Andrés Marenco
AI is everywhere—and for enterprises, the hardest part is knowing where to start. Which technologies matter? How do you separate hype from real value? And what’s the smartest place to focus first?
In this blog you’ll learn:
- Where enterprises often struggle with AI—and how to move past it.
- Why search is the natural starting point for practical innovation.
- How Discovery helps turn early ideas into enterprise-scale solutions.
By the end, you’ll see how to cut through the buzzwords and make AI a tool that delivers measurable impact for your business and your customers.
The Hype and the Reality of AI Projects
AI is exciting. It promises new opportunities, simpler workflows, and lower costs.
Exciting, but overwhelming. Is it RAG or MCP? Which LLM? Do I need an agent or an agentic AI solution? LangChain or LlamaIndex? Is it something I can do on my own, or should I call the experts?
And now we also have “Discovery”? Where does Discovery fit in this ocean of buzzwords?
Ok… let’s step back for a second. Before talking about technologies, let’s set the context with a simple question:
Did you know that 95% of AI projects at enterprise level fail to deliver measurable impact?
No surprise in there: we are living an “exploration” phase where we just want to know if there is some value in the hype. Prototyping fast is the way to go… but struggling to promote a promising idea to production is highlighting how big is the gap between “it works on my computer” and “it works at enterprise level”.
Unsuccessful stories vary from being unable to process the volume of data to the being unable to handle the number of active users. Could be costs. Or performance. Maybe security. Or the lack of flexibility for new use cases.
Maybe the reason is not technical. Unrealistic expectations about the capabilities of these technologies and their misalignment with business needs is a recipe for failure. Maybe that prototype that blew our minds during a demo was never meant to be integrated into an enterprise workflow. Maybe that prototype is just an interesting exploration of trending technologies. Useful at the “works on my computer” level, but with no concrete value in my line of business.
And while we don’t want to fall into that infamous 95%, avoiding the AI revolution so we don’t fail is no longer an option. The pressure to innovate is real.
That’s the trap: treating AI as the destination instead of what it really is – a tool.
What enterprises need is a clear strategy: one that avoids chasing the “trend of the month” and instead aligns technology with business goals. Because staying ahead isn’t about adopting the newest innovation. It’s about choosing the right one—the one that delivers measurable impact.
And for many businesses, that begins with “search”.
When Better Business Demands Better Search
Search sits at the center of many digital experiences. Whether it’s an online store or a publishing platform, success depends on how quickly and accurately users can find what they need.
In e-commerce, that means guiding a shopper to the exact product they want to buy. In publishing, it means leading subscribers to the most relevant articles. Different core business, different target audiences – yet they share the same fundamental truth: when people can find what they’re looking for, they’re more satisfied, more loyal, and more likely to return.
Do it right, and you get retention.
Do it better, and you grow the business.
Choosing the right search technologies shortens the path between need and answer—and that’s what real innovation looks like.
So, what does “better search” look like in practice?
Practical Paths to Better Search
The goal is clear: better search.
The path to that goal is not as evident.
Keyword search is a given. Any search engine implementation with proper tuning can fulfill the task.
What’s next? Well, we don’t have to go with the latest trend. There is money in “boring”. We just need the right features. The ones that make my core business better, without completely disrupting day-to-day operations. We can start small evolve from there.
So, how about generative answers? A common feature to answer questions over large collections. Configurable enough to answer using profile-based contexts and with tags descriptive enough to maintain the user confidence:
Or maybe a simple semantic search that can easily close the semantic gap between what the user typed and what the user was truly looking for:
Too simple? Maybe an AI-powered chatbot is what we are looking for. Smart enough to keep an appropriate conversation about a collection of products, items, or publications. Capable of remembering previous conversations in an effort to better understand the user needs:
Too up-front? How about search analytics? You can’t improve what you can’t measure, and measuring the correct data points, together with an appropriate analysis leads to a better search experience and more happy users.
Notice how some of those use cases are conceptually easy to implement, and yet, potentially dangerous if the correct guardrails are not there with access control and protection from hallucinations.
Features that, at enterprise level, need to seamlessly integrate with current workflows.
That’s where Discovery comes in—built to deliver these features safely, at scale, and in line with enterprise needs.
Discovery: The right tool for the right problems
Discovery is here to help search-centric business cutting through the noise and focusing on features with measurable impact.
An end-to-end solution builder for search applications, at enterprise level. A technology agnostic platform, adaptable to any ecosystem. No matter the cloud provider or the technology stack, the integration is smooth.
Although you could say the same about other tools.
So, what makes Discovery the right tool for a better search experience? Well, the devil is in the details…
- How do I handle my data volumes while keeping the flexibility to support my business rules at a reasonable cost?
If we are talking about e-commerce and publishers, it is easy to assume we are talking about large numbers of documents and users.
Text, images, PDFs, and other media types can be in the mix, and they might need to follow different business rules based on multiple conditionals.
Discovery Ingestion is a flexible, auto-scalable, distributed ETL processing framework, designed for high volumes of data processing where data sources, transformers and hydrators are be combined in pipelines that describe use cases such as search, RAG, generative answers, search analytics, and many others.
Descriptive enough to keep track of where each record is, and with enough troubleshooting tools to correct problems along the way.
- How about a staging layer? Or maybe a “memory” for an AI assistant?
Discovery Staging is just what you need: a simple storage to add, update, retrieve and delete information of any type. Already connected to other Discovery products and accessible through HTTP.
- Generative answers and an AI Assistant are very different things, right? Can Discovery provide this type of user experience?
Well, there is where Discovery QueryFlow shines. Its design based on building blocks that can be arranged without restriction, and rearranged without down times, result in a powerful solution that can enable any of the proposed enhancements while integrating into existing systems.
With tracing tools that help analyze and tune each request. With flexibility for complex A/B testing setups that allow a real-time evaluation of each new prompt, each new guardrail, each update on a business rule and each adjustment on the security filtering.
With consistent error handling to process and report problems.
- Can all of this be achieved with tools other than Discovery?
Yes. Definitely. Either with a single tool or a combination of them. And after getting your hands dirty in developer’s territory. And after implementing a monitoring mechanism. And after defining the workflows for updates and maintainability. And after individually tackling each one of the challenges you’ll face along the way… you’ll get there.
Or don’t reinvent the wheel and let Discovery deal with the heavy lifting in the path of adopting new technologies for a better search experience.
Integrating Discovery into the AI Landscape
At its core, success in AI isn’t about chasing buzzwords. It’s about making technology work in service of the business. AI isn’t the destination. It’s the tool that gets you there.
Discovery was built on that principle. It gives search enterprises the flexibility to choose the right technologies, the reliability to trust them at scale, and the integrations to make them part of existing workflows.
Most AI projects stall before they deliver results—but yours doesn’t have to. With the right approach, you can move from prototypes that impress in demos to enterprise solutions that scale with confidence.
Discovery gives you the tools to bridge that gap: aligning AI with business needs, enhancing search where it matters most, and supporting growth from first experiments to full production.
The path forward is clear: treat AI as a tool, not the destination—and let Discovery help you turn that strategy into measurable impact.
About the Author
Andrés Marenco is the Product Architect for Pureinsights Discovery. This post marks the beginning of a blog series where Andrés will share his perspective on the Discovery platform, its evolution, and what lies ahead for its future development.
