GenAI Won’t Work Until You Nail These 4 Fundamentals

If you think generative AI (GenAI) is the ultimate solution to “revolutionize” your business, think again. The headlines may paint it as akin to the steam engine of our era or a magic wand to cut costs and drive innovation, but the reality is that inefficiencies, unpredictable outputs and costly experiments will fail to deliver results for most organizations.

The truth is that GenAI is not a quick fix. Without solid foundations of an explicit roadmap, clean data, robust governance and a culture ready to embrace change, it becomes another tech trend that overpromises and underdelivers. Many companies are charging ahead, but few are getting it right.

So, where does your organization stand? Are you building for long-term success or repeating the same missteps as others? What separates the hype-fueled adopters from those actualizing GenAI’s potential?

This article will uncover the four fundamentals for using GenAI effectively, backed by industry surveys from Harvard Business Review (HBR) and KPMG, as well as expert insights from industry leaders. You’ll receive a blueprint for overcoming these challenges with more actionable insights available in this comprehensive HBR report sponsored by Boomi.

Key takeaways:

• Clear roadmap: Align GenAI with business goals by prioritizing feasible, high-value use cases.
• Solid data foundation: Clean and integrate data for scalable and accurate GenAI models.
• Robust governance: Develop responsible AI policies and conduct audits to reduce risks.
• GenAI-ready culture: Educate teams and encourage cross-functional collaboration for alignment.

Core Problems Holding Back GenAI

Understanding the core GenAI challenges is the first step toward building a sustainable GenAI strategy.

Many Organizations Chase Hype Instead of Value

Too often, organizations leap into GenAI fueled by excitement rather than strategic intent. The urgency to appear innovative or keep up with competitors drives rushed implementations without distinct goals.

They see GenAI as the “shiny new [toy],” as Kevin Collins, CEO of Charli AI, aptly puts it, but the reality check comes hard and fast: “Getting to that shiny new toy is expensive and complicated.” This rush is reflected in over 30,000 mentions of AI on earnings calls in 2023 alone, signaling widespread enthusiasm but often without the necessary clarity of purpose.

A KPMG survey from 2023 reveals that 19% of organizations lack an informed business case for GenAI adoption. This absence of focus alters what could be a transformative technology into a costly experiment that fails to align with business objectives.

👁 Barriers to genAI adoption: 19% lack of clear business use case; 23% security concerns; 24% internal resistance; 27% legacy systems; 28% talent gaps; 22% cost

Poor Data Quality and Silos Limit GenAI’s Potential

The shortage of strategic clarity isn’t the only roadblock. Even when organizations manage to identify a business case, they often find themselves hamstrung by another pervasive issue: their data.

Messy data hampers organizations’ ability to mature beyond entry-level use cases. Data silos, inconsistent formats and incomplete records create bottlenecks that prevent GenAI from delivering its promised value. In the HBR whitepaper, Maryam Alavi, professor of IT management at Georgia Tech, said:

“Large enterprises, in particular, have silos, different versions of the same data, different approaches to naming data elements, different formats and large volume and velocity of data. Data needs to be managed a lot better in all aspects — [whether it’s] integration, issues around security, privacy, access control — than it has been.”

That over 87% of executives cite data silos and inconsistencies as significant barriers tells a sobering story. GenAI models cannot scale or perform effectively without clean, harmonized data pipelines.

Lack of Governance Creates Risks and Inefficiencies

Weak or nonexistent governance structures expose companies to various ethical, legal and operational risks that can derail their GenAI ambitions.

According to data from an Info-Tech Research Group survey, only 33% of GenAI adopters have implemented clear usage policies. This absence of oversight allows risks like hallucinations, bias and inaccurate outputs to go unchecked, creating potential regulatory and reputational vulnerabilities.

👁 AI skeptics report low adoption of AI governance practices

Responsible AI principles (covering fairness, transparency and accountability) are often an afterthought, leading to systems that don’t align with organizational values or comply with legal requirements.

Cultural Resistance and Mistrust Slow Progress

Governance frameworks alone cannot address the deeper human challenges that organizations face. The cultural dynamics within companies often create barriers that no amount of technical precision can overcome.

Leaders may overestimate their grasp of GenAI, with 67% of board members in 2023 rating their understanding as “advanced” or better, according to Alteryx research. Yet, this confidence rarely translates to informed decision-making, leading to a disconnect between strategic ambitions and practical realities.

👁 67% of board members say they have "advanced" understanding of GenAI, and 24% say internal resistance is a major hurdle to adoption

On the flip side, employees are often skeptical. For many, GenAI feels more like a threat than a tool, stirring fears of job displacement or mistrust in its outputs. It’s no wonder that 24% of organizations cited internal resistance as a major hurdle in the HBR study.

These contrasting perspectives — leaders pushing forward and employees pulling back — result in a cultural tug-of-war. Without alignment, GenAI adoption becomes another well-intentioned effort lost in translation.

Key Solutions for Unlocking GenAI’s Potential

Despite these challenges, many organizations still adopt GenAI, primarily for low-hanging fruit use cases like customer relationship management (CRM) software enhancements or AI-generated content. But is this enough? Organizations poised for long-term success are those preparing for advanced use cases with a holistic approach backed by these four fundamentals:

1. Create a Roadmap for Sustainable and Scalable GenAI Adoption

Building a roadmap for generative AI adoption requires a structured approach to evaluating and prioritizing use cases. In the HBR whitepaper, Himanshu Arora, vice president and CXO advisor at Infosys, suggested evaluating use cases across six dimensions to identify those most likely to deliver value:

• Business validity and feasibility: Assess whether the use case aligns with strategic objectives and can realistically be implemented.
• Quantified business value: Measure the potential impact, such as cost savings or revenue growth.
• Data readiness: Determine whether the necessary data is clean, accessible and harmonized.
• Responsible AI alignment: Avoid use cases that cross ethical or regulatory boundaries.
• Cultural readiness: Evaluate the organization’s ability to adopt and scale the use case.
• Cost: Balance expected returns with the investment required.

👁 Roadmap for sustainable and scalable GenAI adoption

Seth Earley, founder and CEO of professional services firm Earley Information Science, also drew attention in the report to the importance of focusing on differentiators, such as business model innovation or idea generation, to drive competitive advantage. Starting with low-risk, high-impact use cases builds momentum and prepares organizations for advanced applications, enabling long-term scalability and growth.

2. Build a Solid Data Foundation for GenAI’s Success

“Your business is going to be running on those models and they’re going to be making predictions. So, they’d better get it right because it is garbage in, garbage out.”
— Kevin Collins, Charli AI

To unlock GenAI’s potential, you must address five critical dimensions of data readiness recommended by Arora:

• Data readiness: Cleanse and harmonize data to eliminate silos and inconsistencies.
• Model efficacy: Evaluate how well models align with business needs and improve outputs.
• Use-case value: Measure the impact of GenAI in specific scenarios, such as faster onboarding or coding efficiency.
• Strategic alignment: Assess the contribution of GenAI initiatives to broader business goals like revenue growth or operational improvements.
• Regulatory compliance: Monitor data privacy, security and ethical standards to reduce liability and maintain trust.

👁 5 critical dimensions of data readiness

Combining these dimensions with actions like data readiness reviews, metadata standardization and system integration will refine your data ecosystem and position your organization to scale GenAI effectively.

Building this foundation also requires upskilling employees, partnering with vendors and recruiting individuals with technical and problem-solving skills.

3. Establish Governance to Mitigate Risks

To build a strong governance framework, you should:

• Develop responsible AI principles: Create policies emphasizing data privacy, fairness, accountability and transparency. “Systems must avoid introducing biases into processes and be inclusive and respectful to individuals and communities,” advises the HBR report.
• Involve cross-functional teams: Bring together IT, legal, business leaders and end users to create and oversee governance policies. Bill Wong, an AI research fellow at Info-Tech Research Group, recommends distributing responsibilities across the organization to address AI risks better.
• Conduct regular audits and impact assessments: Test models for compliance, evaluate outputs and refine processes to maintain alignment with ethical and regulatory standards.
• Build a library of tested use cases: Earley suggests establishing “gold standard use case” benchmarks for policy compliance and operational success.

👁 Components of a strong governance framework

A well-structured governance approach protects against liabilities and positions GenAI as a trustworthy and scalable organizational tool.

4. Build a Culture that Embraces GenAI

Tom Davenport, a professor at Babson College, suggests forming cross-functional teams: “Companies should have a group, maybe it’s an AI steering committee, not just made up of technologists, but with people in the business who are somewhat knowledgeable about [GenAI].”

Key actions to build a GenAI-ready culture:

• Educate leadership and employees: Provide coherent guidance on GenAI’s role as an augmentation tool, not a replacement for human expertise.
• Establish centralized leadership: Create a center of excellence (CoE) or assign a chief AI officer to guide strategy, governance and adoption processes.
• Encourage cross-functional involvement: Involve IT, human resources, risk management and business units in shaping and executing GenAI initiatives.

👁 Key actions to build a GenAI-ready culture

A collaborative approach bridges gaps between leadership and employees, building trust and aligning organizational goals with GenAI’s potential.

Wrapping Up

GenAI holds immense potential to transform organizations, enabling innovation, streamlining workflows and driving new business models. However, realizing these benefits requires more than enthusiasm. Without addressing these four foundational gaps, even the most ambitious GenAI projects risk becoming costly experiments that fail to deliver meaningful value.

Is your organization prepared to make the investments needed to build sustainable, scalable GenAI initiatives? This article only scratches the surface of what’s required to unlock GenAI’s transformative power.

To dive deeper into overcoming these challenges and access more actionable strategies for success, adopt the insights and expert recommendations in this comprehensive HBR report sponsored by Boomi.