Ashar Samdani: CEO of Code District, a Washington-based software development firm that focuses on helping businesses incorporate modern tech
Plenty has been said, written and preached about when it comes to putting AI at the core of digital transformation. And most businesses agree. According to Gartner, 91% of organizations are pursuing “some form of digital initiative,” and 87% of senior leaders say digitalization is of top importance.
But as with any transformation effort—where only 30% of initiatives are considered successful—the challenge is execution, not intent. The difference now is that the economics of getting it right have fundamentally changed.
The biggest shift with AI-first transformation is that it’s no longer a slow, costly endeavor. Thanks to advances in generative AI and AI agents, efforts that once required months of manual work and tens of millions of dollars can now be achieved in a fraction of the time and cost.
AI-first transformation is not just cheaper and faster—it’s more aligned, more actionable and more scalable. But to harness it fully, a structured approach is essential. This article will walk you through practical steps to successfully make AI the center of your digital transformation.
1. Start With The Problem, Not The Technology
One of the most common missteps in AI-led digital transformation is starting with a tool and then looking for a reason to use it. The allure of emerging technologies like generative AI, machine learning and autonomous automation like AI agents can tempt organizations to dive in without a clear understanding of what problem they’re trying to solve.
But transformation should never begin with a product pitch. It should begin with a pain point.
To drive real impact, leaders should identify high-friction areas in the business: where time is wasted, customer experiences suffer or operational inefficiencies linger. These pain points are the foundation of a meaningful AI transformation initiative.
I find the best AI use cases are:
• Business-critical: Tied to revenue, operations or customer satisfaction
• Quantifiable: Capable of demonstrating measurable ROI
• Scalable: Useful beyond one department
• Achievable: Deliver value without taking years to implement
2. Think Big, Start Small
The goal of AI-first digital transformation is not to deploy AI everywhere all at once. It’s to find that one use case that could fundamentally improve the business if scaled.
Look for high-value opportunities that:
• Address a strategic challenge or unlock a new opportunity
• Can be tested quickly in a low-risk environment
• Offer clear metrics to track impact
The objective isn’t experimentation for its own sake. It’s about creating a blueprint that can be scaled across the enterprise.
3. Analyze And Prepare Your Data
Data is the foundation of AI—but not just any data. For AI systems to be effective, the data must be relevant, structured and trustworthy.
I recommend you start with comprehensive data collection across departments: CRM systems, transaction records, web analytics, ERP systems, support tickets and external data feeds. Then move to cleaning: Remove duplicates, correct errors, resolve inconsistencies and handle missing values. Clean data not only improves model performance but also builds trust across stakeholders.
Once cleaned, explore and profile the data to detect patterns, outliers and anomalies. This exploratory analysis helps lay the groundwork for effective feature engineering—an essential process to train your AI models.
4. Choose And Develop The Right AI Model
Once your data is structured and ready, the next step is selecting the right model. Not all models are created equal—and the right choice depends on the nature of the problem and the data at hand. For instance:
• Predictive maintenance in manufacturing might rely on time-series forecasting or regression.
• E-commerce personalization may benefit more from collaborative filtering or recommendation engines.
• Document processing may be best tackled using NLP models fine-tuned for domain-specific language.
This is also where technical strategy intersects with business goals. Choosing the wrong model can mean poor predictions, slow performance and wasted resources.
5. Choose The Right Approach
There’s no one-size-fits-all AI strategy. Businesses must choose between off-the-shelf tools, low-code platforms, custom-built solutions or hybrid approaches.
Off-the-shelf AI tools can be appealing because they are fast to implement and cost-effective, though they typically offer limited customization options. In contrast, low-code and no-code platforms enable faster deployment while providing a moderate degree of flexibility.
For those seeking maximum control and long-term value, I think custom solutions are ideal—especially when paired with proprietary data to create a true competitive advantage. However, hybrid models are also gaining traction, as they combine the strengths of both pre-built and custom approaches.
A pragmatic approach is to follow the 80/20 rule: Leverage an off-the-shelf or low-code solution that fulfills around 80% of your needs with minimal tailoring, then customize the remaining 20% to address your unique workflows or differentiators.
When selecting a solution, consider:
• Integration with existing systems
• Long-term scalability
• Security and compliance requirements
• User experience and ease of adoption
• Cost versus long-term ROI
6. Choose The Right Partner
Technology is only half the equation. The other half is the team that helps implement it. Selecting a vendor or development partner who understands your business context is critical. I recommend you look for partners who:
• Have proven experience in your industry
• Offer robust support and training
• Demonstrate flexibility and technical expertise
• Have a transparent road map and realistic timelines
Avoid red flags like hidden fees, poor support or overpromising on capabilities and deliveries.
7. Define Success Up Front
Without clear metrics, AI initiatives often suffer from scope creep and unclear ROI. Before launching any AI initiative, define what success looks like.
This could mean improvements in operational efficiency, such as time saved, reduced errors or increased throughput. Financial impact is another key indicator, whether through cost savings, revenue growth or margin improvements. Finally, customer outcomes should be considered, including higher satisfaction scores, improved net promoter scores (NPS) and stronger retention rates.
The future of digital transformation isn’t just digital—it’s intelligent. AI-first transformation holds the potential to reduce costs, accelerate innovation and transform decision making. But only when approached strategically.
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