What does tool consolidation mean in the era of AI?

Article summary

AI collaboration transforms teamwork in consolidated enterprises by creating augmented intelligence where human creativity and machine processing combine. Key capabilities include intelligent content organization (AI-powered clustering), automated insights and decision support, and cross-functional alignment. Organizations see 30% meeting time reductions, improved decision quality, and enhanced creative outcomes. Success requires developing “shared cognition” between human and AI teammates, building AI literacy, and maintaining transparency and user control in AI systems.

The real potential of enterprise AI

The promise of AI in the workplace has long been framed around automation—letting machines handle repetitive tasks so humans can focus on higher-value work. But our latest research reveals a more nuanced reality: While 61% of knowledge workers feel excited about AI’s potential, the biggest gains aren’t coming from what AI does alone, but from how it enhances human collaboration.

This shift has profound implications for tool consolidation strategies. As organizations streamline their tech stacks, the platforms that survive won’t just reduce costs—they’ll amplify human potential through intelligent collaboration.

Despite all the focus on individual productivity gains, 32% of workers predict that AI’s biggest impact will be on collaboration itself. This matters because collaboration is one of the four fundamental building blocks of an innovative company culture—alongside purpose, adaptability, and customer-centricity.

Yet many organizations approach AI collaboration backwards, adding AI tools to existing workflows rather than reimagining how teams work together when augmented by intelligent systems. The result? AI becomes another source of fragmentation rather than a consolidation opportunity.

Many organizations approach AI collaboration backwards, adding AI tools to existing workflows rather than reimagining how teams work together when augmented by intelligent systems.

The most successful organizations use tool consolidation as a chance to embed AI collaboration capabilities into core workflows, creating human-AI teams that outperform either humans or AI working alone.

What makes AI collaboration different

Traditional collaboration tools connect people. AI collaboration platforms actively participate in the collaborative process, creating human-AI partnerships that enhance decision-making, accelerate insight generation, and improve team alignment.

Research on human-AI teaming reveals that effective implementations share three characteristics:

• Contextual intelligence: AI understands not just what’s being discussed, but the broader context of team goals, project history, and organizational priorities.
• Adaptive participation: Rather than following rigid scripts, AI adjusts its involvement based on team needs, sometimes providing active input and other times staying in the background.
• Transparent collaboration: Team members understand how AI contributes and can easily modify or override its suggestions.

Miro’s AI capabilities exemplify this approach. Instead of replacing human creativity, Miro AI acts as an intelligent collaborator that can cluster ideas by sentiment, transform brainstorms into structured documents, and generate prototypes from rough concepts—while keeping humans in control.

The consolidation advantage in AI collaboration

When organizations consolidate collaboration tools, they create unique AI enhancement opportunities impossible in fragmented environments. Consider the typical enterprise: Teams use different tools for brainstorming, project planning, documentation, and presentation. Each tool operates in isolation, with AI capabilities limited to specific contexts.

But when teams consolidate onto an AI-enhanced visual collaboration platform, the AI can see across the entire collaborative workflow—from initial ideation to final delivery. This comprehensive view enables capabilities beyond tool-specific automation:

• Cross-functional insight generation: AI identifies patterns across different collaborative work, surfacing insights that might be missed when data is siloed.
• Intelligent workflow optimization: By understanding how teams work together, AI suggests process improvements and automates routine transitions.
• Contextual knowledge preservation: Instead of losing institutional knowledge when team members leave, AI captures and makes accessible the reasoning behind decisions.

Real-world AI collaboration impact

Leading organizations are seeing measurable results from AI-enhanced collaboration in consolidated environments:

1. Accelerated decision-making: Teams report 30% reduction in meeting time when AI pre-analyzes materials, identifies key decision points, and surfaces relevant historical context.
2. Enhanced cross-functional alignment: AI-powered insights help teams quickly identify consensus and disagreement areas, enabling focused discussions and faster conflict resolution.
3. Improved innovation velocity: By automatically organizing and connecting ideas across projects and teams, AI helps organizations spot innovation opportunities.
4. Reduced cognitive load: When AI handles routine organizational tasks—sorting feedback, tracking action items, maintaining timelines—humans focus on creative problem-solving and strategic thinking.

Overcoming human-AI collaboration challenges

Despite potential benefits, research shows human-AI teams often underperform due to trust issues and inadequate mutual understanding. Our survey data reflects this: while 76% of workers believe AI could benefit their role, 54% struggle to know when to use it.

While 76% of workers believe AI could benefit their role, 54% struggle to know when to use it.

Success requires designing AI collaboration systems that address human factors:

• Building appropriate trust: Set realistic expectations and provide clear feedback about AI confidence levels and limitations.
• Maintaining human agency: Enhance human decision-making rather than replacing it, ensuring people control important choices.
• Supporting skill development: As teams become comfortable with AI collaboration, platforms should evolve to support more sophisticated partnerships.
• Preserving human connection: AI should enhance collaboration efficiency without replacing the human relationships that make teams effective.

The future of consolidated AI collaboration

The organizations that will thrive view AI collaboration not as an add-on feature, but as a fundamental reimagining of how teams work together in consolidated environments.

This transformation is already beginning. Our research shows 69% of workers plan to upskill on AI in 2025, and 66% believe their AI skills will make them more competitive. As adoption accelerates, we’ll see new forms of human-AI collaboration that are barely imaginable today.

The implications for tool consolidation are profound. Instead of choosing platforms based primarily on features or cost, organizations will evaluate how well solutions support the evolution of human-AI teamwork.

Platforms like Miro, with flexible visual interfaces and embedded AI capabilities, represent this evolution’s direction. They offer the consolidated simplicity IT leaders need while providing intelligent collaboration capabilities that will define the future of work.

The future of enterprise collaboration isn’t human versus AI—it’s human with AI, working together in ways that amplify the best of both. Organizations that master this balance will find themselves with a significant competitive advantage.