Key Takeaways:
- Adopt a Data First approach for AI success: Investing in AI without quality data and validation leads to poor outcomes.
- AI integration must align with real business workflows: Avoid shallow use cases—focus on practical, high-ROI implementations.
- AI-assisted Data Quality accelerates transformation: Syniti's AI-enabled engine helps launch robust data programs in days.
- High-quality data is the foundation of effective AI: Clean, reliable data sets the stage for scalable AI innovations.
Enterprises are rushing to invest in AI, either with the hope of gaining an early advantage or the fear of being left behind. Both possibilities are legitimate drivers, however, history has shown time and again that investments made without experimentation and validation fail to produce the outcomes enterprises expect. Some companies fall into the trap of disillusionment and burn out resulting in them missing out on the amazing value that AI is able to create. We are only at the start of this journey, there are many opportunities and innovations yet to come if you take the correct approach – a Data First approach.
For those facing pressure from management to adopt and implement AI, my advice is to be patient and strategic. Be open to experimentation, and don’t get caught up in the rush to deploy unproven solutions just for the sake of having AI in place.
At Syniti, we see many opportunities to innovate and integrate AI within our platform and methodology. We believe that AI will be very productive in accelerating the digital transformation journeys for enterprises globally and Syniti is ready to lead the way, by driving revolutionary change through our Syniti Knowledge Platform (SKP), delivery team, and our Data First approach.
Syniti didn’t start by making bold claims about how we can use AI to help others. Instead, we have been quietly experimenting and adopting AI in everything we do, from reviewing legal contracts to developing automated test cases for our platform.
Many companies rushed to put shallow use cases that were poorly integrated into users' workflows, failing to deliver the promised business results and leaving user communities disillusioned. It begs the question, “Can you trust a company to deliver AI capabilities if it doesn’t embrace AI within its own operations and functions?”
At Syniti, we are all about business users and producing business value that is durable. We’ve carefully assessed all potential use cases to ensure they provide the greatest return on investment for our customers, prioritizing those that integrate AI seamlessly into existing workflows. The goal is to create game-changing efficiencies in tasks that users can already complete and enable new capabilities that were not previously possible.
We’ve been both transparent and confident about the potential challenges and benefits. This approach finds the balance between driving innovations into the product and knowing the limits of what is possible, while continually iterating and improving.
Here are some examples of use cases in which we are incorporating AI into the platform:
At Syniti, we have a longstanding commitment to helping our customers understand the value of good data quality. This legacy predates the rise of AI innovation. While many are still figuring out how to leverage AI, we’ve focused on how we can use AI to help our customers get ready for AI by enabling them with high-quality data. By preparing organizations with reliable, clean data, we’ve set the stage for successful AI adoption.
We’ve taken our expertise and developed an AI-assisted Data Quality (DQ) capability within the platform. Our industry-first AI-enabled data quality engine allows customers to establish an advanced data quality program in days—not weeks or months. This breakthrough is powered by decades of Syniti's knowledge and rules, which have been enhanced with an AI model capable of generating data quality rules and reports for any business process or dataset. With just a few clicks through the user interface and a few questions, our customers are already ahead of most traditional data quality efforts.
Syniti has been a leader in the data transformation space, and our methodology, combined with the platform, has helped customers achieve “boring” go-lives—smooth, seamless transitions that deliver results. However, complex data transformations often require technical expertise, particularly in SQL. With AI, we’ve introduced a capability called "What you say is what you get," a play on the traditional "what you see is what you get." This feature allows business users to describe their goals in plain language, and Syniti’s AI model uses the context of the dataset design to generate the necessary transformation rules. This not only enhances accuracy and efficiency but also empowers functional experts to engage directly without the need for technical support. We’re enabling a true digital transformation factory by integrating AI directly into the platform at scale.
These are just a few examples of the innovative solutions we’re driving into our product. But this is only the beginning. As our CEO, Kevin Campbell, often says, “The best is yet to come.”