Generative AI holds incredible promise, but that potential is often blocked by poor app experiences.
AI leaders are grappling with not just the problem of model performance, but the practical realities of turning generative AI into user-friendly applications that deliver measurable enterprise value.
Infrastructure demands, unclear output expectations, and complex prototyping processes slow down progress and frustrate teams.
The rapid pace of AI innovation has also led to a growing patchwork of tools and processes, forcing teams to spend time on integrations and out-of-the-box functionality instead of delivering meaningful business solutions.
In this blog, we explore why AI teams face these obstacles and offer actionable solutions to overcome them.
What stands in the way of effective generative AI apps?
While teams are rapidly advancing technology, they often face significant barriers to delivering useful and effective business applications.
- technical complexity: From vector databases to large language model (LLM) orchestration, building the infrastructure to support generative AI apps requires deep technical expertise that most organizations lack. Choosing the right LLM for your specific business needs adds another level of complexity.
- unclear goal: The unpredictability of Generative AI makes it difficult to define clear, business-aligned goals. Teams often struggle to connect AI capabilities to solutions that meet real-world needs and expectations.
- Talent and expertise: Generative AI moves quickly, but there is a shortage of skilled talent to develop, manage, and govern these applications. Many organizations rely on multiple roles to fill the gaps, increasing risk and slowing down progress.
- collaboration gap: Misalignment between technical teams and business stakeholders often results in generative AI apps failing to meet expectations in both what they deliver and how users use them.
- prototyping barriers: Prototyping generative AI apps is slow and resource-intensive. Teams struggle to test user interactions, improve interfaces, and validate output efficiently, slowing progress and limiting innovation.
- Hosting Difficulties: Deployment is often difficult due to high computing requirements, integration complexity, and unpredictable results. Success requires not only cross-departmental collaboration, but also strong coordination and tools to adapt to evolving needs. Without workflows to unify processes, teams end up managing disjointed systems, further delaying innovation.
What are the results? A fragmented and inefficient development process that undermines the transformative potential of generative AI.
Despite these app experience obstacles, some organizations have successfully navigated this environment.
For example, The New Zealand Post, a 180-year-old institution, integrated generative AI into its operations after carefully assessing its needs and capabilities, resulting in a 33% reduction in customer calls.
Their success highlights the importance of aligning generative AI initiatives with business goals and having flexible tools that allow teams to adapt quickly.
Turn generative AI challenges into opportunities
The success of generative AI depends on more than just technology. This requires strategic alignment and strong execution. Even with the best intentions, organizations can easily make mistakes.
Overlooking ethical considerations, mismanaging model results, or relying on flawed data can make small mistakes snowball into costly setbacks.
AI leaders must also respond to rapidly evolving technologies, skills gaps, and growing demands from stakeholders while ensuring that their models are secure, compliant, and perform reliably in real-world scenarios.
Here are six strategies to keep your plan on track.
- Business alignment and needs assessment: Align your AI initiatives with your organization’s mission, vision, and strategic goals to ensure meaningful impact.
- Ready for AI technology: Evaluate your infrastructure and tools. Does your organization have the technology, hardware, networking, and storage to support generative AI implementation? Do you have tools that enable seamless coordination and collaboration so your team can quickly deploy and improve models?
- AI Security and Governance: Include ethics, security, and compliance in your AI initiatives. Establish processes for continuous monitoring, maintenance and optimization to mitigate risk and ensure accountability.
- Change Management and Training: Create a culture of innovation by building skills, providing targeted training, and assessing readiness across the organization.
- Expansion and continuous improvement: Identify new use cases, measure and communicate AI impact, and continuously improve your AI strategy to maximize ROI. Focus on accelerating time to value by ensuring AI delivers real, measurable results, adopting workflows that can adapt to your specific business needs.
Generative AI is no industry secret. Transforming businesses across sectors, driving innovation, efficiency and creativity.
However, according to the Unmet AI Needs survey, 66% of respondents said they had difficulty implementing and hosting generative AI applications. But with the right strategy, companies in almost any industry can gain a competitive advantage and unlock the full potential of AI.
Lead the way to generative AI success
AI leaders hold the key to overcoming the challenges of implementing and hosting generative AI applications. You can pave the way for success by setting clear goals, streamlining workflows, fostering collaboration, and investing in scalable solutions.
To achieve this, it’s important to move beyond the clutter of disconnected tools and processes. AI leaders who integrate models, teams, and workflows gain a strategic advantage by being able to quickly adapt to changing demands while ensuring security and compliance.
Equipping your team with the right tools, targeted training, and a culture of experimentation transforms generative AI from a difficult initiative into a powerful competitive advantage.
Want to learn more about the gaps your team faces when developing, delivering, and managing AI? Explore our Unmet AI Needs report for actionable insights and strategies.
About the author
Savita has over 15 years of experience in the enterprise software industry. She previously served as Vice President of Product Marketing at Primer AI, a leading AI defense technology company.
Savita’s deep expertise spans data management, AI/ML, natural language processing (NLP), data analytics and cloud services across IaaS, PaaS and SaaS models. Her career includes influential roles at renowned technology companies such as Oracle, SAP, Sybase, Proofpoint, Oerlikon, and MKS Instruments.
She holds an MBA from Santa Clara University and a master’s degree in electrical engineering from the New Jersey Institute of Technology. Passionate about giving, Savita serves on the board of Conard House, a Bay Area non-profit providing supportive housing and mental health services in San Francisco.
Meet Savita Raina
