your modernization and AI journey with LHP.