Aligning your AI avalon for growth, efficiency, and intel

If the word ‘AI’ isn’t reflected in your business plans, product roadmaps, marketing messaging, reporting and forecasting, the likelihood of your org faltering is high. And that’s because AI is a movement, not just a suite of tech. 

Those who don’t follow this credence typically fall down the ‘shiny new toy’ path when it comes to AI application. It’s not about collecting all the toys. It’s about isolating one well-oiled and intended machine to support and, in some cases, run your org-wide functions.

From a sheer efficiency, growth, and insights perspective, AI has the competitive edge. It has the capacity to take direction from you, learn on its own, and work autonomously–yet collaboratively–to support your goals and intended outturns. Think of it as your top performing team member. Capable of accomplishing tasks faster and more effectively so that your productivity is focused on higher value activities. 

All of this is said with the caveat that, without the right data sets, data centricity, and data integrity, AI is limited when it comes to knowing your business, building and scaling systems of efficiency, and inevitably hitting your growth marks. 

This is where a healthy data ecosystem is key. 

Building your AI avalon

It’s not enough to infuse AI within your orgs, your tech, and your teams. You need the right data to build your AI wonderland. If your tech stack is disparate and disconnected, there’s no source of truth for your AI engine. Not seeing the full data picture is one; bad for business in general, but two; bad for AI’s mental health. There are a number of reasons why AI hallucinates, and bad data is one of them. Your AI output is a direct reflection of your data's well-being. 

Whether you’re working with AMS', CRMs, CDPs, or other more specialized solutions like EMS’, LMS’, or CMS’, you need an integration that connects all of these data sources. Otherwise, you’re not seeing the full narrative for every one of your contacts. And if you’re not seeing them, your AI companion certainly isn’t. 

For example, HubSpot's Breeze Customer Agent is built to support the entire customer lifecycle. With your guidance and inputs, it creates a blueprint to meet the customers where they’re at, answer their questions with clarity and context, and guide them in the right direction based on key customer flags and indicators. 

Unfortunately, a good portion of your customer data is held in your CMS and EMS, and neither of those systems are connected to your AMS. AI feeds on the data it has access to. And those activities (both rich in identifying content and behavioral interests) aren’t factored into the story. 

So Jane Winsome interacts with your AI Customer Agent. A nice rapport is built and Jane gets the majority of her questions answered. But Jane's session interests and content downloads aren’t accounted for within the interaction. And from a content and product mapping perspective, there’s a huge lead gen miss. This isn’t an AI limitation. This is the byproduct of an unintegrated tech strategy.

Applying the right data governance, policies, and business rules multilaterally across your org is a necessity when it comes to building your AI data empire.

Choosing master tech over point solutions

So yes, data consolidation is key. And while integrating all of your data systems is good practice, isolating one umbrella AI-powered system that’s capable of doing the majority of the work, and integrating plug-ins or other point solutions as needed, is best practice. 

While HubSpot remains the front-runner in this, most platforms are ecosystem-friendly. These providers recognize that their ability to play nice in the (sometimes literal) sandbox creates even more value from a data perspective. And while not all integrations are alike, there will always be a way to connect the lion share of your tech. Take advantage of it. 

When you’re searching for integrations, whether productized, custom shops, or iPaaS solutions, remember that your goals come first. If those integrations don’t support your unique use cases, keep moving. Centralization is one thing. Meaningful alignment with your business is crucial, particularly when it comes to AI. 

When it comes to marketing, services, and sales, HighRoad's Spark integration has its place as it relates to centralizing data under HubSpot's umbrella to serve up connected growth, engagement, and retention approaches. That's not to  undermine the other integrations out there. They all have their place. But investing in a Zapier-like integration that's more focused on maintaining enterprise continuity (in other words, more of a hub & spoke model versus revenue-generating model) isn't necessarily going to cut it when it comes to supporting segmentation, campaigning, and reporting mechanisms. 

Adapting with intent

Sure, you don’t necessarily want to let AI pass you by. That’s clear. But forcing it without the right data and tech synchronization, the right staffing skillsets, and the right change management practices in place, could create risk. 

Evolving into AI at a comfortable clip is best for your business. That means, approaching AI with a roadmap in mind. Isolating and prioritizing your business goals is key. And when it comes to cultural infusion, getting it right the first time is your greatest selling point. Not committing and falling flat is perhaps the fastest way to demotivate your staff around the idea of AI. Make sure you have a strategy in place, identified champions, and documented commitments to run the play. 

For instance, if your primary intent is to generate more leads, open up new markets, and grow revenue, that demonstrates a clear need for Prospecting AI. If you have the formula–data, tech, skill, and will–in place to do the job, start your AI experience with your funnel and pipeline as your prototype. 

If you’ve plotted out your A to Z blueprint, you’ll have the metrics and dashboards to report out on your definition of AI success. There’s no better testament and motivation to move further down the roadmap than a well thought and well executed AI initiative. Demonstrating first hand to your staff that they are crucial (not obsolete) to the process, is key.

In the end, there’s no algorithm for empathy when it comes to AI. When your team’s inputs and interactions become joint in effort, they realize not only their value in the process, but the value they get in return from successful implementation. 

This makes moving into the next initiative (let’s say predictive analytics) even easier because you’ll now have a framework, measurable achievements, and even more confidence going into your next stint. 

Going with the grain, not against it

All this to say that taking it slow doesn’t mean throwing in the towel altogether. All too often, businesses fall into change paralysis. Collective over-thinking can get in the way of real movement. While you should always move with meaning in every AI initiative, keep in mind that your efforts may not be spot-on immediately. And that’s completely okay. 

AI acceptance and infusion is meant to be iterative in nature. The most important factor is that you pull in your org’s collective brain power to really hone in on that first step. Then you take the leap. Keep in mind, when it comes to a movement with no stop button, change is inevitable. When you realize change is a natural and welcomed part of the process, it’s much easier to move into paths of uncertainty. 

Orgs working with legacy mindsets and systems may feel comfortable and steady in the now. But in the end, these philosophies and practices are toxic for AI adoption. These associations and nonprofits aren’t just stepping into an unlevel playing field. They’re taking themselves off the field altogether.