TL;DR:
Fragmented AI instruments are draining budgets, slowing adoption, and irritating groups. To manage prices and speed up ROI, AI leaders want interoperable options that scale back device sprawl and streamline workflows.
AI funding is underneath 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, in keeping with our Unmet AI Needs report.
The wrongdoer? A disconnected AI ecosystem. When groups spend extra time wrestling with disconnected instruments than delivering outcomes, AI leaders threat ballooning prices, stalled ROI, and excessive expertise turnover.
AI practitioners spend extra time sustaining instruments than fixing enterprise issues. The largest blockers? Handbook pipelines, device fragmentation, and connectivity roadblocks.
Think about if cooking a single dish required utilizing a distinct range each single time. Now envision operating a restaurant underneath 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 types, whether or not code-first in notebooks, GUI-driven, or a hybrid strategy. It should additionally bridge gaps between groups, reminiscent of 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 reminiscent of JAR recordsdata, scoring code, APIs or embedded purposes. With out an infrastructure that streamlines these workflows, AI leaders threat 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.
- Software and infrastructure fragmentation. Disconnected environments create bottlenecks and inference latency, forcing groups into countless troubleshooting as an alternative of scaling AI.
- Orchestration complexities. Handbook provisioning of compute assets — configuring servers, DevOps settings, and adjusting as utilization scales — isn’t 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 power silos make integrating new applied sciences gradual, complicated, and unreliable.
The long-term value? Heavy infrastructure administration overhead that eats into ROI.
Extra finances goes towards the overhead prices of handbook patchwork options as an alternative of delivering outcomes.
Over time, these course of breakdowns lock organizations into outdated infrastructure, frustrate AI groups, and stall enterprise affect.
Code-first builders want customization, however expertise 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 usefulness, resulting in misalignments that gradual AI growth and complicate workflows. However these inefficiencies don’t cease with builders. AI integration points have a wider affect on the enterprise.
The true value of integration bottlenecks
Disjointed AI instruments and programs don’t simply affect budgets; they create ripple results that affect group stability and operations.
- The human value. With a mean tenure of simply 11 months, knowledge scientists usually depart 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 at all times 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 person 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 will 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 present expertise 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 accessible compute assets, will increase operational pressure and makes AI even more durable to scale.
4 steps to simplify AI infrastructure and reduce prices
Streamlining AI operations doesn’t need to be overwhelming. Listed here are actionable steps AI leaders can take to optimize operations and empower their groups:
Step 1: Assess device flexibility and adaptableness
Agentic AI requires modular, interoperable instruments that assist 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 reminiscent of 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 want 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 accessible 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 will determine workflow obstacles earlier than they turn out to be implementation roadblocks. Utilizing unified instruments and shared programs 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 threat falling behind.
Inflexible, siloed programs not solely slows innovation and delays ROI, it additionally prevents organizations from responding to fast-moving developments in AI and enterprise expertise.
With 77% of organizations already experimenting with generative and predictive AI, unresolved integration challenges will solely turn out to be extra pricey over time.
Leaders who tackle device sprawl and infrastructure inefficiencies now will decrease operational prices, optimize assets, and see stronger long-term AI returns
Get the complete DataRobot Unmet AI Needs report to find out how prime AI groups are overcoming implementation hurdles and optimizing their AI investments.
In regards to the creator
Could Masoud is a knowledge scientist, AI advocate, and thought chief skilled in classical Statistics and fashionable Machine Studying. At DataRobot she designs market technique for the DataRobot AI Governance product, serving to international organizations derive measurable return on AI investments whereas sustaining enterprise governance and ethics.
Could developed her technical basis by way of levels in Statistics and Economics, adopted by a Grasp of Enterprise Analytics from the Schulich Faculty of Enterprise. This cocktail of technical and enterprise experience has formed Could as an AI practitioner and a thought chief. Could delivers Moral AI and Democratizing AI keynotes and workshops for enterprise and tutorial communities.
Kateryna Bozhenko is a Product Supervisor for AI Manufacturing at DataRobot, with a broad expertise in constructing AI options. With levels in Worldwide Enterprise and Healthcare Administration, she is passionated in serving to customers to make AI fashions work successfully to maximise ROI and expertise true magic of innovation.
