AI Strategies to Grow Your Business | Featured in Forbes | AI Consulting, Courses & Keynotes ➤ @theashleygross

Helping Businesses Grow with AI | AI Teacher, Speaker & Consultant | Executive Workshops Host

Before tools like ChatGPT, I was already implementing AI strategies in large organizations. Within just three months of rolling out AI solutions, we surpassed our annual sales pipeline goal of $90 million, reaching $115 million.

I created the AI Impact Alignment™ process to ensure AI projects directly support business goals. This method has led to real results, like revenue growth, quicker decisions, and more efficient operations.

In addition to consulting, I offer keynote speaking, executive workshops, teach how AI can transform businesses and help them succeed.

Consulting Details:

• Implement an AI strategy to grow your business
• Align AI projects with clear business outcomes
• Teach and advise leaders on how to use AI for long-term success
• Create and deploy AI Agents to optimize your Marketing Operations

Want to know how AI can help your business grow? 

Contact Details

partnerships@theashleygross.com (for brand sponsorship inquiries)

consulting@aiworkforcealliance.com (for AI Strategy consults, building AI Agents, & customized business process automations)

speaking@aiworkforcealliance.com (book me at your next conference, company offsite or university)

A reminder that has nothing to do with AI 💜

You can't say yes to every opportunity and keep your sanity. 

Know what season you're in with your health and family. Then move career opportunities around that, not the other way around.

The question isn't "Can I do this?" It's "Should we do this right now?"
Not just you. Your whole family. 

I've turned down deals because the timing was wrong. I've also said yes to things that nearly broke me because I didn't check in first.

What's keeping you sane right now? I'd love to know what's working for you.

Ashley Gross

Happy Thanksgiving!

If you're reading this while debating whether to order takeout: do it. 

Your kid isn't going to therapy in 20 years talking about the store-bought mashed potatoes.

Order the takeout. Enjoy your people.

What Are the Strategies for Deploying AI Agents at Scale?

(And why scaling too fast can backfire.)

Many teams succeed in building one AI agent. Few succeed in scaling ten.
The difference is not the model, it is the strategy behind deployment.

1. Standardize the framework.
↳ Companies like PwC use systems such as Agent OS to connect AI agents built on Anthropic, Azure, and Google Cloud. Standardization keeps data and tasks aligned across platforms.

2. Build clear handoffs.
↳ AI agents should know when to pass work to humans or other systems. Without defined triggers, scaled operations can easily collapse into confusion.

3. Centralize learning.
↳ Each agent’s performance data should feed into a shared “brain.” This allows global updates without retraining every instance separately.

4. Start small, scale smart.
↳ Deploy in one department, refine the workflow, then replicate. Scaling success is about precision, not speed.

Scaling AI agents is not about volume. It is about coordination.

When teams connect agents under one ecosystem, efficiency compounds instead of collapsing.

What do you think is the hardest part of scaling AI agents: technology, culture, or structure?

___________________________
AI Consultant, Course Creator & Keynote Speaker
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Ashley Gross, CEO of AI Workforce Alliance, poses confidently in a checkered blazer against a plain background. The graphic text reads, “How to Deploy AI Agents at Scale (Without Breaking Your System)”

The Hidden Challenges of Debugging AI Agents

(And why fixing one issue can break three others)

Building AI agents is exciting until something stops working. And debugging them isn’t like debugging traditional code.

Here are the most common issues I see in AI agent development:

1. Ambiguous prompts
↳ Vague instructions lead to unpredictable outputs that ripple across tasks.

2. Broken context chains
↳ When memory resets, agents lose track of progress, and fixing one link can disrupt another.

3. API or integration drift
↳ A single update in a connected system can quietly break multiple dependencies.

4. Misaligned objectives
↳ If an agent optimizes for the wrong outcome, every fix creates new side effects.

Debugging AI isn’t linear.

Each patch can shift how the agent learns, remembers, and interacts.

That’s why observability and structured testing are key. You’re not just fixing code, you’re stabilizing behavior.

What’s the trickiest AI bug you’ve had to untangle?

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Ashley Gross, CEO of AI Workforce Alliance, smiling confidently by a window. The image features a quote that reads, “AI Debugging Isn’t What You Think. Small fixes often create big surprises.

The Ethics Behind AI Decision-Making

(When AI makes choices, whose values does it reflect?)

AI is making more decisions across businesses than ever before, from automated hiring tools to personalized customer recommendations. 

But every AI decision reflects the values built into it, intentionally or not.

Here is what this means for businesses:

1. Customer trust is on the line. 
→ If AI decisions feel biased or unfair, loyalty suffers and regulatory scrutiny increases.

2. Brand integrity matters. 
→ Your AI systems should act consistently with your company’s values or risk undermining your reputation.

3. Transparency reduces risk. 
→ Documenting how AI makes decisions protects your business and helps teams intervene when needed.

4. Operational efficiency depends on alignment. 
→ Misaligned AI can create extra work, human overrides, or costly errors.

Ethical AI is not just a moral discussion, it is a strategic imperative. 

The choices your AI makes today can shape customer perception, employee engagement, and long-term growth.

How is your business ensuring the values reflected in its AI align with your mission?
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How AI Is Changing the Way We Travel

(And what it means for business leaders)

AI isn’t just transforming travel, it’s transforming how the business of travel operates.

→ Hotels use AI agents to adjust pricing dynamically.

→ Airlines use predictive models to reduce delays.

→ Travel platforms use machine learning to tailor offers to each user.

But the real opportunity isn’t automation. It’s intelligence.

Executives who adopt AI in their travel operations aren’t just improving efficiency, they’re unlocking new decision-making power:

→ Predicting market shifts before they happen

→ Reallocating resources based on real-time traveler behavior

→ Turning raw data into smarter, faster business moves

That’s what AI enables: systems that don’t just react to change, but anticipate it.

The future of travel will be led by leaders who understand that innovation in AI isn’t about replacing people, but amplifying business judgment at scale.

How do you see AI transforming the next wave of travel innovation?

___________________________
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Ashley Gross, CEO of AI Workforce Alliance, smile confidently in a checkered blazer with a window behind her. The graphic text reads, “AI is Changing the Way We Travel (And what it means for business leaders)”

Interviewer: "How do you balance being a mom and a founder?"

Me: Finds a photo that accurately depicts what balance means to me. 📸 

Eating two ice cream cones because I couldn't decide on the flavor and didn't want to settle.

This is my balance.
The lines between work and personal are blurred. Work happens 24 hours a day with personal life woven throughout. 

My average workday is 16-18 hours. I start at 3 AM every single day.

There is no balance. There's just trying your best, ruthless prioritization, and hiring people who can manage the day-to-day so you can think big picture.
And sometimes, eating two ice cream cones at once.

What’s the Future of Human-AI Collaboration?

(And how it could transform the way we work.)

Humans and AI are moving beyond automation into collaboration. Research shows that hybrid systems, where human intuition meets machine scale are not just possible, they’re already emerging. 

This shift isn’t simply “AI taking over” but a new workflow:

• AI handles data-heavy, repeatable tasks at speed
• Humans provide judgment, creativity and context

Together they form a partnership that neither could achieve alone.

What this means for work:

1. Increased agility: 
↳ AI lets teams test hypotheses and scale experiments faster.

2. Smarter decisions: 
↳ Humans use AI’s insights, but steer the direction.

3. New roles: 
↳ We’ll see more “AI Collaborators,” “Workflow Architects,” and “Data Empathy Specialists.”

But there are real challenges:

Trust in AI remains a hurdle. Teams hesitating to rely on AI lose its advantage.

Without governance, hybrid systems can amplify bias or faulty data.

Skills gaps matter. Collaboration only works if people know how to partner with AI.

How do you imagine humans and AI dividing work in the next 5 years?
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How do I experiment with AI tools WITHOUT destroying my productivity and time?

Here's my rule: 

Even if I love my current tech stack, when I want to test something new, I give myself 30 minutes max.

If I can't see measurable success in that window—or if the tool is so complicated I can't figure out the basics—it's not worth the next level of experimentation.

These tools are incredible. But they can become the biggest time suck if you're not ruthless about protecting your focus.

Don't chase shiny objects. Experiment strategically. If it doesn't prove value fast, move on.

Your productivity > keeping up with every new AI release.

I hope everyone's weekend was as restful as Spider-Man's was. 😴

If Daylight Saving Time wrecked your sleep OR keeping up with the never
ending flood of AI tool updates is exhausting you... I've got something for you.

I'm giving away my complete AI Tool Directory - over 500 AI tools I've already vetted so you don't have to. 

Each one includes:
• GDPR compliance status
• Country-specific usage notes
• Best use case recommendations
• My personal safety score rating

This is for teams, consultants, and business leaders who are tired of wading through AI hype and just want to know what actually works (and is safe to use).

Want it? Send me a DM telling me what your Halloween costume was. 

Bonus points for photos. 🎃

What’s Missing From Your AI?

(Hint: It’s not more data, it’s more humans.)

A dysfunctional AI agent isn’t one that “doesn’t work.” It’s one that lacks context.

That’s where humans come in.

We bring what AI can’t: nuance, troubleshooting, and emotional intelligence.

When I build agents, I don’t wait until the end to get feedback. 

I set milestones, loop in stakeholders early, and pressure-test at each stage. Because the best AI builds are co-created, not just coded.

The smartest systems still need the smartest oversight, and that’s us.

What do you think: are we involving humans enough in today’s AI builds?

___________________________
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How Smarter Infrastructure Powers Global Growth

(The $1B ARR milestone is just the beginning)

Scaling globally isn’t just about moving money. It’s about removing friction so teams can focus on strategy, innovation, and results. 

That’s what Airwallex enables.

Think of it like this: AI automates repetitive work so humans can focus on higher-impact decisions. 

→ Spend: AI extracts receipt data, auto‑categorises, and matches receipts to transactions, enforcing policy and speeding month‑end close.

→ Payments: An AI optimisation engine improves authorisation with smart routing, decline‑aware retries, fraud scoring, and dynamic 3‑D Secure to lift success and lower costs. 

→ Customer experience: Their conversion-optimized prebuilt payment form uses AI to adapt to customer preferences, predict payment method choices, and minimize friction in the checkout process.

Here’s why executives should pay attention:

→ Scale matters: $1B in ARR 

→ Efficiency matters: Legacy systems like SWIFT are decades old and full of friction, modern infrastructure removes that bottleneck.

For leaders, your company’s financial infrastructure can be a growth engine, not a constraint. The right systems let you move fast, operate globally, and focus on what really drives value.

Airwallex isn’t just changing payments, it’s redefining what operational excellence looks like for modern businesses.

How are you preparing your financial infrastructure to scale without friction?

#AirwallexPartner

Learn More: https://lnkd.in/e6k7JgVT

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Ashley Gross, CEO of AI Workforce Alliance, with long brown hair smiles confidently in a corridor. Text reads, "The New Standard of Global Finance, Powered by Airwallex." with social media icons below and a hashtag "Airwallex Partner"

How I Turn Raw Data Into Strategic Decisions in Minutes

(Without waiting on data teams or writing a single line of code)

Data analysis doesn’t have to take hours, or require a team of specialists.

With Julius AI, I can:

→ Upload any dataset and ask questions in plain English

→ Generate insights and visualizations instantly through Threads

→ Automate repeatable workflows with Notebooks for faster, consistent analysis

→ Collaborate in real time with my team to eliminate data silos

Julius turns messy spreadsheets and complex datasets into presentation-ready visuals, predictive models, and actionable insights, all without coding.

For executives, that means:

→ Faster, data-backed decisions without waiting on analysts

→ More time acting on insights instead of cleaning data

→ Teams empowered to explore data independently

The result? A smarter, faster, and more strategic approach to analytics so you can focus on growth instead of wrangling spreadsheets.

And now with Julius Slack Agent, I can access insights and share reports directly in Slack, keeping my entire team aligned without ever leaving the workspace.

How are you cutting hours off your data analysis workflows?

With Julius, I don’t just analyze data, I turn it into decisions that move the business forward.

👉 Learn more here: https://bit.ly/4hO3kuG

Ashley Gross, CEO of AI Workforce Alliance, in a black blazer stands confidently against an urban background. Text reads, "What If You Could Turn Any Dataset Into a Decision in Minutes? (Julius AI makes that possible)" with social media icons and a hashtag "JuliusAIPartner"

How to Balance Rapid AI Adoption with Risk Management

(Without slowing innovation down)

Every company wants to move fast with AI.

But moving fast without control is not innovation, it is exposure.

Here is what actually works:

1. Define clear ownership
Every AI project needs a human decision-maker not just technically but ethically.

2. Start with low-risk pilots
Automate internal workflows before touching customer-facing systems.

3. Build feedback loops
Models drift. Context changes. Risk control is not static, it is ongoing learning.

4. Standardize handoffs
AI should assist, not confuse. Define where machine insight ends and human judgment begins.

5. Document decisions
Transparency is not bureaucracy, it is your defense when things go wrong.

Move fast but with awareness.

Experiment boldly but document everything.

And most importantly, design systems where humans remain accountable for outcomes.

The balance isn’t between speed and safety, it’s between experimentation and accountability.

How is your team managing the trade-off between innovation and control?
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Who coined the term “Artificial Intelligence”?

(Hint: It happened long before ChatGPT.)

The term Artificial Intelligence was coined in 1956 by John McCarthy, a computer scientist at Dartmouth College.

He organized the first AI conference, believing machines could simulate human intelligence.

That one idea sparked everything. From expert systems in the 80s to generative AI today.

Now, nearly 70 years later, we’re finally seeing his vision take shape in ways even he couldn’t have imagined.

Here’s a fun challenge:
→ What’s one AI fact, story, or piece of trivia you think more people should know?

Drop it in the comments so we can all learn something new today!
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Ashley Gross, CEO of AI Workforce Alliance, wearing a brown cardigan with a white top, smiling with arms crossed in a plain background. Text below her reads, “Artificial Intelligence Started in 1956 (can you guess who coined it?)”

Burnt out on building workflows from scratch? This one's pre-baked.

My Anam assistant just booked a sales call, updated my CRM, and emailed me the details.

While I was sleeping.

I connected Anam.ai's virtual avatar to my website — now when someone clicks "Talk to our assistant," they're chatting with an AI that can actually take action.

3 AM inquiry → Lead in CRM → Meeting on my calendar → Full transcript in my inbox.

Zero clicks from me.

Drop your favorite Thanksgiving dish in the comments and I'll send you this already-done-for-you Anam.ai recipe. 🦃

The Most Overlooked Part of Building With AI

(It’s not the tech, it’s the understanding.)

When clients come to me, it’s often the first time they’re hearing what it really takes to build great technology.

Not the hype. Not the “AI in 5 minutes” clickbait.

But the real work, documenting, communicating, and aligning across teams before anything gets built.

Most people think AI projects fail because of bad models or weak data.

But in my experience, the real challenge is mass enablement.

It’s helping people understand what’s changing, how it affects other teams, and why clear communication matters.

Building AI isn’t just a technical shift.

It’s emotional.

It’s change management.

And that’s the part too many skip until misunderstandings slow everything down.
___________________________
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Look, I get it. You've been meaning to upskill on AI. You've bookmarked 47 articles. You've watched the explainer videos. But you still don't know how to actually USE it in your day-to-day work.

That ends today.

For the next 14 days, when you enroll in any AI Workforce Academy course, you get a second enrollment FREE to gift to a colleague, team member, or that person who keeps asking you "but how do I actually use ChatGPT?"

This is the same practical AI training I've delivered to 2,500+ professionals at Fortune 500 companies.

Here's what you actually learn: 
→ Building automations that save you hours daily 
→ Creating AI agents that handle repetitive work 
→ Implementing AI in your existing workflows (no fancy tech stack required) 
→ Real use cases you can deploy Monday morning

I built this training the same way I built my consulting business: by showing, not telling. 

Why buy one, gift one?

Because AI adoption doesn't happen in isolation. 

When your whole team speaks the same language and uses the same frameworks, that's when transformation actually happens.

The promo ends December 10th  at midnight. 😃

Between you, me, and AI I'm sure we can figure it out." — my husband on Thanksgiving morning.

We did not figure it out.

AI told me to pat the turkey, compliment it, remove things I'd rather not discuss, and "spatchcock" it.

I switched tools. Asked for a gentler approach. Got told to pretend I'm a chiropractor.

Here's the thing..AI is only as useful as your ability to work with it. 

Wrong tool, wrong prompt, wrong approach? You're ordering DoorDash.

We got takeout. Happy holidays.

P.S Can anyone actually confirm if they even like turkey?

AI doesn't fix broken systems. It magnifies them. That small tech stack issue you've been ignoring? AI just made it impossible to miss 👇

Before you add AI to your tech stack, ask: 
✓ Do my tools actually talk to each other? 
✓ Is my data clean and accessible? 
✓ Can I trust my current workflows?

AI will enhance whatever you give it, good systems or broken ones. 
Fix the foundation first, or AI will just amplify the chaos.

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