Why AI leaders can’t afford fragmented AI instruments

TL;DR:

Fragmented AI instruments are draining  budgets, slowing adoption, and irritating groups. To regulate prices and speed up ROI, AI leaders want interoperable options that scale back instrument sprawl and streamline workflows.

AI funding is beneath a microscope in 2025. Leaders aren’t simply requested to show AI’s worth — they’re being requested why, after important investments, their groups nonetheless battle to ship outcomes.

1-in-4 groups report problem implementing AI instruments, and almost 30% cite integration and workflow inefficiencies as their prime frustration, based on our Unmet AI Wants report.

The offender? A disconnected AI ecosystem. When groups spend extra time wrestling with disconnected instruments than delivering outcomes, AI leaders danger ballooning prices, stalled ROI, and excessive expertise turnover. 

AI practitioners spend extra time sustaining instruments than fixing enterprise issues. The most important blockers? Handbook pipelines, instrument fragmentation, and connectivity roadblocks.

Think about if cooking a single dish required utilizing a special range each single time. Now envision operating a restaurant beneath these circumstances. Scaling could be not possible. 

Equally, AI practitioners are slowed down by the time-consuming, brittle pipelines, leaving much less time to advance and ship AI options.

AI integration should accommodate numerous working kinds, whether or not code-first in notebooks, GUI-driven, or a hybrid strategy. It should additionally bridge gaps between groups, equivalent to knowledge science and DevOps, the place every group depends on totally different toolsets. When these workflows stay siloed, collaboration slows, and deployment bottlenecks emerge.

Scalable AI additionally calls for deployment flexibility equivalent to JAR information, scoring code, APIs or embedded purposes. With out an infrastructure that streamlines these workflows, AI leaders danger stalled innovation, rising inefficiencies, and unrealized AI potential. 

How integration gaps drain AI budgets and assets 

Interoperability hurdles don’t simply decelerate groups – they create important value implications.

The highest workflow restrictions AI practitioners face:

• Handbook pipelines. Tedious setup and upkeep pull AI, engineering, DevOps, and IT groups away from innovation and new AI deployments.

• Device and infrastructure fragmentation. Disconnected environments create bottlenecks and inference latency, forcing groups into infinite troubleshooting as a substitute of scaling AI.
• Orchestration complexities.  Handbook provisioning of compute assets — configuring servers, DevOps settings, and adjusting as utilization scales — will not be solely time-consuming however almost not possible to optimize manually. This results in efficiency limitations, wasted effort, and underutilized compute, finally stopping AI from scaling successfully.
• Tough updates. Fragile pipelines and gear silos make integrating new applied sciences sluggish, complicated, and unreliable. 

The long-term value? Heavy infrastructure administration overhead that eats into ROI. 

Extra price range goes towards the overhead prices of handbook patchwork options as a substitute of delivering outcomes.

Over time, these course of breakdowns lock organizations into outdated infrastructure, frustrate AI groups, and stall enterprise influence.

Code-first builders desire customization, however know-how misalignment makes it more durable to work effectively.

• 42% of builders say customization improves AI workflows.
• Only one-in-3 say their AI instruments are straightforward to make use of.

This disconnect forces groups to decide on between flexibility and value, resulting in misalignments that sluggish AI improvement and complicate workflows. However these inefficiencies don’t cease with builders. AI integration points have a wider influence on the enterprise.

The true value of integration bottlenecks

Disjointed AI instruments and methods don’t simply influence budgets; they create ripple results that influence crew stability and operations. 

• The human value. With a median tenure of simply 11 months, knowledge scientists usually go away earlier than organizations can absolutely profit from their experience. Irritating workflows and disconnected instruments contribute to excessive turnover.
• Misplaced collaboration alternatives. Solely 26% of AI practitioners really feel assured counting on their very own experience, making cross-functional collaboration important for knowledge-sharing and retention.

Siloed infrastructure slows AI adoption. Leaders usually flip to hyperscalers for value financial savings, however these options don’t all the time combine simply with instruments, including backend friction for AI groups. 

Generative AI and agentic are including extra complexity

With 90% of respondents anticipating generative AI and predictive AI to converge, AI groups should steadiness consumer wants with technical feasibility.

As King’s Hawaiian CDAO Ray Fager explains:
“Utilizing generative AI in tandem with predictive AI has actually helped us construct belief. Enterprise customers ‘get’ generative AI since they’ll simply work together with it. After they have a GenAI app that helps them work together with predictive AI, it’s a lot simpler to construct a shared understanding.”

With an rising demand for generative and agentic AI, practitioners face mounting compute, scalability, and operational challenges. Many organizations are layering new generative AI instruments on prime of their current know-how stack with out a clear integration and orchestration technique. 

The addition of generative and agentic AI, with out the inspiration to effectively allocate these complicated workloads throughout all out there compute assets, will increase operational pressure and makes AI even more durable to scale.

4 steps to simplify AI infrastructure and minimize prices  

Streamlining AI operations doesn’t need to be overwhelming. Listed below are actionable steps AI leaders can take to optimize operations and empower their groups:

Step 1: Assess instrument flexibility and adaptableness

Agentic AI requires modular, interoperable instruments that help frictionless upgrades and integrations. As necessities evolve, AI workflows ought to stay versatile, not constrained by vendor lock-in or inflexible instruments and architectures.

Two necessary inquiries to ask are:

• Can AI groups simply join, handle, and interchange instruments equivalent to LLMs, vector databases, or orchestration and safety layers with out downtime or main reengineering?
• Do our AI instruments scale throughout numerous environments (on-prem, cloud, hybrid), or are they locked into particular distributors and inflexible infrastructure?

Step 2: Leverage a hybrid interface

53% of practitioners desire a hybrid AI interface that blends the pliability of coding with the accessibility of GUI-based instruments. As one knowledge science lead defined, “GUI is important for explainability, particularly for constructing belief between technical and non-technical stakeholders.” 

Step 3: Streamline workflows with AI platforms

Consolidating instruments into a unified platform reduces handbook pipeline stitching, eliminates blockers, and improves scalability. A platform strategy additionally optimizes AI workflow orchestration by leveraging the perfect out there compute assets, minimizing infrastructure overhead whereas making certain low-latency, high-performance AI options.

Step 4: Foster cross-functional collaboration

When IT, knowledge science, and enterprise groups align early, they’ll determine workflow boundaries earlier than they change into implementation roadblocks. Utilizing unified instruments and shared methods reduces redundancy, automates processes, and accelerates AI adoption. 

Set the stage for future AI innovation

The Unmet AI Wants survey makes one factor clear: AI leaders should prioritize adaptable, interoperable instruments — or danger falling behind. 

Inflexible, siloed methods not solely slows innovation and delays ROI, it additionally prevents organizations from responding to fast-moving developments in AI and enterprise know-how. 

With 77% of organizations already experimenting with generative and predictive AI, unresolved integration challenges will solely change into extra expensive over time. 

Leaders who deal with instrument sprawl and infrastructure inefficiencies now will decrease operational prices, optimize assets, and see stronger long-term AI returns

Get the total DataRobot Unmet AI Wants report to find out how prime AI groups are overcoming implementation hurdles and optimizing their AI investments.