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 below 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 issue implementing AI instruments, and almost 30% cite integration and workflow inefficiencies as their high frustration, in line with 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 largest blockers? Guide pipelines, device fragmentation, and connectivity roadblocks.
Think about if cooking a single dish required utilizing a unique range each single time. Now envision working a restaurant below these situations. Scaling can be inconceivable.
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 method. It should additionally bridge gaps between groups, comparable to knowledge science and DevOps, the place every group depends on completely different toolsets. When these workflows stay siloed, collaboration slows, and deployment bottlenecks emerge.
Scalable AI additionally calls for deployment flexibility comparable to JAR recordsdata, scoring code, APIs or embedded functions. 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 sources
Interoperability hurdles don’t simply decelerate groups – they create important price implications.
The highest workflow restrictions AI practitioners face:
- Guide pipelines. Tedious setup and upkeep pull AI, engineering, DevOps, and IT groups away from innovation and new AI deployments.
- Instrument and infrastructure fragmentation. Disconnected environments create bottlenecks and inference latency, forcing groups into countless troubleshooting as an alternative of scaling AI.
- Orchestration complexities. Guide provisioning of compute sources — configuring servers, DevOps settings, and adjusting as utilization scales — will not be solely time-consuming however almost inconceivable to optimize manually. This results in efficiency limitations, wasted effort, and underutilized compute, in the end stopping AI from scaling successfully.
- Tough updates. Fragile pipelines and gear silos make integrating new applied sciences sluggish, complicated, and unreliable.
The long-term price? Heavy infrastructure administration overhead that eats into ROI.
Extra funds 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 influence.
Code-first builders choose 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 simple to make use of.
This disconnect forces groups to decide on between flexibility and usefulness, 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 price of integration bottlenecks
Disjointed AI instruments and techniques don’t simply influence budgets; they create ripple results that influence workforce stability and operations.
- The human price. With a median tenure of simply 11 months, knowledge scientists usually go away earlier than organizations can totally 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 price 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 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’ll simply work together with it. Once 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 growing demand for generative and agentic AI, practitioners face mounting compute, scalability, and operational challenges. Many organizations are layering new generative AI instruments on high of their current 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 obtainable compute sources, 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 below 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 essential inquiries to ask are:
- Can AI groups simply join, handle, and interchange instruments comparable to LLMs, vector databases, or orchestration and safety layers with out downtime or main reengineering?
- Do our AI instruments scale throughout varied environments (on-prem, cloud, hybrid), or are they locked into particular distributors and inflexible infrastructure?
Step 2: Leverage a hybrid interface
53% of practitioners choose a hybrid AI interface that blends the flexibleness of coding with the accessibility of GUI-based instruments. As one knowledge science lead defined, “GUI is essential 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 method additionally optimizes AI workflow orchestration by leveraging one of the best obtainable compute sources, minimizing infrastructure overhead whereas guaranteeing 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 obstacles earlier than they grow to be implementation roadblocks. Utilizing unified instruments and shared techniques 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 techniques 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 grow to be extra expensive over time.
Leaders who tackle device sprawl and infrastructure inefficiencies now will decrease operational prices, optimize sources, and see stronger long-term AI returns
Get the complete DataRobot Unmet AI Wants report to find out how high AI groups are overcoming implementation hurdles and optimizing their AI investments.
In regards to the writer
Might Masoud is a knowledge scientist, AI advocate, and thought chief educated 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.
Might 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 Might as an AI practitioner and a thought chief. Might 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.
