TL;DR:
Fragmented AI tools are draining budgets, slowing adoption, and frustrating teams. To control costs and accelerate ROI, AI leaders need interoperable solutions that reduce tool sprawl and streamline workflows.
AI investment is under a microscope in 2025. Leaders aren’t just asked to prove AI’s value — they’re being asked why, after significant investments, their teams still struggle to deliver results.
1-in-4 teams report difficulty implementing AI tools, and nearly 30% cite integration and workflow inefficiencies as their top frustration, according to our Unmet AI Needs report.
The culprit? A disconnected AI ecosystem. When teams spend more time wrestling with disconnected tools than delivering outcomes, AI leaders risk ballooning costs, stalled ROI, and high talent turnover.
AI practitioners spend more time maintaining tools than solving business problems. The biggest blockers? Manual pipelines, tool fragmentation, and connectivity roadblocks.
Imagine if cooking a single dish required using a different stove every single time. Now envision running a restaurant under those conditions. Scaling would be impossible.
Similarly, AI practitioners are bogged down by the time-consuming, brittle pipelines, leaving less time to advance and deliver AI solutions.
AI integration must accommodate diverse working styles, whether code-first in notebooks, GUI-driven, or a hybrid approach. It must also bridge gaps between teams, such as data science and DevOps, where each group relies on different toolsets. When these workflows remain siloed, collaboration slows, and deployment bottlenecks emerge.
Scalable AI also demands deployment flexibility such as JAR files, scoring code, APIs or embedded applications. Without an infrastructure that streamlines these workflows, AI leaders risk stalled innovation, rising inefficiencies, and unrealized AI potential.
Interoperability hurdles don’t just slow down teams – they create significant cost implications.
The top workflow restrictions AI practitioners face:
The long-term cost? Heavy infrastructure management overhead that eats into ROI.
More budget goes toward the overhead costs of manual patchwork solutions instead of delivering results.
Over time, these process breakdowns lock organizations into outdated infrastructure, frustrate AI teams, and stall business impact.
Code-first developers prefer customization, but technology misalignment makes it harder to work efficiently.
This disconnect forces teams to choose between flexibility and usability, leading to misalignments that slow AI development and complicate workflows. But these inefficiencies don’t stop with developers. AI integration issues have a much broader impact on the business.
Disjointed AI tools and systems don’t just impact budgets; they create ripple effects that impact team stability and operations.
Siloed infrastructure slows AI adoption. Leaders often turn to hyperscalers for cost savings, but these solutions don’t always integrate easily with tools, adding backend friction for AI teams.
With 90% of respondents expecting generative AI and predictive AI to converge, AI teams must balance user needs with technical feasibility.
As King’s Hawaiian CDAO Ray Fager explains:
“Using generative AI in tandem with predictive AI has really helped us build trust. Business users ‘get’ generative AI since they can easily interact with it. When they have a GenAI app that helps them interact with predictive AI, it’s much easier to build a shared understanding.”
With an increasing demand for generative and agentic AI, practitioners face mounting compute, scalability, and operational challenges. Many organizations are layering new generative AI tools on top of their existing technology stack without a clear integration and orchestration strategy.
The addition of generative and agentic AI, without the foundation to efficiently allocate these complex workloads across all available compute resources, increases operational strain and makes AI even harder to scale.
Streamlining AI operations doesn’t have to be overwhelming. Here are actionable steps AI leaders can take to optimize operations and empower their teams:
Agentic AI requires modular, interoperable tools that support frictionless upgrades and integrations. As requirements evolve, AI workflows should remain flexible, not constrained by vendor lock-in or rigid tools and architectures.
Two important questions to ask are:
53% of practitioners prefer a hybrid AI interface that blends the flexibility of coding with the accessibility of GUI-based tools. As one data science lead explained, “GUI is critical for explainability, especially for building trust between technical and non-technical stakeholders.”
Consolidating tools into a unified platform reduces manual pipeline stitching, eliminates blockers, and improves scalability. A platform approach also optimizes AI workflow orchestration by leveraging the best available compute resources, minimizing infrastructure overhead while ensuring low-latency, high-performance AI solutions.
When IT, data science, and business teams align early, they can identify workflow barriers before they become implementation roadblocks. Using unified tools and shared systems reduces redundancy, automates processes, and accelerates AI adoption.
The Unmet AI Needs survey makes one thing clear: AI leaders must prioritize adaptable, interoperable tools — or risk falling behind.
Rigid, siloed systems not only slows innovation and delays ROI, it also prevents organizations from responding to fast-moving advancements in AI and enterprise technology.
With 77% of organizations already experimenting with generative and predictive AI, unresolved integration challenges will only become more costly over time.
Leaders who address tool sprawl and infrastructure inefficiencies now will lower operational costs, optimize resources, and see stronger long-term AI returns
Get the full DataRobot Unmet AI Needs report to learn how top AI teams are overcoming implementation hurdles and optimizing their AI investments.
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I did a podcast this month on how to use technology to increase innovation. Now, I’m not a fan of innovation for its own sake. If you have something that works, innovation can be a bad thing because it may break […]
The post Using AI To Fix The Innovation Problem: The Three Step Solution appeared first on TechSpective.
While many competitors are nearly as good, ChatGPT-4 currently has no equal when it comes to creative writing, handling long-form queries and prompts and engaging in in-depth conversations, according to the Times.
The source of the Economic Times’ report is a popular AI rating service, LMArena.
Volunteers visiting LMArena evaluate AI by testing two unidentified and randomly selected chatbots — and then rating which chatbot responds to their prompt best.
In other news and analysis on AI writing:
*Facebook’s Parent Company Promising ChatGPT Competitor: Facebook inventor Mark Zuckerberg is currently developing a direct competitor to ChatGPT, according to Euro News.
Zuckerberg already has AI software – dubbed Llama – that competes on par with the AI software undergirding ChatGPT.
But so far, Zuckerberg’s AI has only been integrated into various platforms owned by Facebook parent company Meta – and never unveiled as a stand-alone, ChatGPT competitor.
*AI Now Great at Conducting Interviews, Too: Veteran journalist Alex Kantrowitz has discovered a disturbing truth.
Not only can AI write incredibly well: It can also conduct interviews like a news reporter.
Case in point: Kantrowitz says a fellow journalist – Evan Ratliff – recently used voice-powered AI to conduct an interview with a tech CEO.
The result, according to Kantrowitz: “When Ratliff listened to the recording, he was surprised to find the CEO really opened up.
“He was a little more forthcoming with the AI than he was with me,” Ratliff told me.
“There’s a quality of, you don’t necessarily feel like there’s someone there and you might be a little more intimate than you would have otherwise. And that can be very valuable in an interview for a reporting project.”
*ChatGPT-Maker Mulling $20,000/Month Charge for Advanced AI Agents: AI’s Next Big Thing – AI agents that can work autonomously and do things like operate on the Web on your behalf – may be coming with a hefty price tag.
ChatGPT-Maker OpenAI is reportedly weighing a $20,000/month charge for a PhD-level agent designed to do highly advanced research for you.
Meanwhile, AI software developer agents might go for $10,000/month and a knowledge worker agent is being floated at $2,000/month.
Wow — from ‘AI collaborator’ to ‘AI employee.’
That was fast.
*Microsoft Copilot Offers More Freebies: Users of ChatGPT competitor MS Copilot now have two more reasons to stick with the chatbot: Free access to ThinkDeeper and voice.
Like many new deep research tools cropping-up in the market, Copilot ThinkDeeper does a more extensive search and analysis to question as compared to the standard response from Copilot.
Meanwhile, Copilot Voice enables you to operate Copilot with your voice – rather than by using a keyboard.
*Google AI Overviews Gets an Upgrade: Writers who rely on Google AI Overviews for some research should expect better performance.
Specifically, AI Overviews – which study a number of links associated with a search and auto-generate a written summary – are now able to handle tougher questions, according to Robby Stein, VP of product, Google Search.
Plus, AI Overviews is also getting a new, experimental ‘AI Mode.’
Observes Robby Stein, VP of product, Google Search: “This new Search mode expands what AI Overviews can do with more advanced reasoning, thinking and multimodal capabilities so you can get help with even your toughest questions.
“You can ask anything on your mind and get a helpful AI-powered response with the ability to go further with follow-up questions and helpful Web links.”
*Duke University Joins Study on How to Better Embed AI in Education and Government: Duke University – along with 15 other universities – has joined OpenAI’s ‘NextGenAI Consortium’ to analyze how to better integrate AI into education and government.
Observes Brad Lightcap, OpenAI chief operating officer: “A close collaboration with universities is essential to our mission of building AI that benefits everyone.
“NextGenAI will accelerate research progress and catalyze a new generation of institutions equipped to harness the transformative power of AI.”
*ChatGPT Rival Anthropic Snags $3.5 Billion in New Funding: Anthropic – makers of the ChatGPT rival Claude chatbot – has just snagged $3.5 billion in new funding.
Anthropic was founded by former researchers from OpenAI, whose mission is to develop AI with firmer safety guardrails.
Competing in the same space when it comes to AI writing are Google Gemini, X’s Grok 3 – and hundreds of custom-tailored AI writing solutions specially designed for marketing, education, technical writing, law, health and other genres.
*New AI Email Marketing Tool Released: A new AI-powered email marketing platform – Stripo – is promising enhanced automation for email marketers.
Stripo’s AI Assistant – according to Oleksandra Khlystova, PR team lead, Stripo — enhances the email creation process by:
~Generating emails instantly – AI-powered automation reduces time spent on production
~Optimizing email design and structure – AI ensures well-structured layouts while allowing users to fine-tune branding
~Improving content clarity – AI-generated emails maintain strong readability, minimizing the need for manual editing
*AI BIG PICTURE: New Study Finds AI-Powered Writing a Big Hit Among Many White Collar Pros: Stanford University researchers have found that AI writing is being heavily embraced by many white collar workers.
Observes writer Matthias Bastian: “The impact is particularly noticeable in press releases, where up to 24% of content now comes from generative AI systems, or shows significant AI modification.
“The researchers suspect that actual AI adoption rates are higher than their analysis suggests.
“It likely missed heavily human-edited content and text from advanced AI models that closely mimic human writing.
“The study also didn’t examine other potential AI writing use cases, such as social media content creation.”
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–Joe Dysart is editor of RobotWritersAI.com and a tech journalist with 20+ years experience. His work has appeared in 150+ publications, including The New York Times and the Financial Times of London.
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Claire chatted to Vali Lalioti from the University of the Arts London about how art, culture and robotics interact.
Vali Lalioti is a pioneering designer, computer scientist and innovator. She has a PhD in Computer Science, an MRes in Design and an MBA, and extensive international leadership, research and innovation experience in Silicon Valley, Africa, China, Japan and Europe. Vali is passionate about how technology interacts with society and talks globally on women in tech, art and technology education and her research in societal applications for well-being, healthy ageing and art. She developed the first ever BBC Augmented Reality production in 2003 and has introduced the UK’s first Creative Robotics University Degrees.