Fragmented collaboration undermines AI gains in digital transformation

From a sustainability perspective, fragmented collaboration increases digital waste. Redundant documents, unnecessary meetings, repeated communications, and inefficient cloud usage contribute to rising energy consumption and carbon footprints. The study highlights that sustainability challenges in digital transformation are not only technical but organisational, rooted in how collaboration is structured and managed.

The rapid adoption of digital tools, remote work systems, and artificial intelligence-enabled platforms has not only improved speed and reach but also created fragmented workflows, duplicated tools, rising digital fatigue, and growing sustainability costs. While AI is widely promoted as a solution to inefficiency, evidence shows that without mature collaboration structures, AI investments fail to deliver long-term value.

A new study titled “AI-Augmented Digital Collaboration Improvement Framework for Sustainable Digital Transformation,” published in Administrative Sciences, directly addresses this gap. The research introduces a holistic framework that positions digital collaboration maturity as the central mechanism through which AI and sustainability can be effectively embedded into organisational transformation.

Fragmented digital collaboration limits AI and sustainability gains

The research diagnoses a problem common across industries: digital fragmentation. As organisations adopt multiple collaboration tools in parallel, teams often operate in disconnected digital ecosystems. Messaging platforms, document repositories, project management systems, and communication tools are used inconsistently, leading to information silos, duplicated work, and excessive communication loops.

The study finds that this fragmentation directly undermines collaboration maturity across strategic, technological, managerial, and behavioural dimensions. Even in organisations with high levels of digital tool adoption, collaboration effectiveness remains limited when tools are poorly integrated and governance is weak. This fragmentation also prevents AI from performing its core optimisation functions, as AI systems depend on interoperable data flows and stable workflows.

From a sustainability perspective, fragmented collaboration increases digital waste. Redundant documents, unnecessary meetings, repeated communications, and inefficient cloud usage contribute to rising energy consumption and carbon footprints. The study highlights that sustainability challenges in digital transformation are not only technical but organisational, rooted in how collaboration is structured and managed.

The research shows that while many organisations invest heavily in AI-enabled tools, they often overlook the foundational collaboration structures required to support them. As a result, AI adoption remains superficial, limited to isolated features such as scheduling automation or content assistance, rather than driving systemic improvement.

A four-dimensional framework redefines collaboration maturity

To address these challenges, the study introduces a digital collaboration improvement framework built around four interconnected dimensions: strategy and structure, technology, management and processes, and culture and behaviour. Unlike traditional maturity models, this framework embeds AI and sustainability mechanisms across all dimensions rather than treating them as separate initiatives.

The strategy and structure dimension focuses on governance, alignment, and organisational coherence. The study finds this to be the weakest area in most organisations, with limited collaboration guidelines, weak cross-unit alignment, and insufficient integration of collaboration practices into corporate strategy. Without strategic coordination, AI-driven insights cannot be effectively translated into organisational action, and sustainability goals remain disconnected from daily work routines.

The technology dimension examines how collaboration tools are selected, integrated, and used. While tool adoption is often high, the study identifies poor system integration as the single weakest factor across the entire framework. This lack of integration prevents AI from enabling workflow optimisation, usage analytics, and automated coordination. It also sustains inefficient digital infrastructures that increase energy use and operational costs.

The management and processes dimension addresses leadership involvement, training, performance evaluation, and institutional support. The findings reveal a pattern of strong leadership commitment paired with weak structural routines. Managers support collaboration in principle but lack formal processes to embed AI-driven optimisation and sustainable practices into daily operations. This gap limits the organisation’s ability to scale AI benefits and systematically reduce digital waste.

The culture and behaviour dimension emerges as the strongest area across organisations studied. Employees generally demonstrate high levels of trust, commitment, and willingness to collaborate, even in the absence of strong structural support. This cultural strength acts as a compensatory mechanism, allowing collaboration to function despite fragmented systems. However, the study notes that without formal processes and strategic alignment, this cultural potential remains underutilised in advancing AI-supported and sustainability-oriented collaboration.

AI as an embedded force in sustainable collaboration

The study treats AI not as an external technology layer but as an embedded component of collaboration systems. AI is positioned as operating simultaneously across strategic coordination, technological integration, process optimisation, and behavioural support.

Within mature collaboration environments, AI supports advanced communication, task coordination, and problem-solving by automating routine activities, analysing collaboration patterns, and identifying inefficiencies. AI-enabled analytics can map tool usage, detect redundancy, forecast bottlenecks, and recommend improvements that reduce both workload and resource consumption.

The study highlights AI’s role in mitigating digital fatigue, a growing concern in highly connected workplaces. By reducing unnecessary meetings, streamlining communication flows, and automating low-value tasks, AI contributes to more sustainable work routines. These efficiency gains also translate into lower digital energy use, reduced cloud storage demand, and fewer duplicated digital artefacts.

Importantly, the research demonstrates that AI’s sustainability benefits depend on collaboration maturity. In fragmented environments, AI reinforces inefficiencies by accelerating poorly designed processes. In mature environments, AI becomes a governance and optimisation tool that aligns daily collaboration with sustainability objectives.

The framework also integrates sustainability metrics directly into collaboration improvement processes. Rather than treating sustainability as a reporting exercise, the study embeds it into workflow design, tool selection, and behavioural norms. This approach enables organisations to track reductions in digital waste, energy use, and redundancy as part of collaboration performance.

Empirical validation reveals systemic maturity gaps

The framework is validated through a mixed-method case study combining expert interviews and an organisation-wide employee survey. The empirical results place overall digital collaboration maturity between early and intermediate levels, indicating that collaboration practices are present but not consistently embedded.

The findings show that technology and culture outperform strategy and management dimensions. Employees widely use digital tools and demonstrate strong collaborative attitudes, but strategic alignment and process discipline lag behind. This imbalance creates a ceiling effect, where collaboration cannot advance further without structural reform.

Notably, leaders tend to rate collaboration maturity lower than non-leaders, particularly in management and process areas. This suggests managerial awareness of structural weaknesses that may not be visible at the employee level. The study interprets this gap as an opportunity for targeted intervention, using the framework to align leadership perception with operational reality.

The research also finds minimal variation across departments and regions, indicating that collaboration challenges are systemic rather than isolated. This reinforces the study’s argument that collaboration maturity must be addressed at the organisational level rather than through isolated tool upgrades or departmental initiatives.