Senior executives face the challenge of managing GenAI expectations and understanding the reality of GenAI as it evolves—and all the hype only makes this harder. Nonetheless, it’s crucial you find a way to navigate the rapid innovation, shocking headlines, and inconsistent reporting to determine what’s real and what actions you need to take.

Most CEOs surveyed recently by KPMG describe GenAI as a top investment priority. But GenAI is evolving fast, making it difficult for decision-makers to keep pace. The gap between GenAI expectations and the reality of GenAI remains intimidating for many organizations.

The key to realizing the potential of GenAI is recognizing the common challenges and identifying common paths to success.

Succeeding with GenAI means overcoming key challenges

CEOs anticipate that GenAI will drive profitability, open new market opportunities, and boost innovation. In fact, 62% of CEOs expect results within three to five years, according to KPMG. While CEOs are eager to apply GenAI for growth and innovation, significant obstacles such as talent shortages, data privacy issues, and outdated systems often slow progress.

Understanding these barriers will help you anticipate and address them, positioning your organization to align the reality of GenAI with GenAI expectations.

A talent shortage slows GenAI implementation

Demand for skilled GenAI experts is outpacing supply. GenAI requires a workforce with specialized knowledge, yet the expertise needed is not widely available. You may face delays or increased costs in hiring the right people to manage and develop your GenAI projects.

AI content generation requires accurate data

The datasets used to train large language models (LLMs) can include biases or inaccuracies that harm your brand’s reputation. Even well-known brands like Chevrolet have had AI tools backfire in public settings, risking trust. These risks demand robust governance and supervision.

Legacy systems can prevent full GenAI adoption

GenAI models are complex and require significant computational resources. If your company is running on legacy systems, integrating these modern AI models without the right partnerships in place can be a difficult—and costly—task. Ensuring that your infrastructure can support GenAI is a critical step toward closing the gap.

Data and privacy must be addressed to meet GenAI expectations

GenAI models trained on vast amounts of data may inadvertently expose sensitive information, causing data privacy problems. Samsung, for instance, experienced a leak of internal data due to an engineer uploading sensitive information to ChatGPT. You must ensure that your AI systems are both secure and compliant with privacy regulations.

Meeting GenAI expectations means addressing critical success factors

Once you’ve identified the challenges, it’s time to focus on the steps that can turn your GenAI aspirations into reality. Successful GenAI integration hinges on building the right team, testing early with pilot programs, and investing in continuous learning.

By taking a strategic approach to AI implementation, you can harness the full potential of GenAI and drive meaningful outcomes across your business.

Build a cross-functional team

GenAI touches your entire organization. Assemble a team that includes AI specialists, legal experts, and product managers as well as people from marketing, IT, and HR to help ensure a comprehensive approach. A well-rounded team can better navigate the technical and ethical implications of GenAI adoption, while also addressing how GenAI will improve workflows, corporate governance, and customer interactions.

Start with a pilot program

Before scaling, test GenAI in controlled environments. Choose specific use cases, like improving marketing workflows, improving the customer experience, or optimizing supply chain operations. Then measure the impact against clear KPIs.

For example, a GenAI-powered chatbot might improve customer response time or satisfaction rates. A successful pilot should provide insights into the best ways to expand GenAI across your enterprise and demonstrate early wins that can help build support among stakeholders.

Invest in continuous learning

GenAI isn’t static. It’s a constantly evolving technology that needs ongoing refinement. AI systems must be updated regularly with new data to keep pace with evolving business needs, and employees need to be equipped with the knowledge to use AI tools effectively.

Offer training programs for your staff to understand how GenAI integrates with their workflows and empower them to make data-driven decisions. This investment can empower your team to feel more comfortable using AI in day-to-day operations and encourage enterprise-wide adoption.

Embrace a human-in-the-loop approach

GenAI is powerful, but with great power comes great responsibility. A human-in-the-loop (HITL) approach helps ensure that AI outputs are reviewed for biases or inaccuracies, protecting your brand’s reputation. This approach can prevent issues like hallucination—where the AI generates false or misleading information.

For instance, in sensitive sectors such as finance or healthcare, human review is essential for catching errors and ensuring the AI’s outputs align with legal and ethical standards. A HITL approach enables employees to collaborate with GenAI, driving better outcomes by combining human intuition with AI’s processing power.

Enhance data governance

GenAI relies on vast amounts of data, but not all data is created equal. Focus on using diverse and representative datasets to minimize bias and improve output quality. Keep your data governance policies robust enough to protect privacy and comply with regulations, especially as more businesses rely on cloud-based AI services.

Regular audits of your data handling processes will help identify any risks associated with data leaks or misuse. Strong governance is essential not only to safeguard sensitive information but also to maintain customer trust.

In addition, you could use a frontier AI data foundry platform to curate diverse and representative datasets that minimize bias and improve output quality.

Streamline infrastructure and integration

Integrating GenAI into your business operations often requires upgrading legacy systems. Your IT team should assess whether your current infrastructure can support the demands of AI, such as processing large datasets in real time or running complex machine learning algorithms.

In some cases, it might be necessary to replace outdated systems with more modern, AI-friendly solutions. Cloud-based infrastructure can also help scale GenAI capabilities while reducing the cost of managing in-house servers. Consider collaborating with technology partners who specialize in GenAI to help you navigate this technical complexity.

Upskill and retrain talent

GenAI is transforming the workplace, and your business must be proactive in preparing your workforce for these changes. While GenAI can automate many tasks, it also creates new roles that require specialized skills in AI and machine learning. Focus on retraining and upskilling employees to fill these emerging roles rather than simply replacing them with automation.

For example, customer service representatives may transition to managing AI chatbots, while data analysts may be trained to work with AI-powered analytics tools. By upskilling your team, you not only retain valuable employees but also foster innovation across your organization.

Create a practical business case

One of your chief concerns is likely ROI. The reality of GenAI is that, while challenges exist, the potential rewards are too great to ignore. To make the business case for GenAI, focus on use cases that drive measurable outcomes.

For instance, retail businesses can train AI models on first-party data to anticipate customer behavior, offering personalized product recommendations that drive sales. Meanwhile, healthcare organizations can use synthetic data to train AI models that improve diagnostics and patient outcomes, without compromising privacy.

Find a trusted partner who understands the reality of GenAI

GenAI adoption can be daunting, especially when it comes to implementing the technology at scale. Working with an experienced partner can make the process smoother.

A partner who specializes in GenAI can help you stay on top of the latest advancements and best practices while providing access to a team of experts capable of enhancing your systems. Partners can also guide you through common challenges such as minimizing hallucination risks, enhancing data security, and ensuring ethical AI use.

Keep your expectations for GenAI grounded in reality

By focusing on practical steps—starting with a clear strategy, assembling the right team, and embracing data governance—you can move your business closer to realizing the true potential of GenAI.

Discover how Centific can help you succeed with GenAI.