Becoming an “AI-First” Company, Detecting AI Content, and More…
If you're not focused on AI yet, time is running out. Seriously.
Thanks for reading AlphaEngage issue #105. Read past issues.
Inside: AI or bust, AI literacy across the org, staying on brand when using AI to create content, and more.
EXECUTIVE
Becoming an “AI-First” Company
Most companies are still at the "AI pilot" stage—proofs of concept, a few use cases, maybe a chatbot. AI-first companies are building around it. The difference is not in the tools, but in the mindset and structure.
Being AI-first means making artificial intelligence a foundation of how the business runs, not a department, a demo, or an R&D project. That includes how decisions are made, how products evolve, and how teams operate.
From Digital-First to AI-First: What’s Different
The digital-first push centered on channels, including websites, apps, e-commerce, and mobile user experience (UX). AI-first hits at capability. It’s about how work gets done and how insights are generated. A digital-first bank moved customers online. An AI-first bank pre-approves loans based on real-time behavioral models, customizes pricing dynamically, and uses AI to coach reps in live calls.
What makes this transition harder is that AI isn’t a standalone rollout. You can’t just "go AI" like you “went mobile.” It has to be embedded into CRM systems, ERP processes, product logic, customer experiences, and compliance workflows.
Unlike digital, which is mostly additive (with more channels and faster access), AI can be subtractive. It replaces tasks, condenses teams, and forces rethinking of org charts. That comes with political and operational risk.
Where to Start
Start where AI already has a measurable impact. Three areas consistently deliver returns:
Sales and Marketing
Lead scoring, email personalization, ad targeting, and pricing optimization all benefit from AI with relatively low integration costs.
Start with tools your team already uses (like Salesforce, HubSpot, or Google Ads), and layer AI on top before rebuilding anything from scratch.
Customer Service
AI can reduce response time, increase self-service rates, and divert volume away from agents. Look for platforms that offer native GPT integrations or voice-to-text summarization.
High-volume support orgs can quickly measure ROI via handle time and CSAT metrics.
Ops and Finance
Fraud detection, demand forecasting, invoice processing, and exception management are all areas that are ripe for automation.
These areas are often data-rich, rules-based, and politically safe as a starting point.
What It Takes Internally
AI-first transformation is not a single initiative. It touches data architecture, hiring, budgeting, compliance, and culture.
Data as a product. You need unified, clean, accessible data to train reliable models. That often means building data pipelines and cleaning up fragmented systems—boring but essential.
AI literacy across roles. This doesn’t mean turning everyone into a prompt engineer. It means ensuring that teams understand what AI can and cannot do, how to work effectively with it, and how to critically evaluate its output. Keep reading to the next section for much more on this topic.
AI ownership. Someone needs to own AI strategy and integration end-to-end. Not IT alone. Not marketing alone. Ideally, a cross-functional leader or team (depending on the size of your organization) with both advanced business and technical skills.
Governance upfront. Bias, IP risk, and regulatory exposure—all of it scales with usage. If you don’t establish AI governance while the organization is still learning, you’ll pay for it in audits, lawsuits, or negative PR later.
Talent tension. Many companies try to centralize AI expertise. That’s fine to start. But AI-first companies decentralize over time, embedding capability in each business unit while platform teams support scale.
Signs You’re Becoming AI-First
Your default question shifts from “Can we try AI here?” to “Why wouldn’t we use AI here?”
You build internal benchmarks for AI impact, such as productivity per seat, cost per ticket, and conversion lift per campaign.
You stop building dashboards and start deploying decision agents that don’t just surface insights, but act on them.
AI is discussed in board meetings not as a risk or curiosity, but as a growth and margin lever.
The Cost of Waiting
Early digital adopters ate market share while others debated mobile formats. Many legacy companies tried to fast-follow and found themselves saddled with technical debt, vendor lock-in, or cultural resistance. A few never made it.
The same will happen here, except that AI advances more rapidly and its effects are more profound. This isn’t about presence; it’s about performance. Companies that miss the AI shift won’t just be behind, they’ll be structurally disadvantaged.
Digital-first was about showing up. AI-first is about dominating once you’re there.
ORGANIZATIONAL DEVELOPMENT
Making AI Literacy a Company-Wide Habit
AI adoption won’t stick unless your people know how to use it, question it, and spot risks. But AI literacy isn’t about turning everyone into prompt engineers. It’s about practical fluency: knowing what AI can and can’t do, how it impacts your workflow, and when to trust or challenge its output.
What Needs to Be Taught: The AI Literacy Framework
To make AI literacy actionable and measurable, use this framework to guide what should be taught and reinforced across the organization:
What AI Is and Is Not
Basic definitions and types of AI, such as machine learning, generative AI, and automation
Common myths and misconceptions about AI’s capabilities and limitations
How AI Works in Your Business Context
Specific examples of how AI is used in sales, marketing, customer service, operations, and finance
The difference between AI supplementing versus replacing work
Data Fundamentals
Why clean, unified, and accessible data is essential for effective AI
Basics of data privacy, security, and compliance as they relate to AI use
Prompting and Interacting with AI
How to write effective prompts for generative AI tools
How to challenge AI outputs and verify accuracy
Spotting and Handling AI Errors
Recognizing AI “hallucinations” or inaccurate information
Knowing when to escalate issues to a human or specialist
Ethical and Responsible Use
Identifying and mitigating bias in AI outputs
Following company guidelines for ethical AI use and understanding the risks of misuse
Brand and Communication Guidelines
Ensuring AI-generated content aligns with the company’s tone, style, and standards
Using brand guidelines and custom instructions with AI tools
AI Tool Adoption and Safe Experimentation
Using approved AI tools and avoiding shadow IT
Encouraging experimentation within safe, governed boundaries
Continuous Learning and Feedback
Sharing AI success stories and failures across teams
Participating in ongoing training, workshops, and peer learning sessions
Measuring and Improving AI Literacy
Understanding the benchmarks for AI competency in each role
Using self-assessments, surveys, and adoption metrics to track progress
How to Make It Happen
Ownership of AI literacy should be distributed. Form a cross-functional working group with representatives from HR, IT, business units, compliance, and an executive sponsor. This group sets learning goals, curates resources, and ensures every department gets training that’s relevant and role-specific.
Start with a baseline assessment to identify knowledge gaps. Build a curriculum using short workshops, hands-on labs, on-demand videos, and regular “AI office hours.” Encourage teams to share real use cases—successes and failures—to drive peer learning and adoption.
How to Measure Progress
Track shifts in confidence and skills with pre- and post-training surveys. Define clear role-based benchmarks. For example:
Can a marketer spot and correct AI hallucinations?
Can a finance analyst automate a routine report?
Can a customer service rep escalate an AI-generated response when needed?
Monitor adoption of approved tools and flag shadow IT as a sign of unmet needs. Highlight wins and improvements to maintain momentum.
Why It Matters
When AI literacy is integrated into onboarding, training, and daily work, adoption accelerates, mistakes decrease, and people become more creative in their use of AI. The companies that thrive with AI are the ones where everyone, not just specialists, knows how to use, question, and improve these tools together.
AI literacy isn’t a one-time initiative. It’s a habit that keeps your teams competitive as AI reshapes how work gets done.
AI IN ACTION
Smarter Support at Scale
Problem: Cintas, a major B2B services provider, needed to improve the speed and accuracy of its customer support operations. Fragmented internal systems and manual searches hindered representatives, who had to navigate through documents, contracts, and product data, which impacted both productivity and the customer experience.
AI Solution: Cintas partnered with Google Cloud to build an internal knowledge center powered by generative AI and Vertex AI Search. The system utilizes advanced search and summarization to instantly surface relevant information from across the company’s document base. AI-powered chatbots and virtual assistants also handle routine inquiries and recommend next steps based on customer data and history.
Results: According to research cited by Cintas, the solution has improved agent productivity by 14% and dramatically reduced the time needed to resolve customer issues. Support teams now have centralized access to real-time, accurate data, reducing the need for swivel-chair work and enabling faster, more personalized responses. The same AI engine also supports backend operations, such as inventory and logistics.
MARKETING
Detecting AI Content
Unless you have a strict policy on AI/LLM usage across your company (and you should have a policy), chances are your marketing team is using AI to generate copy. That’s not a bad thing, and AI is only getting better. But AI should supplement the content generation process, not replace it.
At least not yet.
Right now, AI copy is fairly easy to spot if you know what to look for, even without using the relatively new and only partially reliable online AI copy detection tools.
Just the Fact, Ma'am.
The most obvious check is for accuracy. If your marketers are getting basic facts incorrect, it might be time for a more comprehensive approach. AI is wrong. A lot. We call it hallucination, and there are different forms of it. The AI will often combine multiple facts into a single statement that isn’t true in context. And context is very important.
One common mistake is when the marketer asks the chatbot for an example of something—a case study or use case, for example. The LLM will then generate an example that may or may not be accurate. Or it may be partially true. It was only asked to give an “example,” and it did.
This is why prompt engineering is so critical. More on that in a later issue.
AI must be challenged to provide verifiable sources, and those sources need to be verified by a human (for now). Reddit, Quora, and even major news outlets are not always reliable sources of information. They’re all authored by regular people with opinions, biases, and inaccuracies. AI doesn’t know how to deal with this very well yet, even when it cross-checks facts on its own or because you prompted it to with a challenge.
Who Says That?!?
Another telltale sign that AI contributed significantly to a piece is the use of certain words and phrases. And there are a lot of ‘em!
People often don’t communicate or write in the same way AI does. It’s especially obvious when you’re already familiar with the marketer’s writing style and tone and, suddenly, they’re cranking out loads of content with big words at a cadence that isn’t realistic.
Here are some words that AI seems to adore:
Delve / Delve into
Pivotal
Realm
Harness
Illuminate
Comprehensive
Vital
Robust
Tapestry
Catalyst
Paradigm
Framework
Landscape
Beacon
Interplay
Navigate / Navigating
Paramount
Foster
Convey
Optimize
Elevate
Implement
Transform
Develop
Analyze
Improve
Meticulous
Complexities
Cutting-edge
Adhere
Excels
Embark
Vibrant
Objective
Form
Placeholder
Grammar
Discover
Explore
Engage
Notably
Undoubtedly
Consequently
Crucial
Dive
Furthermore
Moreover
And some phrases:
It is important to note that...
In today's digital age...
In conclusion...
Research needed to understand...
Despite facing...
Expressed excitement...
An objective study aimed...
By [doing something]...
Not only... but also...
In light of this...
To sum up...
It is worth mentioning that...
At its core...
Let’s delve into...
This study underscores...
Plays a pivotal role...
Aims to illuminate...
Today’s fast-paced world...
How about some stylistic patterns that AI uses too often?
Excessive use of transition words: Moreover, Furthermore, Therefore, Consequently
Repetitive phrasing and sentence structures, especially in introductions and conclusions
Overly formal, polished, or stiff tone, lacking natural variation
Excessive buzzwords and jargon
Sentences that are grammatically perfect but feel unnatural or rigid
Placeholder text (e.g., "Insert name here") left in the content
Finally, metaphors and descriptors that normal people don’t use much:
Tapestry (to describe complexity)
Beacon (to describe leadership or influence)
Landscape (to describe a field or domain)
Cutting-edge (to describe innovation)
Navigating complexities (to describe problem-solving)
Oh, and in a recent update to ChatGPT, OpenAI decided that we needed more em dashes—yeah, that thing. I use these a lot. Probably too much. But now I can’t. Thanks, ChatGPT!😞
Inform your marketing team, or whoever is utilizing AI to create content that represents the company, to ensure they adhere to brand guidelines, which should include tone, and verify that the content generated by AI is 100% accurate and truthful. It’s kind of important.
Most LLMs now allow the user to add custom instructions with file attachments. By uploading brand guidelines and instructing the AI to use a specific tone and writing style, its responses will be more closely aligned with your brand.
It is essential to note that file uploads within custom instructions are not typically automatically referenced during a chat unless explicitly instructed to do so. Adding files to a chat thread (as opposed to uploading them to a parent project folder or custom instructions set) is always preferable to ensure those files are referenced during the session.