AI & ML

Will AI take my job?

By
Steve Hazelton
April 7, 2023
5 min read

“100% of this article was written by a human” (True)

We’ve all seen large language models (LLMs) rapidly move into the mainstream, and many of us are already using them at present to generate blog posts, sales emails, and marketing campaigns (sadly, not yours truly). 

Indeed, there is no shortage of ideas and blog posts explaining how businesses will use LLMs to generate content and remove routine or laborious tasks. Some say LLMs will render coders and lawyers obsolete. (BTW, they won’t). While there will certainly be job disruptions, we believe the change will be one of radical productivity improvement and, as a result, much more interesting and fulfilling work for many of today’s knowledge workers.

Before continuing, let’s clarify. LLMs are just one type of AI, a “tool.” If you think that auto-creating a sales email is a kind of “meh” endgame of billions of funding, you won’t hurt our feelings. That’s because we feel the future of AI in business will be utilizing many AI tools that will go far beyond creating content. They will help you think and take action. They will help you do things that are almost impossible or are very expensive to do today.

If you love manually updating JIRA with bug reports, typing QBR results into spreadsheets every Friday, or getting five people in a room to discuss your best customer, then the adoption of AI for your business is going to be a bummer. For the rest of us, AI will make us much more productive, informed, and, ultimately, employable.

Before continuing, I would like to say that I have spoken to many companies that have “started using AI in our business.” This always means dumping a healthy serving of emails and support tickets into GPT and generating summaries. And many now say, “That was cool, but now what?”

They’ve just scratched the surface. At the risk of sounding like an AI hype machine, answering “What’s next?” is really difficult because we’re still trapped in an impossibility box: today, there are things we think are impossible but aren’t anymore. 

 

At Sturdy, business for AI will go beyond Generative AI, fundamentally changing how businesses collect, organize and synthesize their information. For now, I will call this AI Harmonization (AIH).  Again, this goes far beyond writing sales emails. Beyond the LLM, AIH will illuminate previously dark sources of data and harmonize that information across other models, thereby creating previously unimaginable strategic visibility and scale.

The pieces of this AIH pie are the following: 

  1. Collect and flatten metadata, structured data, and unstructured data into a privacy-compliant, permissioned, and normalized structure;
  2. Autonomously identify themes, topics, and insights inside this information and at its intersections. 
  3. Automatically deliver “stuff” to, and synchronize with, other systems, workflows, and people who need it.
  4. All of the above will be done automatically, in real-time, without supervision.
  5. (extra credit): Your interface to this information will fundamentally change from “click here, then click here” to “tell me what you want.”

Of course, what I just laid out vastly simplifies the challenge. Yet, I can’t help but feel super excited about what it means…here are some examples:

If you are an Account Manager, your day might start with, 

“Here are three accounts you need to look at right now.”

Or, let’s say you’re a VP of CS, 

“Let me know anytime an enterprise customer has an issue within 90 days of their renewal date. Check this every day at 9 am.”

Or, imagine being a new VP of Products at a SaaS business, 

“Every Tuesday, send me an XLS with a product roadmap with all bugs, organized by topic, reported by our enterprise customers worth more than 500k in ARR ranked by unhappiness generated and estimated engineering complexity.” 

Sure, many of the examples above are already completed in many well-run companies today. They are just being done with manual labor. A lot of manual labor. In fact, our Sturdy data shows us that as much as 45% of an Account Manager’s day is data entry. Logging tickets, updating salesforce, writing call summaries (that number doesn’t even count the “account review” meetings). 

Yes, AI will eliminate almost all this manual labor masquerading as knowledge work. 

It will automate almost all data collection… reporting…and, eventually, much of the response. 

But, worker productivity will skyrocket and allow our teammates to focus on much more meaningful tasks.

(Shameless plug: you could buy Sturdy today and next week 3 of the examples shown above are now part of your business. (What, you think I write blog posts for my health?))

In short, the people interacting on a day-to-day basis with customers will have about twice as much time to do high-value things, like talking to customers. If I had to guess, I would say that this probably means companies will have fewer Account Managers, but it also means that they’ll become twice as valuable (because they’ll be 2x more productive).

If you were hired as an Engineer in 1970, it is likely that for the first decade of your professional career, you spent your days using a slide rule to double-check someone else’s work. Did a computer take the engineer's job? No. Did their jobs get better, more productive, and more important? You betcha. 

Let’s do this. 

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Improving Revenue Retention in 2025

Joel Passen
November 15, 2024
5 min read

If improving revenue retention is a key priority in FY25, here is some food for thought. If you believe data is the essential foundation for improving retention, imagine the possibilities with 50-100x more data about your customers. Here’s the thing: Every business has this customer data, but 99% of businesses are sleeping on a data set that could change their business. It’s the unstructured data that’s sitting in ticketing systems, CRMs, chat systems, surveys, and the biggest silo by volume - corporate email systems. Most of us still rely on structured data like usage, click rates, and engagement logs to gauge our customers' health. However, structured data provides only a partial view of customer behavior and revenue drivers. Unstructured data—like customer emails, chats, tickets, and calls —holds the most valuable insights that, when leveraged, will significantly improve revenue outcomes.

Why Unstructured Data is Essential for Revenue GrowthImproving Customer Retention: Unstructured data helps businesses identify early warning signs of dissatisfaction, allowing them to create proactive interventions before customers churn. Repeated mentions of poor experiences, response lags, product-related frustration, and more in call transcripts, cases, and emails indicate potential churn risks. By identifying these trends while they are trending, businesses will improve retention.

Fueling Product Innovation: Let’s face it: Our customers bought a product or service. Post-sales teams don’t develop products and are limited in what they can directly impact. Product teams need more unbiased, unfiltered contextual customer data, and they need it consistently. Unstructured data provides real-time feedback on how customers use products and services. Businesses can analyze customer feedback from multiple channels to identify recurring requests and pain points. This data fuels product innovation and informs customer-led roadmaps that lead to higher engagement rates and more profound value. Developing products that directly respond to customer feedback leads to faster adoption, better advocacy, and a competitive advantage.

Identifying Expansion Opportunities: Unstructured data reveals customer needs and preferences that structured data often overlooks. Businesses can uncover untapped expansion opportunities by analyzing email, chats, and case feedback. These insights help identify additional products or services that interest customers, leading to new upsell or cross-sell possibilities. To drive immediate improvements in revenue retention, the key isn't pouring resources into complex churn algorithms, chatbots, or traditional customer success platforms—it's being more creative with the data you're already collecting. Start listening more closely to your customers, identify the patterns in their pain points, and share this knowledge with your peers who can improve your offerings. This is the year to start thinking outside of the box.

Customer Retention

Burton's Broken Zippers

Steve Hazelton
November 15, 2024
5 min read

Last year, I bought a pair of ski pants and the zipper fell out on the first chair lift. I called Burton, and they offered an exchange. New pants, first chair, same problem. Support informed me that I was required to return the pants for repair. The repairs would be completed after ski season. For the inconvenience, Burton offered me a 20% discount on my next purchase of skiwear. The next time I am in the market for skiwear that I can't wear during ski season, I will use that coupon.

I started my first business over 25 years ago. Since that day, I have lived in an almost constant state of fear that somehow, somewhere, things would get so broken that we'd treat a customer like this.

Let's be clear, no one who runs a business wants stuff like this to happen. Yet, it happens all the time.

If you run a software company, your engineering team will have usage tools and server logs to tell you when your product is "down" or running slowly. They can report which features are being used and which ones aren't. You'll learn that certain features in your product cost more to run than others, maybe because of a bad query, code, or something else. And you'll know what needs to be upgraded.

However, every time a customer contacts a business, they are "using" (or "testing") your product. If you sell ski pants, your product is ski pants, and your customer service team. If you sell software, your product is your tech and your customer service.

Yet, your customer-facing teams have very poor usage data, if any at all. Which feature of our service gets used the most (billing, success, support)? What are the common themes? Is one group working more effectively than the others? Does a team need an upgrade? 

(BTW, what costs more, your AWS bill or your payroll?)

The reason your customer-facing teams don't have usage data is because this data is "unstructured," and it is everywhere. Imagine if your engineering team needed to check 50 email inboxes, 1,000 phone recordings, a CRM, and a ticket system to get your product usage statistics. 

That's where your customer-facing teams are today. Until you can get answers from these systems as easily as an engineer can, you’ll continue to churn, annoy customers, and try to hire your way out of a retention problem. It won’t work.

AI & ML

Navigating AI Ethics

Joel Passen
October 14, 2024
5 min read

The question is no longer about whether you will use AI; it’s when. And no matter where you are on your journey, navigating the ethical implications of AI use is crucial. Ethical AI is not just a buzzword but a set of principles designed to ensure fairness, transparency, and accountability in how businesses use artificial intelligence. In the case of Sturdy, we’ve made ethical AI a core commitment. These principles guide our every move, ensuring AI benefits businesses without crossing the line into unmitigated risk.

What Is Ethical AI?

Ethical AI refers to developing and deploying AI systems that prioritize fairness, transparency, and respect for privacy. For businesses, this means using AI to make smarter decisions while ensuring that the data and technologies used do not cause harm or reinforce biases. The importance of this cannot be overstated—AI has the potential to either empower or exploit, and ethical guidelines ensure we remain on the right side of that divide.

Sturdy’s Commitment to Ethical AI

Sturdy's approach to AI revolves around several inviolable principles:

  1. Business-Only Data Use: Sturdy’s AI systems focus solely on improving how businesses make decisions. They don't delve into personal data or manipulate information for other purposes. The data processed by Sturdy comes from business sources like support tickets, corporate emails, or recorded calls—never from personal channels.
  2. No Ulterior Motives for Data: The data collected by Sturdy is knowingly provided by our customers, and the company doesn't use this data for any purpose beyond what's agreed upon. This ensures transparency and trust between the platform and its users.
  3. Privacy and Protection: One of the most critical aspects of Sturdy’s approach is its commitment to not allowing any entity—whether a business or government—to use its technology in ways that violate privacy. If a client were found to be doing so, Sturdy would terminate the relationship.
  4. No Deception: Our product is engineered to prevent deception. It never manipulates or deceives users, ensuring that the insights drawn from AI are used to enhance business practices rather than exploit loopholes.

Human Oversight and the Role of AI

At the core of Sturdy’s AI principles is the belief that AI should not replace human decision-making but augment it. Our Natural Language Classifiers (NLCs) are built to detect risks and opportunities based on the probability that a conversation indicates a particular issue. For example, when a customer complains about a "buggy" product, Sturdy’s AI might tag it as a "Bug" and label the customer as "Unhappy." However, humans remain in control—analyzing the situation and deciding the best action.

Final Thoughts

Sturdy's approach to AI exemplifies how businesses can responsibly use technology to drive growth and improve operations while safeguarding ethics. They demonstrate that AI doesn’t need to infringe on privacy or replace human decision-making. Instead, AI should be a tool that empowers teams, ensures transparency, and upholds ethical standards. Navigating the ethics of AI is not just a challenge—it’s an ongoing commitment, and Sturdy is setting a new standard for how it should be done.

How many customers will you have to lose before you try Sturdy?

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