In This Blog
- TL;DR
- The five most common pitfalls to avoid
- How to define a smart, scalable AI strategy
- Why data quality and governance matter
- Security and compliance considerations
- How Emergent helps companies build AI that works
- Advice for companies launching their first AI project
- FAQ: Five questions we hear from business leaders
Why AI Initiatives Often Fail
Every week, I meet with organizations that are eager to harness AI but unsure where to begin. Most have the right intentions, talented teams, and powerful tools, yet many still struggle to get meaningful results from their first AI projects.
After working with dozens of clients across industries, I’ve noticed a clear pattern: the same five pitfalls appear again and again. These challenges don’t just slow progress, they can derail promising projects entirely.
If you’re planning your first AI initiative, here are the five most common mistakes to avoid, and what to do instead.
1: Trying to Boil the Ocean
The first mistake I see is scope. Many organizations start by saying, “We want to implement AI across the business.” That’s a noble goal, but it’s also one of the fastest ways to fail.
AI isn’t a single technology or product; it’s a collection of tools and methods that need to be applied to specific problems. When teams set out to “use AI everywhere,” they often end up with unfocused objectives, scattered investments, and few measurable results.
The key is to start small and stay focused. I always encourage organizations to identify a single process or workflow where AI can make a real difference, something tangible and measurable, like automating a manual reporting task or improving response accuracy in customer service.
Focused objectives enable early wins, and those early wins matter. They build confidence, demonstrate ROI, and create momentum. Once you’ve proven success in one area, expanding becomes much easier.
In other words, don’t try to boil the ocean. Solve one meaningful problem, learn from it, and scale from there.
2: Waiting for Perfection Instead of Iterating
Perfection is the enemy of progress – a reminder I give every client I work with.
Too often, teams delay rollout until their AI solution feels “ready.” They want 100% accuracy, the model trained perfectly, the workflows refined, and the interfaces polished. But the truth is, innovation rarely happens in perfect conditions.
AI projects thrive on iteration. Even an 80% effective agent can deliver measurable value and valuable feedback to guide improvement. The organizations that succeed are the ones willing to release early, test, break things, learn, and refine continuously.
I’ve seen this firsthand. A client once spent weeks fine-tuning the instructions to their declarative agent and organizing their data just so, believing that better accuracy would unlock immediate value. But when we finally launched the first iteration, we discovered that the real bottleneck wasn’t accuracy, it was adoption: users didn’t understand the tool and others didn’t believe its use case valuable.
By shifting to an iterative, sprint-based approach based on Agile development methodology, we were able to make small updates, launch them quickly to get feedback (which was then incorporated), and build trust step by step. Within weeks, adoption soared.
Progress beats perfection every time when developing AI.
3: Overlooking Data Quality and Governance
Data is the foundation of every AI solution, and its quality often determines success or failure.
“Garbage in, garbage out” may sound cliché, but I see it play out constantly. A brilliant AI model built on inconsistent, incomplete, or poorly structured data will always struggle to produce reliable results.
One common issue is feeding too much unorganized data into large language models (LLMs). Every model has a limited “context window,” meaning it can only reason through a certain amount of information at once.
For example, we recently worked with a vehicle manufacturer that wanted AI to analyze thousands of pages of service manuals. At first, the model struggled with long documents, producing incomplete or inconsistent answers. Our solution was simple but effective: we segmented the thousand-page manuals into smaller “chunks” of about 100 pages each and developed a custom hierarchy for loading and storing them. Suddenly, accuracy jumped dramatically, nearly perfect responses without retraining the model.
The lesson: well-structured data performs better than massive, unorganized data sets.
Another frequent challenge is inconsistency: say, customer addresses stored in different formats or missing metadata in product catalogs. These inconsistencies cause AI systems to misclassify, misunderstand, or overlook key details.
We address this through strong data governance practices: master data management, validation rules, and layered “medallion” architectures that clean and structure data before it reaches the AI layer.
Good data improves model performance while helping users see accurate, consistent results so they are far more likely to trust and embrace AI tools as part of daily operations.
4: Ignoring Security and Compliance Considerations
AI doesn’t necessarily create new risks, but it does reveal the ones already hiding in your data.
Take Microsoft Copilot, for example. It can retrieve and summarize information from across your organization in seconds. That’s powerful, but it can also expose sensitive or confidential data if access controls aren’t in place. Copilot does automatically respect existing file/folder permissions and won’t surface information from files that a user doesn’t have rights to, but it’s also an extremely powerful search tool.
I’ve imagined situations where employees could suddenly surface payroll data or private HR files... not because AI was “hacking” anything, but because those files were stored in open locations for which folder lockdown permissions or confidentiality tags weren’t correctly applied.
That’s why governance and security must be part of the conversation. Before and while deploying AI, every organization should review:
- Access control: Who can use AI tools, and what data can they reach?
- Data classification: Is sensitive information properly labeled and restricted?
- Data loss prevention (DLP): Are there guardrails to detect and block inappropriate data sharing?
Microsoft Purview is an excellent governance tool for maintaining visibility and control over data estates and can provide great risk controls against situations like I described above with Copilot and data in a Microsoft environment. On the other hand, public, consumer-grade AI tools often lack these protections and security tool integrations, and should in most cases be avoided for enterprise use.
Education is equally critical. Employees need to understand why governance matters, not just what the rules are. When people know the boundaries and rationale, they’re more likely to use AI responsibly.
Strong governance isn’t about slowing progress; it’s about creating a safe foundation that allows innovation to thrive.
5: Treating AI as a Silver Bullet Instead of a Complementary Tool
AI is a powerful enabler, but it’s not a magic wand. I often tell clients that AI should complement your people and processes, not replace them.
The organizations that get the most value from AI are those that integrate it as one piece of a larger ecosystem. They pair AI with automation, analytics, and human expertise to create better outcomes than any single component could achieve alone.
For instance, using AI to generate insights is valuable, but those insights still need context. Humans interpret, challenge, and apply them with judgment and empathy. That’s where the real transformation happens.
This holistic mindset is exactly what we help organizations build at Emergent.
How Emergent Helps Companies Build AI That Works
At Emergent, we start with the business problem rather than the technology.
Our AI Strategy Workshops are designed to bring clarity and alignment before any development begins. In these sessions, we collaborate with business and technical leaders to:
- Define a clear vision and purpose for AI
- Evaluate and prioritize potential use cases
- Assess value, risk, and adoption factors
- Develop a roadmap for pilot projects and scaling
Most organizations walk away with 10–15 well-scored use cases and a prioritized roadmap of what to build first.
From there, we help clients move from strategy to execution through three key pathways:
- Microsoft 365 Copilot: We guide teams through enablement, licensing, and adoption to deliver early wins with familiar tools.
- Copilot Studio / Power Platform: These environments empower business users to build low-code AI agents and automate repetitive workflows safely.
- Azure AI Foundry: For custom, enterprise-grade solutions, Azure AI Foundry provides the foundation for intelligent document processing, chatbots, and cognitive services.
This tiered approach lets organizations start small, experiment safely, and scale confidently without overcommitting resources or exposing risk too early.
TL;DR
AI is transforming business operations, but early missteps can derail progress. The five most common pitfalls I see are:
- Trying to solve everything at once instead of starting small
- Waiting for perfection instead of iterating
- Overlooking data quality and governance
- Ignoring security and compliance
- Treating AI as a silver bullet rather than a complementary tool
The organizations that succeed are those that combine clear goals, clean data, and a willingness to learn.
At Emergent, we help companies do exactly that. From defining vision and strategy to building practical solutions with Copilot and Azure AI Foundry.
FAQ: Five Questions I Hear Most Often
1. What’s the first thing I should do before launching AI in my organization?
Start with strategy. Define what success looks like in business outcomes. Ask: What problem are we trying to solve, and how will we measure success?
If you can’t answer those questions, you’re not ready to build. In our workshops, we work with clients to define metrics like time savings, customer satisfaction, or cost reduction. Having a clear definition of value keeps AI projects grounded and measurable.
2. How do I know which business problems AI can solve?
AI is best suited for repetitive, data-heavy, or decision-intensive processes. For example, customer support automation, data classification, or document summarization.
We use a structured framework, ROI (Return on Investment), ROE (Return on Experience), and Competitive Advantage, to score use cases. This ensures that each project not only makes technical sense but aligns with real business goals.
3. What are the biggest data challenges for AI?
Poor data quality and inconsistency are the biggest roadblocks. Large, messy, or unstructured data sets can confuse models and produce unreliable outputs.
Invest early in master data management, metadata tagging, and validation processes. Even small improvements in data quality can significantly improve model accuracy and performance.
4. How do I manage security and compliance risks?
Strong governance is nonnegotiable. That starts with enterprise-grade tools that include features like data classification, access control, and DLP.
Equally important is user education. Employees should understand what’s considered sensitive, what’s authorized, and how to handle AI responsibly. I always recommend pairing governance policies with clear communication and training, because people are your first line of defense.
5. Should I start with custom AI development or off-the-shelf tools?
Start where you can create value fastest. Most organizations begin with Microsoft 365 Copilot or Power Platform because they’re familiar, secure, and quick to implement. Once you’ve built confidence and identified where AI adds the most value, you can expand into custom solutions using Azure AI Foundry.
This phased approach minimizes risk and accelerates learning, helping you scale with clarity and control.
