The Generative AI Manifesto: Experiment Fast, Early, and Responsibly

When we look back on 2023, it will be remembered as the year Generative AI changed the world. Already we are seeing the impact of Generative AI across industries and professions—the contact center included.

More than two years later, that shift has followed exactly the path this manifesto argued for. The same principles of experimenting early, fast, and responsibly have carried the contact center from generative AI features that draft a summary or suggest a reply to full agentic AI contact center deployments, where AI agents for contact centers handle interactions and complete tasks on their own rather than just producing text for a human to send. VoiceAI Agents and ChatAI Agents now resolve voice and chat conversations end to end, and a Companion Agent, the evolution of real-time agent assist, supports human agents while they work. A third layer, AI agents for operations, evaluates those interactions and generates coaching and insights for both human and AI agents. The guidance below still applies. It now extends to a more capable class of AI agent.

As with mobile, and cloud computing before it, those that do not adapt will fall behind.

With any paradigm shift, there are two critical choices to make:

1. What will you do?
2. Who will you trust?

What will you do?

The answer to the first question should be straightforward by now: We must embrace it or risk becoming obsolete.

Yes, there are a lot of unknowns.

Yes, there are real and relevant reservations about the technology.

But we also have the rare opportunity to be the pioneers in this revolution.

This is my strong recommendation to you: Experiment early and experiment fast, but experiment responsibly. 

Generative AI provides a nearly zero-barrier to entry for most applications. And most implementations can be reversed.

For any Generative AI application you’re considering, if it checks those two boxes (low barrier to entry and easily reversible) and the value is there, then it’s well worth a test.

The downside is minimal, and the upside is limitless.

Experiment. Learn. Iterate.

But this brings me to the second question.

Who will you trust?

Every pioneer has leaned on the expertise of a guide. The sherpas of Mount Everest. Sacajawea to Lewis and Clark. Who you partner with often determines your success or failure.

Right now, the answer to this question is more confusing than ever.

Type “Generative AI for Contact Centers” into Google and you’ll get over 20 million results.

So let me provide some answers:

Should you use ChatGPT for your contact center?

Short answer: No. Why? While ChatGPT is impressive and produces comprehensive and cohesive answers, it is prone to serious inaccuracies and hallucinations, making them too risky to use on an enterprise level.

‍How can you experiment responsibly?

Generative AI solutions must have the ability to calibrate and fine tune the system. By nature, generic out-of-the-box models are trained on broad data sets and will not understand the nuances of contact center conversations. A black box solution without the means to calibrate it can be risky when things go awry. No machine gets it right the first time, every time. Allowing humans to refine the machine is essential.

What should you look for in a Generative AI partner?

First, are they well-versed enough in contact center dynamics to understand the intricacies of customer interactions, agent workflows, and the overall operational ecosystem. Someone with this level of expertise will be an accelerator to your experimentation with Generative AI.

Second, do they have a team of AI and machine learning experts with a strong track record. The low barrier to developing Generative AI solutions means simpler applications developed by teams unfamiliar with the contact center space will soon be worth a dime a dozen. Firms with an AI pedigree and funding will outlast and out-innovate those who may have made a splash being first to market. 

Third, understand the underlying technology. Most solutions are using out-of-the-box language models trained on generic data sets. On the surface, these may appear worthy of experimentation, but they are limited in capability by nature. The truth is, contact centers need an LLM that can adapt to their data nuances for better comprehension of interactions and more accurate identification of key actions and events you need to make better business decisions.

Applying This to Agentic AI for Contact Centers

The same test still applies when you evaluate an agentic AI contact center deployment: is the barrier to entry low, and can you reverse course if it does not work. Start by piloting a VoiceAI Agent or ChatAI Agent on one well-defined interaction type, measure the results against your current process, and expand only once those results hold up. Ask any vendor offering AI agents for contact centers the same questions you would ask about a generative AI partner: do they understand contact center operations, do they have a proven AI team behind the product, and is the model built on your data rather than a generic one. An AI agent that acts on a customer’s behalf, including a Companion Agent working alongside a human, needs the same calibration and human oversight this manifesto called for from the start, because autonomous action raises the stakes of getting it wrong.

Announcing Observe.AI’s Proprietary Contact Center LLM

Every day your agents are interfacing with customers on the front lines of your business. Every interaction is an opportunity to drive revenue, build brand loyalty, and provide customer support.

By now, there are dozens (if not hundreds) of solutions claiming to leverage Generative AI to improve, accelerate, or automate every one of these touch points.

The question you need to ask is: Can you trust them to do the job you need them to do?

If your provider’s solutions are built on generic LLM models, you may want to reconsider for all of the reasons I’ve mentioned above. 

Our mission has always been to help contact centers, and their leaders, drive better performance across the entire operation with the most robust and useful technology and expertise.

This is exactly why we have been developing our industry-first contact center large language model. 

Our groundbreaking proprietary 30 billion parameter LLM is customized for contact centers and trained specifically for contact center use cases, including automatic summarization, generating coaching notes, helping agents to query knowledge, and extracting insights.

Today, we are officially launching our own groundbreaking proprietary LLM to power our suite of Generative AI solutions: Knowledge AI, Auto Summary, and Auto Coaching. Read more about them here.

To evaluate the performance objectively, we conducted a comparative analysis between our proprietary model and GPT3.5 and the results were dramatic. Not only that, but we give you more control over the model with a feedback loop to improve and fine-tune it to the needs of your business.

This may sound like yet another claim made by yet another vendor. I don’t blame you if you’re skeptical.

So I invite you to see a demo with one of our experts and test it out for yourself. 

This is a new era of contact center artificial intelligence and we are here to partner with you every step of the way.