AI Adoption Challenges: Why Executives Are Slow to Embrace Generative AI

Generative AI is knocking on every C-suite, but only few are answering with confidence. Ever wondered why?

AI adoption today goes far beyond what many initially perceive. Generative AI is no longer limited to content creation - it has evolved into a core pillar of enterprise AI infrastructure. From building autonomous agentic systems to orchestrating complex workflows across departments, its capabilities are both strategic and expansive.

One of its most transformative roles lies in unifying fragmented enterprise data through semantic integration. This enables decision-makers to extract real-time insights from both structured and unstructured data sources. As a result, generative AI enhances decision-making, streamlines knowledge processes, and powers the next generation of AI-native applications.

Despite its well-documented potential to drive innovation and boost productivity, executives still remain slow and hesitant to adopt generative AI. This largely stems from leadership concerns, adoption costs, integration issues, and security risks. 

So let’s dig deeper into some of the key AI adoption challenges executives face and how they can overcome them to bring maximum value to their operations.

The Current State of Generative AI Adoption

Did you know that the global generative AI market is projected to reach  $136.7 billion in 2030 from a $20.9 billion valuation in 2024? Source Widespread public attention and industry momentum towards enhanced productivity and cost reduction have been significant contributors to this number. However, challenges such as data security and shortage of skilled talent are still slowing full-scale integration in many companies. 

While the technology sector leads the charge in generative AI adoption at an impressive 88% usage rate across functions in 2024, other sectors are not far behind. Source Here’s how other industries are utilizing generative AI:

• Healthcare: Generative AI is primarily used here for medical imaging, diagnostics, and personalized patient communications.
• Marketing and advertising: Used for campaign generation, content personalization, and creative assistance.
• Finance: Adopted by banks and other financial institutions for fraud detection, risk management, and customer service automation.
• Retail: Highly used for product and display design, dynamic pricing in real-time, marketing content generation, supply chain optimization. 

Key Challenges Hindering Executives from Adopting Generative AI

While generative AI offers immense potential, there are still plenty of reasons why executives today still hesitate to fully adopt generative AI into their operations. Some of them are:

Lack of technical expertise 

The market today for AI specialists is highly competitive, with 45% of organizations worldwide citing lack of skilled professionals in AI and generative technologies a top challenge in AI adoption. Source Since generative AI models are highly complex and require technical expertise, companies might struggle to find the right talent that can ensure project success.

Integrating generative AI into legacy systems is also a technically-demanding and time-consuming process for which organizations may not have expert resources readily available. Along with lack of expertise comes the issue of regulatory management too as executives find it difficult to navigate data privacy, compliance, and ethical considerations as well. 

Leadership resistance to change

Executives may resist adopting generative AI due to factors like job displacement and not wanting to change well-established workflows. It all stems from conservative leadership practices where leaders:

• Are risk-averse and do not wish to encounter any failures
• Have personal discomforts with AI initiatives and preferring familiar workflows 
• Fear internal conflicts with IT teams and employees, furthering complications in AI adoption
• Are unclear about the ROI generative AI projects can generate for the company

Executives are also bound to exert passive resistance to generative AI tools due its technical complexities and there is the common notion that certain leadership functions may become obsolete and power structures within the organization may be disrupted due to it. 

High implementation costs

Generative AI implementation requires substantial upfront investments . There is no fixed price tag as there are several costs that organizations need to cover: 

1. Research and development
2. Choosing the development model
3. Gathering and preparing data
4. Training of model
5. Deployment and integration
6. Ongoing maintenance 

Additional cost considerations also include:

• Budget overruns: Underestimating resource demands, leading to budget overruns
• Vendor lock-in: Hidden costs and lack of pricing transparency can unexpectedly inflate costs 
• Alternative options: Third-party services, open-source models, and APIs may reduce initial investment costs, but have limited customization features

Data complexity and privacy concerns 

Generative AI uses complex, cross-format data across multiple formats like texts, images, code, etc. Managing these massive datasets can be complicated and may require sophisticated data infrastructures. And on top of data complexity, there is the matter of data privacy and regulatory issues that executives might seek to avoid. 

Common data complexity issues include:

• Unstructured data: Some data sets are unstructured or semi-structured, lacking a consistent schema. This makes it difficult to aggregate, search, and process data efficiently.
• Data quality and consistency: The success of generative AI models heavily rely on high-quality, consistent data. Missing values, outdated information, and improper formatting can impact model performance.
• Data integration: Organizations store data sets in multiple formats and platforms, making integration complex and resource-intensive process due to compatibility issues. 

Even after bypassing complex data structures, generative AI still raises the question of how secure your data sets are. Generative AI models pose the risk of data leaks as they can inadvertently memorize and reproduce confidential information from their training data. Executives would also have to implement robust data governance frameworks to ensure AI systems do not misuse personal information, violating privacy concerns of stakeholders. 

Overcoming the Barriers of Generative AI Adoption

As generative AI continues to evolve, organizations are looking to adopt this technology into their operations. However, we’ve seen why executives are skeptical about it. But the good news is that there are strategies that can help overcome those barriers for smoother AI adoption and sustained success:

Developing a Robust AI Strategy

At the foundational level, a clear and actionable AI strategy is critical for aligning generative AI initiatives with business objectives. Executives should start with:

Defining their vision and goals: Establish what they wish to achieve using generative AI in both the short and long-run. It can be improved customer experiences, new revenue streams, or enhanced operational efficiency. 

Prioritizing use cases: Identify high-impact areas where generative AI can deliver measurable value. It also emphasizes. This is where specialized AI consulting firms step in to bring their vast industry expertise to the table by helping companies accelerate AI adoption.

Create a roadmap: The final step is to develop a phased plan for piloting, scaling, and integrating generative AI solutions, ensuring alignment with broader business goals.

Thinking big, but starting small

The concept of thinking big, but starting small in generative AI adoption is a strategic approach designed to develop ambitious ideas that can deliver value while focusing on the success of initial and manageable projects. With this strategy, organizations can lay the groundwork for future projects while also allowing them to explore the potential of AI without disrupting operations or exhausting all their resources. 

But why should you think big?

By thinking big, you develop broad and ambitious AI strategies and explore what they can do in terms of bringing in new services, operational capabilities, and satisfying new customers. It also encompasses designing the right data architecture, breaking down silos, and promoting collaboration between cross-functional teams. 

And what does it mean to start small? It’s all about:

• Starting with smaller initiatives to test AI’s potential
• Using pilot projects as a proof of concept for large-scale projects and to showcase quick wins
• Focusing on specific, high-impact areas for quicker, more tangible results
• Automating routine tasks and enhancing data analysis within single departments without overhauling entire systems

To sum it up, this approach can reduce failure rates and accelerate value creation by harnessing the power of generative AI and combining it with strategic foresight and implementation of ideas. 

Drafting a clear AI roadmap

Generative AI is a powerful tool that can help organizations transform business operations and make strategic decisions. But for AI initiatives to be truly effective, they need to ensure their AI goals align with business objectives. This alignment ensures generative AI projects deliver measurable business value and support overall corporate strategy. 

With alignment comes achievable milestones and timelines set with clear metrics to measure progress and cost-savings. This is primarily to measure success and facilitate ongoing ROI assessments. 

Preparing an AI roadmap and executing all plans is a lengthy process that requires careful planning and precision. And this is paramount especially in fields like healthcare. Let’s explore how Tredence enabled a US-based healthcare center leverage generative AI to deliver patient-centered care:

Case Study:

The challenge

• Healthcare professionals spend considerable time composing care plans instead of engaging with the patient.

• Non-standardized processes for drafting care plans means providers miss out on key information, record inaccurate entries, and miscommunicate instructions. 

The approach

• The goal was to rapidly develop tailored, comprehensive, and HIPAA-compliant care plans based on the patient’s most recent and previous interactions with the provider. 

• The plans had to be continuously refined based on the patients’ current progress, medical history, and preferences, along with latest provider insights and up-to-date information from care plans of thousands of patients. 

How Tredence helped address the above challenges

Within one month, Tredence built and delivered a fully functional “MVP Advanced Care Plan Feature.” The following steps highlight how the plan was built:

• Tredence professionals created a wish list of requirements, combining industry best practices with specific client requirements. 
• They conducted a technical assessment of rich data sources on multiple client systems. The process was followed by utilizing cutting-edge generative AI LLMs to construct a powerful Azure GPT 3.5 Turbo Model.
• The model played a crucial role in meticulously analyzing provider notes after each patient interaction, combining them with insights from a vast repository of related care plans and ensuring they were HIPAA-compliant.

Business Impact

• Providers were able to navigate the care planning process quickly and effectively, saving time and costs thanks to smooth accurate flow of information, quick personalization, and standardization.
• Improved access to updated care plans based on their latest progress and opportunity to have meaningful interactions with providers reduced administrative burdens, promoting patient trust and confidence. 

Investing in talent development

Investing in talent development goes beyond just hiring the right people. It also includes key elements such as:

Fostering a culture of AI enthusiasm: Everyone in the organization, from leadership to employees actively promote and participate in AI initiatives. Also includes providing a safe environment for employees to experiment with AI tools without fear of failure.

Providing comprehensive training programs: This involves assessing current skill levels of employees to identify knowledge gaps, covering the fundamentals of topics such as AI, ML, deep learning, and data management. These steps are followed by offering hands-on workshops, real-world projects, and regular assessments to refine training effectiveness over time.

Implementing No-code/Low-code tools: These tools improve access to generative AI by allowing employees with limited technical knowledge to build, customize, and deploy AI solutions. They bridge the skill gap among employees and allow them to work independently without having to rely on technical experts. In short, organizations can scale AI operations, empowering a broader segment of the workforce with low-code/no-code tools. 

Driving Holistic AI Transformation

AI transformation isn’t just adopting new technologies - it’s about fundamentally reimagining how your company operates, delivers value, and stands out among competitors. For executives, it’s about orchestrating the right changes across people, processes, and technology. 

Data readiness: AI transformation depends on high-quality, accessible data. Which means executives would have to invest more in data management and ensure AI models are trained to process relevant and accurate information. 

Process design: Generative AI adoption may require full reengineering of workflows that take advantage of automation, predictive analysis, and decision support. As part of the reengineering process, executives have the responsibility to identify processes that can be easily performed with generative AI, such as customer service, content creation, supply chain optimization, etc. 

Integration with legacy systems: Some companies may still run on legacy systems that aren’t compatible with AI. Executives would have to formulate integration or data migration strategies and use middleware to bridge that gap.

Ethical AI use: Ethical AI guidelines would have to be developed to address issues like fairness, transparency, and accountability when handling matters related to generative AI use.

Innovation mindset: Without innovation, tech-driven organizations experience minimal to no growth. Building a culture of innovation that encourages experimentation and rapid prototyping is one way to make breakthroughs in AI. With that, executives can also empower employees to suggest new AI use cases, explore, and reward them for participating in initiatives pertaining to AI transformation. 

Stay Ahead in Generative AI Advancements with Tredence

Despite the hurdles - from shortage of talent to cultural resistance, executives truly cannot overlook the transformative potential of generative AI to drive innovation, future-proof operations, and unlock new revenue streams. And the path to generative AI adoption can be straightforward when starting small and developing a culture of continuous learning. 

If you’re looking to accelerate your journey in generative AI adoption, look no further than Tredence as we are dedicated to supporting you in that journey. From generative AI consulting to UX design, change management and governance, we offer our expertise and end-to-end services to create Gen AI structures that scale. By working with us, you can develop new business strategies, redefine value-chain processes, and scale model development conveniently. Additionally, we offer domain-specific accelerators that are easily customizable and can simplify complex processes.

Get in touch with us today and let’s speed up your path to generative AI success!

FAQs

1. How does organizational resistance impact generative AI adoption?

Organizational resistance stems from fear of job displacement, skepticism about AI's effectiveness, and reluctance to change established processes. Employees may feel uncertain about how AI will affect their roles, leading to a lack of buy-in.

2. How can companies address the skills gap in generative AI adoption?

To bridge skill gaps, businesses can invest in training programs, leverage low-code/no-code tools, and hire specialized professionals to accelerate implementation and innovation

3. What role does leadership play in successful Gen AI implementation?

Leadership buy-in is critical for driving generative AI adoption. Without executive sponsorship, initiatives often lose momentum. Leaders must oversee implementation, measure impact, and regularly communicate progress to align AI efforts with strategic business goals.

4. What kind of data privacy risks do generative AI tools pose?

Common data privacy risks posed by Generative AI tools include data breaches, unauthorized sharing of user data, insufficient anonymization leading to re-identification of individuals, lack of user consent and transparency, and improper data retention or deletion practices.

5. What governance frameworks are recommended for managing generative AI risks?

Effective governance involves establishing clear policies and principles to ensure responsible AI use, mapping AI systems in use, defining roles and responsibilities, and implementing controls to mitigate risks while fostering innovation. Frameworks should incorporate privacy by design, ethical guidelines, and bias mitigation strategies.

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Editorial Team
Tredence