Think back to when chat-based assistants like Google Assistant or Siri first appeared. These early tools promised convenience, letting us type or speak a question and receive a quick digital reply.
Yet, their capabilities were often basic. If the answer wasn’t in their database, you’d be prompted to look elsewhere. Their usefulness hinted at the future, but the real breakthrough was still to come.
Generative AI changed the game by filling in many of those old gaps. Now, you can ask pretty much anything, in whatever way makes sense to you, and get answers that are tailored and easy to tweak, completely transforming the way we find information.
Cofounder and CPO of Revenue AI platform Gong.
It wasn’t long before technology leaders saw the business potential: imagine connecting a chatbot to company data to create a kind of digital copilot that could answer business questions in real time.
No need to jump through menus or juggle dashboards, just talk to the system and get the info you need, right when you need it.
But here’s the thing: that vision isn’t reality yet. Recent research shows that chatbots haven’t had a big impact on productivity or the bottom line, mainly because business processes need more than just a chat interface: they need structure, reliability, and context.
The idea of replacing traditional software with chat is tempting, but it misses a much bigger opportunity: putting AI right where the real work happens.
Reducing friction and moving past reactivity
One big challenge for chat-based AI in business is something called the “blank canvas” problem. Chatbots are meant to be open-ended: you can ask anything, and they’ll do their best to respond. That’s great for consumers, but in a business setting, it can slow things down.
When chatbots don’t have a definitive answer to an open-ended question, they may offer misleading or incorrect information, and knowing this, workers need to spend extra time verifying outputs against trusted sources.
Picture a salesperson juggling lots of clients and prospects. Instead of leaving their email to ask a chatbot about an account, refining their question, reviewing the result and then returning after reading the answer, what if the company’s AI was built right into their existing tools? That way, insights pop up instantly, no detours needed. In this scenario, chatbots just add extra steps.
All too often, chatbots operate outside the main systems that employees use every day. If a manager wants to check on a sales pipeline, for example, they might have to bounce between a CRM and a separate window – ask a question, switch apps, copy, paste, repeat. Not only does this eat up time, but it also increases the chance of mistakes and keeps important knowledge scattered across different tools.
There’s also a key difference between reactive and proactive AI. Chatbots sit back and wait for someone to ask them something. But in the fast-moving worlds of sales, finance, or customer support, no one can afford to wait for the right question. Teams need to know if a deal is in trouble or a forecast is off, before anyone even thinks to ask.
Embedded AI doesn’t just sit around. It spots issues as they happen, brings them right into the tools people are already using and suggests next steps without prompting. Then, it’s not just answering questions, it’s actively influencing outcomes.
Consistency is crucial
Another challenge with chat-first interfaces is getting consistent answers. Give five employees the same task, and if they all use a chatbot, they’ll probably get five different answers. Some may use detailed prompts, others might be more general. That kind of inconsistency just doesn’t work for teams that rely on shared workflows and consistent results.
When AI is built into business tools, it helps everyone stay on the same page. It makes sure all employees follow the same best practices, right from inside the platforms they already use, like Microsoft Teams or Word. After a call, for instance, the system can immediately offer up next steps that match the organization’s goals and policies.
Bringing AI into the workflow is the way forward. When intelligence is built into a sales platform, for example, it can automatically spot red flags, suggest actions, and update forecasts in a clear, consistent way. No perfect prompts needed, it just works.
There’s also a limit to relying only on text chat. Business decisions usually need complex data that lives in lots of different places. A chatbot can summarize what it’s given, but it can’t match the interactive experience of a dashboard that shows regional numbers, customer segments, or makes real-time predictions.
Business professionals need more than just a summary. They need insights that are clear, relevant and actionable. Knowing what’s going on is helpful, but when the pressure is on, people need to know exactly what steps to take to get the best results.
Adaptability > eloquence
Chat interfaces are often impressive in their fluency, but business leaders shouldn’t confuse how something looks and feels with the value it brings. Chatbots will keep being popular with consumers, but the real win for enterprise AI is in how it helps teams make better decisions and reach better outcomes.
When you embed intelligence into the platforms employees already use – whether that’s CRMs, messaging apps or other AI copilots – it makes it easier for everyone to access trustworthy information as part of their daily workflows.
That means automating tedious research and serving up the right business insights at the right times to help workers make the best decisions. In turn, employees can focus on connecting with customers, equipped with the guidance they need to truly succeed.
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