AI is everywhere these days. Companies across the globe are clamoring to hop on the AI bandwagon and leverage its power to reinvent their business potential.
But, as Bill Tennant, Chief of Revenue at BlueCloud, pointed out in a recent conversation, enterprises might be missing a few key points when it comes to adopting AI successfully, emphasizing that organizations need to “take the blinders off” and rethink how they approach AI integration.
In this article, Tennant will take you through some of the biggest challenges companies face when adopting AI and share practical solutions they must incorporate to tackle the challenges head-on.
1. Data Governance & Security: The Silent Killer of AI Initiatives
One of the biggest hurdles for enterprises diving into AI is dealing with data, especially when it comes to open model vulnerabilities. These vulnerabilities can expose organizations to security risks, data breaches, and even legal issues around intellectual property. You want AI to work its magic, but are you sure the data it's training on is secure, accurate, and legally usable?
Bill Tennant offers a clear solution: prioritize governance.
"You really have to be cognizant of the fact that some of that data is maybe not positioned to provide you with the most secure and accurate framework to support what you're doing. Organizations must ensure security and address concerns about using data they don’t own. The only way to effectively handle this challenge is to have clear governance principles throughout the entirety of the process,” said Bill.
Governance isn't just a nice-to-have—it’s essential. Whether you’re dealing with open-source models or your own proprietary ones, ensuring that data flows securely and ethically across your enterprise will make or break your AI success.
2. Overcoming Integration Hurdles
Another issue many enterprises face is integration. They often try to implement AI too broadly, which complicates integration.
They want to combine 15 different areas and create a beautiful tapestry, paint the Sistine Chapel with AI. They want a grand solution where AI is going to optimize every process, solve every problem, and churn out massive gains overnight. But that’s not how it works in reality. Bill adds that the data needed to support companies’ AI vision may not be available yet or may not be built in a way that supports the company’s vision.
The truth is that companies need to focus on one specific AI use case that delivers value quickly.
"The reality is, if you approach it with a clear focus on finding one specific way to deliver value with this use case, you can achieve ROI more quickly and effectively deploy that particular solution," Bill explained.
Focusing on a single AI-driven outcome, such as revenue optimization or improving efficiency in a specific process, makes integration manageable and allows businesses to show measurable success quickly. Once that use case is proven, you can scale from there.
“Organizations must be very clear about what they want to achieve with AI and what's required to make it a success. This is where a partner like BlueCloud can bring enormous value - not only helping customers understand their use case and what’s needed to deliver the solution, but also by empowering them with the right expertise and engineering support to tackle any challenges that come their way,” explains Bill.
3. Balancing Data Privacy with AI Ambition
With data privacy concerns on the rise, many companies find themselves stuck in a paradox. They want to use data to build powerful AI systems, but they're constrained by privacy regulations, internal governance, and concerns over sensitive data usage. How do you innovate with AI when you can't access the data you need?
According to Bill, the answer lies in anonymization and encryption techniques.
"The only way to overcome that challenge is to focus on the governance aspect and partner with someone who can help you anonymize data and implement encryption techniques. This approach will protect sensitive information, ensure transparent data practices, support regular audits, and build long-lasting trust with your users," Bill advised.
By anonymizing data and leveraging encryption, companies can mitigate the risk of data privacy breaches while still using valuable datasets to train their AI models. Synthetic data, or artificially generated datasets, can also play a role in ensuring privacy without sacrificing the quality of the AI models.
4. Proving ROI: The Tangible Benefits of AI
Gartner predicts that by 2028, more than half of enterprises that have built their own large models from scratch will abandon those projects due to rising costs, complexity, and the growing burden of technical debt.
A lot of companies are facing challenges when it comes to defining and measuring the ROI from their AI initiatives. In fact, a Gartner also found that one of the biggest obstacles is simply figuring out how to define and measure the return on investment.
The key questions organizations need to ask themselves to avoid this challenge are:
- How do you measure success?
- Can you keep up with that pace of change not just from a delivery standpoint, but also in terms of managing change internally and externally?
- Can you leverage AI to uncover new efficiencies and scale those results for even more business value?
"It's easy to get swept up in the excitement of AI, but without a clear ROI, your AI project is unlikely to succeed. In fact, less than half of AI projects ever make it to production. That's why it's crucial to define specific cost savings, efficiency improvements, or revenue gains upfront. Without a clear business objective and a dedicated stakeholder to own that goal, success will always be a challenge," Bill explained.
BlueCloud’s borderless delivery model empowers organizations to achieve far more, faster, and at a lower cost by providing both strategic and executional support. BlueCloud helps companies quickly develop a strong AI business case, deliver tangible value, and build a solid foundation for long-term AI maturity and growth.
5. Skill Gaps: Overcoming the Human Element
One of the biggest barriers to AI adoption is the lack of skilled talent. AI requires a specialized workforce, but many companies simply don’t have access to the right people.
“There are incredibly talented people across the globe, but if you're a small, mid-sized, or even large enterprise hiring for a specialized role, focusing on one region limits your options. BlueCloud’s borderless delivery model eliminates that limitation. By tapping into a global talent pool, BlueCloud connects businesses with top-tier experts from around the world—talent that would otherwise be out of reach—helping companies access the right skills faster and more cost-effectively,” Bill explained.
This model not only fills the immediate skill gaps but also helps train and upskill internal teams and scale their resources to meet their business needs. It also provides flexibility, allowing organizations to scale up or down as needed, without the hassle of trying to hire someone who has to come into the office every day in a specific location.
6. Ethical Considerations: Avoiding AI Pitfalls
AI can do amazing things, but it also comes with its own set of ethical challenges. From biased recommendations to potential copyright infringements, Bill warns that companies need to know what their AI systems are doing behind the scenes as well as what recommendations are coming from AI, and GenAI models.
Are the models suggesting things that violate certain business principles or making recommendations that discriminate against certain groups? Are they driving decisions that harm the business overall?
“To avoid bias in AI, it's crucial to take a holistic view. It’s not just about the output but also the underlying patterns and sentiment. Like analyzing customer emotions in a call center, with generative AI, you need to examine the questions, responses, and emerging themes to ensure ethical boundaries aren’t crossed,” said Bill.
BlueCloud’s Approach: Partnership, Not Just Service
Rather than simply acting as a service provider, BlueCloud builds and nurtures deep, collaborative relationships with its clients, ensuring accountability on both sides.
“BlueCloud's key differentiator is their partnership-based approach. It's about eliminating that vendor-customer relationship and becoming true partners... holding each other accountable throughout the process," Bill said.
BlueCloud’s borderless delivery model also allows organizations to overcome their skill gap and triumph on the AI adoption journey by leveraging the accumulative power of premium global talent.
By fostering these collaborative relationships and BlueCloud’s unique approach to building strong governance frameworks, narrowing AI use cases for better ROI, and addressing ethical concerns, BlueCloud ensures that their clients’ AI initiatives are guided by shared goals and a commitment to long-term success.
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Explore our Generative AI and Machine Learning (ML) and Data Governance services to learn how we can help you lead your organization into the era of AI. Reach out to Bill Tennant to learn how we can help you overcome challenges and reinvent with AI on your data journey.
