AI Shows Promise in Bridging Business Divides, Experts Say

AI software that crafts consensus statements from opposing viewpoints could one day be a tool for smoothing corporate negotiations and stakeholder disputes.

A new breed of artificial intelligence system can analyze conflicting positions and generate balanced group statements that capture majority and minority perspectives. This could transform how businesses handle everything from labor talks to merger discussions. Researchers and business consultants say AI could help parties find common ground faster than traditional mediation.

“We have to work together, we have to collaborate,” Hanne Wulp, executive consultant and founder of Communication Wise told PYMNTS. “These hardened, far out-of-the-middleground perceptions don’t come in handy. There won’t be many others to collaborate with. When AI-driven mediation can soften, or tweak, that lens slightly, just by gathering and framing perceptions in a neutral, non-confrontational/collaborative way, it can enhance collaboration.”

Study Shows Promise in Consensus Building Tool

The new AI tool developed by Google DeepMind shows promise in bridging ideological divides through group discussions. In a study published in Science, researchers found that their “Habermas Machine” — based on the Chinchilla language model — effectively synthesized opposing viewpoints into consensus statements. Testing with 439 UK residents revealed that 56% preferred AI-generated summaries over human mediators. The system could improve citizens’ assemblies and public policy discussions by creating more balanced, representative statements — or even have commercial implications.

“Group statements generated by AI can integrate the needs, opinions, and cultural backgrounds of different consumers,” Alex Li, Founder of AI company StudyX, told PYMNTS. “This inclusive marketing strategy can resonate with more consumers, enhance brand appeal, and finally influence consumer behavior, making them inclined to choose products that align with their values.”

AI systems are increasingly helping businesses reach consensus in complex commercial decisions. OpenAI’s Swarm Framework allows multiple AI agents to work together, streamlining decision-making. Google’s Gemini models enhance negotiation capabilities, helping companies align transaction interests. IBM’s Watson assists supply chain management by analyzing data from different stakeholders, leading to agreed-upon solutions for sourcing and logistics. Additionally, platforms like Pactum automate contract negotiations, ensuring fair terms for all parties.

Skeptics Abound

Not everyone’s a fan of AI taking charge of sending out group statements. Michael Taylor, CEO of SchellingPoint, which manages what he describes as “the world’s largest database of real-time group decisions” with over 9 million data points, told PYMNTS he’s skeptical about AI-generated group consensus.

Taylor explains that when groups first discuss a shared topic, “17% of their opinions are like-minded, and 83% are non-likeminded.” Using a framework based on Harvard Professor Chris Argyris’s work, his organization analyzes why people agree or disagree.

He identifies key patterns in group disagreement: “30% of the time” differences stem from varying access to information, while “65% of the time” disputes arise from different interpretations of terminology. He argues that both cases can be resolved through understanding rather than compromise.

“Replacing the reconciliation of non-aligned opinions with suggested group statements to gain consensus would significantly compromise the accuracy and integrity of the strategies, policies, decisions and changes these groups are forming,” Taylor warns.

Instead, SchellingPoint has developed an AI system that analyzes group thinking patterns and helps determine accurate conclusions rather than seeking consensus for consensus’s sake.

“AI should be used iteratively at the individual level not for consensus, but rather to counter internal bias, poorly actuated mental heuristics, and undiscovered cognitive dissonance,” Christopher Kaufman, a professor of Business and Leadership Studies at Westcliff University, told PYMNTS. “Then each person, after their own iteration of bias and dissonance discovery, can then use AI to present their own new formatted concepts. And then see if their new ideas are adopted by human consensus.”