AI in Companies: Why Platforms Will Win and Fragmented Wallets Will Fail

The challenge of data integration in AI adoption

Companies are rapidly pushing artificial intelligence (AI) towards production, but they are facing a significant obstacle: legacy data infrastructure. AI amplifies the cracks of a fragmented IT strategy, revealing that a portfolio of point solutions assembled together simply cannot withstand the scale. To achieve a return on investment (ROI), AI requires a unified architecture designed to handle massive, complex, and unstructured data.

Elastic's partners are a concrete example of how a well-structured architecture can lead to success. By building on a unified platform instead of a fragmented portfolio, they can bypass the friction derived from integrating disparate tools. This allows them to scale faster and optimize delivery, positioning themselves to capture a larger share of their clients' AI investments.

The cost of waiting

The promise of AI is real, but unlocking its full business value takes time. This follows the J-curve of technology adoption: an adaptation phase where companies invest, rebuild, and retrain before productivity increases. Companies that move now with the right architecture are better equipped to bridge this gap.

According to IDC projections, by 2029 global spending on AI infrastructure should exceed 1 trillion dollars. However, nearly half of enterprise AI initiatives may not achieve ROI targets by 2026 due to inefficient databases and infrastructure. This is one of the most common roadblocks we observe. When AI is implemented through fragmented tools, partners spend time stitching together solutions instead of delivering differentiated value.

For partners implementing AI, this means unifying data models, reorganizing processes, and managing integrations that were not designed to communicate with each other. The added complexity lengthens the J-curve, delaying ROI and limiting the ability to grow with clients.

The advantages of the unified platform

Let's compare this situation with Elastic's platform approach. Our search, observability, and security offerings work on a single data layer, allowing data to move freely and providing AI with better context. The advantage of Elastic also comes from being a single data store, which means unified indexing, a single query layer, and a single place where data actually resides together. This unified backend changes the cost model and expands capabilities. This is why Elastic has recently been able to reduce the price of metrics and eliminate high cardinality penalties that competitors are subject to, providing partners with a base on which to build real services and IP and shorten the time to value.

Elastic is also open by design, offering partners the architectural flexibility to adapt the solution to the problem. They can choose where to deploy and which AI or large language model (LLM) to use based on performance, risk, and cost, rather than imposing a single rigid architecture on every customer.

Customers are already experiencing these benefits in production. For example, PepsiCo was using 55 disparate monitoring tools before consolidating on Elastic Observability. By unifying that architecture, they reduced hardware costs and halved the mean time to resolution (MTTR). Colsubsidio saw a similar trajectory working with the Elastic partner Insoftar. By implementing Elastic Observability, they unified logs, metrics, and traces in over 40 business processes that they previously could not track. This reduced critical incidents by 95% and MTTR by 30%.

A unified and open platform allows partners to spend less time rebuilding infrastructure and more time delivering business value.

The opportunity for partners

AI is driving consolidation across the industry. Customers have stopped buying based on features. They are examining the architecture to determine what is built for the future and what is simply assembled together.

This shift presents a huge opportunity. Historically, purchasing a fragmented portfolio meant that partners billed for the manual work of assembling disparate databases. This can be a low-margin activity that limits their growth and burns client goodwill when ROI slows.

Elastic changes this dynamic. Because our platform provides a unified base for AI, partners don't have to reorganize systems to deploy them. The return is twofold. First, the infrastructure is scalable, which can help strengthen the partner's footing within their own account, creating additional revenue paths. Second, partner commitments can extend from integration work to consulting services on redefining the business processes that successful AI requires.

Fragmented portfolios force partners to do the heavy lifting of integration, while platforms allow them to focus on delivering value. Partners who make this transition will be the ones driving their clients' AI transformation.

Implications for security and observability

In the context of unified architectures, observability and security become critical elements. The adoption of platforms like Elastic Observability allows for the centralization of log, metric, and trace management, significantly reducing the time to detect and resolve incidents. This is particularly relevant in an ever-evolving threat landscape, where the ability to respond quickly to security events is fundamental.

Another relevant aspect is the reduction of operational costs. The consolidation of disparate tools into a unified platform not only simplifies management but also reduces licensing and maintenance costs. This allows companies to allocate resources more efficiently, investing in strategic areas such as the development of new features or market expansion.

Future considerations

As the adoption of AI continues to grow, data infrastructures will need to evolve to support increasingly complex workloads. Unified platforms are well-positioned to address this challenge, thanks to their ability to scale and adapt to new requirements. Partners who invest in these solutions today will be better prepared to leverage emerging opportunities in the next decade.

A strategic approach to AI adoption

The transition to unified platforms represents an opportunity for companies to reorient their technology strategies toward a more sustainable and scalable model. Partners who embrace this change will not only improve their competitive position but also contribute to defining the future of AI adoption in enterprises. In a context of rapid technological evolution, the ability to adapt and innovate will be the key to success.