Simpplr research find AI Ambitions Hit a Wall of Fragmented Workplace Data

Forrester has published a new white paper sponsored by Simpplr that examines how genAI and agentic AI are shaping digital workplace and EX technologies. The report is based on a survey of 310 IT respondents in North America and the UK. Though it does not separate the findings by region.

The report examines the factors influencing AI deployment, the associated challenges, and the anticipated benefits. It emphasises that fragmented knowledge systems are restricting the implementation of AI. According to 85% of IT leaders, unified data and integrated systems are essential for successful AI adoption.

The challenges are hindering deployment: 75% are interested in an AI-powered digital workplace platform. But only around a quarter have deployed one. Barriers to success include the skills gap (87%), fragmented systems (85%) and appropriate security frameworks (78%).

Dhiraj Sharma, CEO and Co-Founder of Simpplr, commented, “AI is revealing how fragmented the digital workplace is. You can’t scale AI if your digital workplace is disconnected. Most organizations are struggling because their knowledge, workflows, and systems were never designed to work together, and layering AI on top of that just compounds the problem.”

What is in the Report

The 21-page report begins with an executive summary and key findings, with the meat of the report contained within three further sections.

• AI Should Drive Productivity And Agility Before Revenue
• Fragmented Data, Missing Context, And Governance Gaps Limit AI Effectiveness
• Leaders Turn To Unified Digital Workplace Platforms To Scale AI

The report concludes with six key recommendations for actions organisations should take to successfully scale AI in the digital workplace. The three main sections consist of a mix of data visualisations, data points, commentary and analysis. There are no comments from third-party experts, Simpplr leaders, or even survey respondents. Nor have the authors cited any other research within the paper.

AI Should Drive Productivity And Agility Before Revenue

AI investments in the digital workplace are primarily focused on improving operational outcomes. Such as customer experience (CX) (68%), speed (65%), agility (65%), and employee productivity (65%), rather than direct revenue generation. Improved employee experience (EX) is also a significant objective(58%), as it is closely linked to better CX.

However, the report did not state what percentage is using AI to generate revenue directly. And it is unclear whether there were few responses or if it was not an option.

Employees are using AI to automate routine tasks and self-service requests, with more advanced applications, such as enhanced collaboration and workflow automation, emerging as AI maturity increases.

Governance approaches for AI remain preliminary, with most organisations documenting AI strategies (63%) and implementing responsible AI standards (62%). However, only 29% have established communities of practice to share governance knowledge and standards across teams.

The most valued outcomes are improved productivity (72%), AI utilisation by employees (52%), and satisfaction with AI (54%). However, organisations find them difficult to measure, and they need better systems to track these KPIs accurately.

Fragmented Data, Missing Context, And Governance Gaps Limit AI Effectiveness

The data suggests enterprise readiness, not model capability, constrains AI scale in the digital workplace: fragmented context, immature controls, and weak operating discipline turn AI from a force multiplier into a brittle layer over disconnected systems.

Most people (85%) believe that bringing together knowledge and data systems is essential. The main problem isn’t getting “wrong answers,” but that AI lacks a solid understanding of the organisation. A large majority (83%) think it will become more difficult to unify these systems as companies add more AI tools. Because every new tool makes integration more complex unless a common foundation exists.

Nearly half (45%) say that missing organisation context is the biggest reason AI doesn’t work well. This means that many AI projects get judged by their results before teams lay the proper groundwork, leading to disappointment and limiting trust.

Security and access control are the gating risk for agentic use cases. 78% calling for better security frameworks and 49% ranking AI security/access as a top concern, indicate leaders view scale as a control problem, not just a capability problem.

The 41% concern about agents that auto-connect systems or automate services end-to-end is particularly telling: these are the highest-value scenarios, but they require identity, least-privilege access, auditability, and clear decisions on credentialing (agent-as-user vs agent-with-its-own-identity). Without that, organisations will either constrain agents to low-impact tasks or accept unacceptable exposure—both of which slow ROI.

The skills gap is an adoption bottleneck and a risk amplifier. 87% say employees need more training, yet only 29% have communities of practice. That suggests that many organisations are treating enablement as one-off training rather than continuous capability-building.

Governance and observability are where strategy meets operations—and many organisations fail to operationalise them. The figures point to an execution gap: even with high-level intent, 51% struggle with observability, 48% with testing/oversight, and 48% with responsible-AI implementation. Put differently, organisations can start pilots, but they cannot yet instrument, test, and monitor AI reliably enough to scale.

Leaders Turn To Unified Digital Workplace Platforms To Scale AI

Despite facing architectural and governance challenges, organisations anticipate continued growth in their AI deployments over the next one to two years. With three-quarters expecting increases of up to 20% and another 20% foreseeing even greater expansion. Decision-makers are increasingly focusing on connecting AI to employee users, recognising that employee experience is central to the success of AI initiatives.

The priority is on enabling employees to work more efficiently. With many leaders viewing AI-powered digital workplace platforms as the pathway to these improvements. Respondents highlighted that digital workplace AI can eliminate mundane tasks and unnecessary communication loops. Thus, allowing employees to concentrate on higher-value work.

Over half believe that AI’s ability to help employees focus on more meaningful tasks will significantly enhance employee experience (EX), and half also see value in AI that reduces time spent searching for information. Security and access controls remain concerns when integrating multiple systems, but leaders are confident that effective cross-system integration and automation will ultimately improve EX.

Organisations see a unified, AI-powered digital workplace platform as essential for addressing fragmentation and scaling AI. Such platforms unite people, knowledge, applications, and workflows, supporting employee productivity, engagement, and business agility. Three-quarters of respondents want to adopt these platforms, with nearly a quarter having already started.

The anticipated benefits include increased employee productivity (68%), enhanced operational agility (55%), and improved employee experience (55%). Achieving these outcomes requires a solid foundation of unified data, shared knowledge, robust governance, and contextual understanding, enabling organisations to fully realise the value of AI and drive measurable improvements.

Enterprise Times: What does this mean

This report underscores the transformative potential of generative and agentic AI in the digital workplace. However, it also underscores that organisations must first address foundational challenges to realise meaningful impact. For organisations, the key takeaway is that AI success depends on unifying fragmented data, knowledge, and governance systems.

Without a consistent digital foundation, AI initiatives often fail to deliver value, as disconnected systems limit AI’s ability to automate, guide workflows, and enhance employee experience (EX).

For IT leaders, the report emphasises prioritising operational effectiveness—such as productivity, agility, and employee satisfaction—over direct revenue gains. Leaders should invest in unified, AI-powered digital workplace platforms that bring together people, knowledge, and workflows, while also strengthening security, access controls, and governance frameworks.

Upskilling employees and fostering communities of practice are critical to closing AI literacy gaps and ensuring responsible, scalable adoption. The report is worth downloading and reading for the six key recommendations alone, and it adds the context required for them. For Simpplr, the findings validate its strategy of offering unified digital workplace solutions.

The report positions Simpplr as a key enabler for organisations seeking to overcome fragmentation and scale AI effectively. By addressing data unification, governance, and employee readiness, Simpplr can help clients achieve improved productivity, agility, and EX—aligning with the top priorities identified by IT decision-makers in the study.

Where the report falls short is the lack of a qualitative element that could have provided greater insight into the challenges faced. It also appears to be an IT-centric rather than a business-centric report. It is a little concerning that respondents did not view AI as having the potential to generate revenue; the board may see this differently.

AI Highlights The Limits And Potential Of The Digital Workplace can be downloaded from the Simpplr website (registration required).

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