How generative AI for business enhances collaboration
Generative AI, in combination with agentic AI, presents some intriguing use cases that companies should consider as they assess their collaboration needs.
I first wrote about the effect of generative AI on workplace collaboration in 2023, when the technology was still new, and agentic AI was not yet in the market. The core use cases for workplace collaboration have not really changed since then, but as generative AI (GenAI) evolves, those use cases have broadened. Today, not only does the technology enable deeper forms of collaboration, GenAI for business also enhances personal productivity.
As agentic AI becomes widely used across the enterprise, IT leaders must consider a new layer of business value. Let's examine how GenAI is reshaping collaboration and consider the role agentic AI may play. To do that, here are four collaboration use cases and two key deployment challenges.
4 collaboration use cases for GenAI
1. Virtual assistants
This would be Copilot and similar assistants, which have become standard for all unified communications as a service platforms and are widely used for tasks, such as calendar management, email drafts, meeting summaries and prioritizing work. To some extent, agentic AI takes these capabilities even further, but the overall goal is to automate routine or simple tasks, so workers can spend more time on critical thinking, creative problem-solving and managing complex tasks with teams.
These needs haven't changed since 2023, but GenAI can now take on a bigger share of the everyday workload. The further AI goes on this path, the more quickly workers' personal productivity will improve, allowing them to be more present and engaged when collaborating with their teams.
2. Content management
As was the case in 2023, GenAI is still used to generate content for workers. Fed by search queries and end-user prompts, GenAI streamlines collaboration by generating reports, action plans for team members and articles or posts for social media. This is another form of automation, but it also helps teams work faster because GenAI produces these outputs faster than most workers can do on their own.
Agentic AI makes content generation more intelligent. It also fuels content creation by producing copy whose quality often surpasses what workers can write. Content management, too, benefits from agentic AI tailoring content to the specific needs of a team or a project that workers are collaborating on.
3. Workflow management
This is core to collaboration and goes beyond content generation and the automation of everyday tasks for individual workers. Collaboration often involves cross-functional teams, and in hybrid organizations, these teams will be a mix of office and home-based workers. This requires a higher level of workflow management, where processes and information-sharing must be orchestrated across multiple systems, databases and platforms.
The more distributed and geographically dispersed these teams are, the more challenging collaboration becomes, especially when real time results are needed. GenAI -- as opposed to legacy tools -- can automate intelligent workflows at scale and orchestrate them across a complex set of systems, thus ensuring consistent collaboration.
4. Document management
AI is well-suited for document management, simply due to the sheer volume of documents that enterprises must manage. As organizations move along the digital transformation spectrum, documenting everything becomes easier, which presents new management challenges. For any given task or group project, there are now so many documents involved that nobody has the time or ability to personally review them all.
This creates workflow bottlenecks and raises the potential for human error, such as missing important information, using outdated or inaccurate materials or failing to validate sources. This can result in poor decisions, bad outcomes and the possibility that workers could unwittingly expose sensitive documents or share details in a non-compliant manner.
GenAI makes document management more efficient and effective, but only if documents are digitized with these needs in mind. If that's the case, agentic AI can be used to process all these inputs and incorporate the most relevant pieces into workflows for humans to work with.
AI is not yet mature enough to be fully trusted for these needs, but working side-by-side with human workers, these tools can be very beneficial for document management. By extension, this also applies to knowledge management, where generative AI for business can make it much easier for workers to retrieve essential information from vast troves of documentation across the organization.
IT challenges with GenAI for collaboration
Either in part or in whole, these use cases illustrate GenAI's wide range of benefits for enterprises, especially for improving collaboration. That alone provides IT leaders with a solid rationale to invest in AI. The business case becomes even stronger with agentic AI as workers become comfortable having tasks and workflows fully automated by AI. That said, these capabilities are far from being fully formed, so IT leaders must consider AI in a broader context. Two challenges in particular should be top-of-mind for IT's decision-making.
1. Managing AI deployments at scale
GenAI pilots usually run with small groups and with fairly simple scenarios. Scaling GenAI across the organization for collaboration is far more challenging, especially with highly decentralized teams. Many enterprises still operate in silos, which can create several obstacles for effective teamwork.
On a technology level, integrations may be weak and impede information-sharing and workflows. Another issue is governance, both in terms of clear guardrails to manage AI and applying it consistently across the organization. Most enterprises are still in the early stages of AI maturity, so scaling AI for collaboration must come with realistic expectations.
2. Rethinking the nature of collaboration with AI
All forms of collaboration require some form of orchestration across the organization. IT has to address that technical challenge. Now, as AI becomes more central to everything in the enterprise, the nature of collaboration itself will change. That presents a different challenge.
To that end, IT leaders need to stop thinking about collaboration as being a human-only activity. That mode will certainly persist, but agentic AI introduces new forms of collaboration. Human workers will now collaborate with AI in addition to fellow coworkers. Part of being agentic means that AI can now make decisions about which tasks to complete independently and which tasks need to be done by human workers.
As AI evolves, skill sets for both humans and AI agents will become fine-tuned; agentic AI will assign tasks to optimize workflow orchestration for collaboration. Generative AI for business is really just the first step toward this broader model of collaboration. Agentic AI will transform collaboration into an activity where human workers and bots are interdependent, rather than AI just being a tool for workers to help do their jobs.
Jon Arnold is principal of J Arnold & Associates, an independent analyst providing thought leadership and go-to-market counsel with a focus on the business-level effect of communications technology on digital transformation.
