Data silos are the silent roadblocks to successful AI adoption, scattering organisational knowledge and limiting insights. This article explores why breaking down silos is crucial for unlocking AI’s potential and how the right tools can make it happen.
Breaking Down Data Silos
Since data forms the foundation of AI adoption, organisations with siloed data often struggle to realise the full potential of their AI initiatives.
These silos—independent, often inaccessible data repositories maintained by individual departments— causes organisational knowledge to become scattered. Every forward-thinking organisation wants to jump on the AI bandwagon, but if their data is stuck in silos, AI adoption is like putting the cart before the horse. Without first addressing silos, organisations set themselves up for disappointment.
What Is a “Silo,” Anyway?
Phil S. Ensor a consultant in organisational development and employee relations in the 1980s, coined the term Functional Silo Syndrome, inspired by large, cylindrical structures used to store bulk grains. Back then, the term referred to the way departments operated in isolation, leading to redundant efforts, poor communication, and an inability to see the bigger picture.
The problem of silos predates the IT era, but the explosion of data in recent decades has given it new dimensions. As organisations began adopting specialised software tailored to individual departments, legacy systems became entrenched, and integration across platforms turned into a complex, resource-intensive challenge.
More than an inconvenience; data silos create ripple effects that impact the entire organisation. The impact can be seen in the following ways:
Inconsistent data
Siloed data often leads to conflicting or outdated information across teams. For example, separate departments may hold different customer records for the same person, leading to little “islands” of analysis that often disagree with each other.
Research also points out that siloed data “diminishes visibility, leading to data inconsistency and poor quality”, costing companies on average ≈$12.9 million per year in bad data alone. By contrast, breaking silos to create a single source of truth is shown to markedly improve data accuracy and trust.
Increased costs
Maintaining isolated silos drives up IT and operational expenses. Gartner’s data shows that businesses can lose on average, ~$15M per year in costs due to poor quality data (like siloed or incorrect data).
Silo maintenance also means redundant infrastructure which directly inflate budgets. Since each silo needs separate maintenance, management, and hardware resources, maintaining multiple data silos can be costly.
In short, fragmented data environments not only waste staff time (a Forrester report found knowledge workers spend ~12 hrs/week “chasing data”) but also require extra systems and labor, both of which drive up operational expenses. This naturally brings us to the next point.
Inefficiency
As mentioned, siloed data causes duplicate and wasted efforts because departments do not have visibility into what others are doing.
These hidden search-and-reconcile tasks mean fewer hours spent on analysis or decision-making. IDC Research shows businesses lose an average 12% of annual revenue due to fragmented data sources. In practice, this could be departments re-creating reports and data models independently, so work is duplicated and hours are lost.
Missed Opportunities
Insights that could emerge from unified data are lost in the noise of isolated systems. Siloed data literally hides information from analysis. Moreover, given how important data is for AI adoption, when data lives in isolated systems, AI and analytics lose their footing. Partial datasets lead to blind spots and biased models, and emerging use cases like agentic AI and predictive analytics cannot deliver value when built on siloed inputs that fail to capture the full picture of customer behaviour or operations.
Silos also choke collaboration causing innovation drag. When teams cannot easily share findings or data, promising ideas fall flat. The cumulative effect of these missed insights is enormous.
Lack of Agility
On the ground, fragmented data slows leaders’ responses. Decision-making is reactive and fragmented, making it hard for organisations to adapt to changing market dynamics. Companies report that silos make decisions “take longer” and increase the risk of mistakes.
Silos also erode company culture. An entrenched “silo mentality” breeds an “us vs. them” mindset, with managers acting as gatekeepers and blocking timely coordination. In such environments, urgent market pivots can’t be executed smoothly, because departments hoard their data.
How Does AI Come into the Picture?
AI thrives on data. Data silos cripple AI by cutting off access to the diverse datasets it needs to work its magic.
Imagine you are a retail company launching AI to personalise online shopping recommendations. You are excited because you have all this data like purchase history, email engagement, customer feedback, etc. But here’s the problem: it’s all scattered. The sales team has the purchase data, marketing has the email engagement data, and customer support has the feedback. None of it is connected.
Now, your AI needs all this information to work effectively. Without it, the AI might recommend a product to a customer who already returned it and complained about its quality. Or, it might completely miss the fact that a customer who bought a baby crib six months ago, is probably ready for stroller recommendations. The AI isn’t wrong; it is just working with incomplete information, so it cannot deliver the insights or results you were hoping for.
That’s how data silos make things worse. Even if you have AI, it cannot do its job properly unless your data is unified.
So, What Is the Solution?
Start by looking for a platform that can pull data and workflows together. One place where sales, marketing, support, and back-office systems can finally speak the same language. The right tool should collect information from every corner of the business, make it accessible it in real time, and automate routine hand-offs so people focus on insight rather than chasing and cleaning up. Break the silos, and your AI projects suddenly have the clear, well-labelled fuel they need to shine.
Take Wolken Agentic AI, for instance. In Wolken HIVE AI Studio, teams can build task-specific AI agents that can talk to each other and connect with existing platforms across IT, HR, CRM, and more. These agents work together like a ‘hive’, which enables different departments to share data and collaborate without traditional integration headaches.
Back to our retail example. Once Wolken is in place, the sales team’s purchase records, marketing’s campaign results and customer-support feedback all land in the same pool. The AI agents dive in, see the full picture and serve up useful actions, like skipping a product that was returned and spotting the perfect moment to recommend a stroller six months after that baby-crib purchase.
Wolken also adds handy automations and clear dashboards so everyone can see what is happening, spot hiccups quickly and get on with higher-value work. Put simply, AI cannot shine without good, joined-up data. Wolken does the hard work of bringing that data together, so your AI projects finally have something solid to stand on.
Moving Beyond Technology
Breaking down silos is as much a cultural challenge as it is a technological one.
Organisations need to encourage cross-departmental collaboration and establish a data-sharing mindset. For example, companies can create data governance policies that ensure shared accountability and incentivise departments to contribute their data to centralised systems.
Training programmes can demystify AI for employees, helping them see it as a tool for empowerment rather than replacement.
Through this process, organisations can not only reap the benefits of AI but also be better prepared to adopt evolving technologies, as data is the source of all innovation.
Further reading:
- The economics of AI points to value of good data. By Felix Martin. Available from: https://www.reuters.com/breakingviews/economics-ai-points-value-good-data-2024-06-28/
- Want ROI From AI? Break Down The Enterprise Silos By Mihir Sukla. Available from: https://www.forbes.com/councils/forbestechcouncil/2024/09/24/want-roi-from-ai-break-down-the-enterprise-silos/
- Silo Persistence: It’s not the Technology, it’s the Culture! By Cromity, J., & de Stricker, U. (2011). https://doi.org/10.1080/13614576.2011.619924
- Organizational Silos: A Scoping Review Informed by a Behavioral Perspective on Systems and Networks By Bento, F.; Tagliabue, M.; Lorenzo, F. Available from: https://www.mdpi.com/2075-4698/10/3/56#B6-societies-10-00056
- What are data silos and what problems do they cause? By Scott Robinson Available from: https://www.techtarget.com/searchdatamanagement/definition/data-silo
About the Author:
Abhay Singh is a seasoned Data Engineer and Technical Lead at Wolken Software, bringing over 19 years of expertise in designing high-performance data architectures and scalable processing pipelines. Known for his deep understanding of distributed systems and data strategy, Abhay has led the deployment of sophisticated data infrastructures that power enterprise-grade solutions. His passion for transforming complex data into actionable intelligence drives his work, making him a key contributor to Wolken’s innovation in AI-powered platforms and enterprise automation.
About Wolken Software
Wolken Software is an enterprise-grade B2B SaaS company that uses agentic AI to automate workflows for enterprises. With its 3S Model (Simple, Scalable, and Secure), Wolken Software empowers global clients in their digital transformation journey. It offers Enterprise Management and Customer Service Desk by providing a powerful, low-code platform to streamline business processes.
Wolken Software caters to customers from the banking and financial services, the semiconductor, software, consumer goods, and electronic component industries and has grown to add many Fortune 100 companies to its clientele in the US, Europe, and Asian markets. It supports over 7,000 agents serving more than 50 million active end-users across 60 countries, processing more than seven million tickets annually.