The business world has moved past the initial hype of "What is AI?" and into the critical phase of "How do we make this work?" As the CEO of soolisAI, I see this shift every day. Companies from local SMBs to global enterprises are no longer asking if they should use AI—they are asking how to operationalize it without burning cash or compromising security.

Drawing from my experience integrating AI solutions and leveraging the robust infrastructure of our partner, Amazon Web Services (AWS), here is a deep dive into the top ten questions businesses are asking right now. This isn't just theory; this is the roadmap for turning AI from a novelty into your strongest competitive advantage.

The Top 10 Questions Businesses Are Asking About AI (and How soolisAI + AWS Answers Them)

By Tim Sebold, CEO of soolisAI

Feb 18, 2026

The conversation around Artificial Intelligence has matured rapidly. In 2024, boardrooms were filled with curiosity. Today, in 2026, they are filled with scrutiny. Business leaders are demanding accountability, security, and tangible results.

At soolisAI, we specialize in bridging the gap between cutting-edge AI capability and real-world business application. By partnering with AWS, we provide a secure, scalable foundation that answers the toughest questions facing modern organizations.

Here are the top ten questions we hear from clients, and how the soolisAI + AWS ecosystem provides the answers.

1. "Where is the tangible ROI, and how do we measure it?"

The Challenge

For too long, AI projects have languished in "Pilot Purgatory"—endless experiments that never deliver a dollar of value. CFOs are tired of vague promises about "productivity gains." They want to see reduced Customer Acquisition Costs (CAC), specific hours saved per employee, or direct revenue attribution.

The soolisAI + AWS Solution

Stop paying for potential. Start paying for production.

We use a "Ladder of Value" methodology to deploy focused Minimum Viable Products (MVPs) rather than sprawling, expensive experiments. Instead of trying to "boil the ocean" with a massive, all-encompassing AI, we target specific, high-friction workflows.

• The AWS Engine: We leverage Amazon SageMaker to track model performance and inference costs in real-time. This allows us to attribute specific dollar amounts to every AI action. For example, we can calculate the exact compute cost per resolved customer support ticket.
• The soolisAI Advantage: We translate these technical metrics into a business-first dashboard. You don't see "token usage"; you see "42% reduction in operational costs" compared to your previous manual workflow. This ensures you never pay for idle GPU capacity and can draw a straight line from investment to return.

2. "Is our data actually ready for AI?"

The Challenge

This is the most common blocker we see. Executives fear their data is too siloed, unstructured, or "dirty" to be useful. They worry that "garbage in" will inevitably lead to "garbage out," stalling projects before they even begin.

The soolisAI + AWS Solution

You don't need perfect data. You need a secure "Walled Garden" to clean it.

The idea that you need a pristine, unified data lake before starting AI is a myth. You need a system that can ingest what you have and make sense of it securely.

• The AWS Engine: Using AWS Glue and Amazon Bedrock, we can securely ingest unstructured data—PDFs, old emails, disparate spreadsheets—without that data ever leaving your secure environment. AWS guarantees that your data is not used to train their base foundation models.
• The soolisAI Advantage: We act as the cleaning and structuring layer. soolisAI indexes and "tags" your proprietary data before it hits the model. This process, known as Retrieval-Augmented Generation (RAG), ensures the AI is grounded in your truth, not internet noise. We turn your messy shared drive into a structured knowledge base.

3. "Should we focus on internal efficiency or customer-facing innovation?"

The Challenge

This is a strategic prioritization question. Should you deploy AI to cut costs (backend automation) or to drive growth (customer-facing bots)? Most organizations are terrified of the "brand risk" associated with customer-facing AI.

The soolisAI + AWS Solution

Start with "Collaborative AI" internally to build the confidence for external launch.

We advocate for an "inside-out" approach. Prove the technology works on your team before you unleash it on your customers.

• The AWS Engine: Amazon Q Business can be deployed almost instantly to allow your internal staff to search wikis, HR documents, and codebases. This provides an immediate "safe win" for efficiency.
• The soolisAI Advantage: We deploy "Human-in-the-Loop" agents. For example, an internal soolisAI bot might draft a response to a complex customer email, but a human employee reviews and sends it. This builds efficiency and safety simultaneously, allowing your team to "train" the AI on company tone and policy in a risk-free environment.

4. "How do we move from 'Chatbots' to 'Agentic AI'?"

The Challenge

The market is flooded with chatbots that can talk. Businesses need agents that can do. The shift is from Generative AI (creating text/images) to Agentic AI (systems that execute workflows).

The soolisAI + AWS Solution

We stop building 'talkers' and start building 'doers'.

• The AWS Engine: Agents for Amazon Bedrock provide the architectural framework for AI to execute API calls. This means the AI can securely connect to your backend systems to perform actions like "Refund Order https://www.google.com/search?q=%23123" or "Update CRM Record."
• The soolisAI Advantage: We configure the specific business logic and "permission sets" for these agents. It's not enough to give an AI access to your database; you need to teach it the rules of the road. soolisAI ensures the agent knows how to refund the ticket according to your specific company policy (e.g., "only refund if purchase was < 30 days ago"), ensuring compliance and preventing costly errors.

5. "How do we integrate AI with our legacy 'Spaghetti Code'?"

The Challenge

Mid-market and enterprise companies often rely on decades-old legacy software (ERPs, old CRMs). They fear that modern AI can't interface with these fragile systems without a complete, expensive rip-and-replace.

The soolisAI + AWS Solution

We don't rewrite the legacy code; we wrap it.

• The AWS Engine: AWS Lambda allows our AI agents to trigger serverless functions. These functions act as secure bridges, interacting with your legacy databases safely without requiring a full system refactor.
• The soolisAI Advantage: soolisAI acts as the API middleware. Our agents serve as the "translator" between modern natural language requests ("Show me sales from Q1") and the complex SQL queries required by your 20-year-old ERP system. This extends the life of your legacy investments while giving them a modern, AI-powered interface.

6. "What are the legal and security risks of 'Shadow AI'?"

The Challenge

Employees are already using AI—often without permission. They paste sensitive company data into free, public models to do their jobs faster, creating a massive "Shadow AI" risk where proprietary IP leaks out into the public domain.

The soolisAI + AWS Solution

Provide a better, safer internal tool so employees stop using the risky ones.

You cannot ban AI; you must provide a compliant alternative that is just as easy to use.

• The AWS Engine: Amazon Bedrock’s Data Privacy guarantee is ironclad: none of your data is used to train the base foundation models. It remains your IP, always.
• The soolisAI Advantage: We provide your team with a "soolisAI Enterprise Portal." It looks and feels like the consumer tools they love (easy chat interface) but is fully logged, monitored, and compliant with your corporate governance. We turn a security risk into a managed asset.

7. "Will this displace our workforce, or do we need to upskill?"

The Challenge

This is the cultural question that keeps leaders up at night. Do we prepare for layoffs (displacement) or invest in training (augmentation)?

The soolisAI + AWS Solution

We are building 'Bionic Employees', not replacing them.

The goal is to remove drudgery, not people. We focus on "human-in-the-loop" workflows where AI handles the repetitive tasks and humans handle the edge cases and strategy.

• The AWS Engine: Amazon Q Developer aids your technical staff, doing the heavy lifting of coding, testing, and debugging. This effectively turns junior developers into seniors by augmenting their capabilities.
• The soolisAI Advantage: We focus on Role-Based AI Agents. We don't just "give everyone AI"; we give the sales team a "Sales Prep Agent" and the HR team a "Compliance Agent." This makes upskilling intuitive because the tool is designed for their specific job, not a generic "magic box."

8. "Buy vs. Build vs. Fine-tune?"

The Challenge

Should an SMB subscribe to a SaaS tool? Should an enterprise build its own model? The options are overwhelming: Off-the-shelf (GPT-4, Claude), open-source (Llama), or custom-built?

The soolisAI + AWS Solution

Don't build the engine (Model); build the car (Application).

• The AWS Engine: AWS provides the "engines"—access to top-tier models like Claude, Llama, and Titan via Amazon Bedrock. You don't need to build a model from scratch; the best ones in the world are available via API.
• The soolisAI Advantage: We help you "rent" the outcome. soolisAI manages the complexity of selecting the right model for the right task (orchestration). We might use a cheaper, faster model for simple queries and a smarter, more expensive one for complex reasoning. This "Model Routing" ensures you get the best performance-to-cost ratio without managing the infrastructure yourself.

9. "How do we prevent brand damage from AI hallucinations?"

The Challenge

After seeing high-profile blunders—chatbots offering unauthorized discounts or swearing at customers—businesses are terrified of brand risk. They need "guardrails" to ensure the AI stays on script.

The soolisAI + AWS Solution

Guardrails are non-negotiable.

• The AWS Engine: Guardrails for Amazon Bedrock allows us to set hard blocks on specific topics. We can programmatically ensure the AI never discusses competitors, never offers financial advice, and never uses profanity.
• The soolisAI Advantage: We implement "Adversarial Testing." Before a bot goes live, soolisAI tests it against thousands of edge cases and "jailbreak" attempts to ensure the guardrails hold. We act as your brand safety officer, ensuring the AI represents your company perfectly.

10. "Is AI a competitive moat, or just table stakes?"

The Challenge

If everyone has access to the same models (Claude, GPT-4, Llama), where is the competitive advantage? Is AI just the new electricity—essential but not a differentiator?

The soolisAI + AWS Solution

The model is a commodity. Your 'orchestration' of it is the moat.

• The AWS Engine: Provides the scalable utility—compute power and model access.
• The soolisAI Advantage: We build your Custom Knowledge Graph. By combining your unique customer data with soolisAI’s industry-specific workflows (e.g., for fitness, hospitality, or finance), you create a system that only your company can possess. Competitors can buy the same AWS models, but they cannot buy your data or the soolisAI-tuned workflows that make that data actionable.

Ready to Answer These Questions for Your Business?

The shift from "AI experimentation" to "AI operationalization" is here. You don't need another generic chatbot; you need a strategic partner who understands both the technology and the business outcomes you're chasing.

At soolisAI, powered by AWS, we help you navigate these questions and build a future-proof AI strategy.

Would you like to schedule a consultation to see how these answers apply to your specific industry?

Contact soolisAI Today

FAQ: AI Strategy & Implementation

Q: How long does a typical soolisAI implementation take?

A: Unlike massive enterprise software rollouts that take years, our "Ladder of Value" approach targets an initial MVP launch in as little as 4-6 weeks.

Q: Is my data safe with soolisAI and AWS?

A: Absolutely. We utilize AWS's enterprise-grade security. Your data is encrypted in transit and at rest, and we have a strict "zero-retention" policy for model training—your data never trains a public model.

Q: Do I need a team of data scientists to manage this?

A: No. That’s our job. soolisAI provides the "AI as a Service" layer, managing the complexity of prompt engineering, model selection, and vector databases so your team can focus on business strategy.

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