A Framework for Value Creation and Risk Mitigation with AI

February 15, 2024

The Pitfalls of Unrealistic Expectations and Inadequate Preparation

A major reason for AI project failure is unmet expectations. Organizations often overestimate the capabilities of AI or underestimate the complexity of implementation. This can lead to a disconnect between desired outcomes and achievable results. Additionally, a lack of data foundation can significantly hinder AI project success. Robust data pipelines are essential for training and maintaining AI models. Insufficient data quantity or quality can lead to inaccurate models and unreliable results.

The Allure of Feature FOMO and Solution Shopping

The rapid pace of AI innovation can lead to impulsive decision-making driven by feature FOMO. Organizations become captivated by the latest AI advancements, their features, and functionalities, without a clear understanding of how these advancements align with their core business needs. This can result in investments in AI solutions that boast impressive features but lack the necessary integration with existing processes or the data infrastructure to function optimally.

Furthermore, organizations can fall prey to solution shopping, seeking pre-built AI solutions without a deep understanding of their own specific business challenges. This approach often leads to poorly-fitting solutions that require extensive customization or fail to deliver on promised benefits.

A Framework for Value Creation and Risk Mitigation

To protect your AI strategy and maximize the probability of success, consider the following framework, incorporating insights from data governance and brand considerations:

  1. Define a Value-Driven AI Strategy:
    • Conduct a thorough business needs assessment to identify areas where AI can deliver the most value. Avoid feature FOMO and solution shopping by focusing on strategic alignment with core business goals.
    • McKinsey research suggests that successful AI projects are three times more likely to be aligned with the organization's overall strategy.
    • Set clear and measurable success metrics that consider both human and business impacts. This might include metrics for employee productivity gains, customer satisfaction improvements, or cost reductions.
  2. Prepare a Robust Data Foundation:
    • Invest in building a comprehensive data infrastructure to collect, store, and manage data effectively.
    • Ensure data quality and consistency to avoid model bias and inaccurate results.
    • Develop a data governance framework to ensure data security, privacy compliance, and responsible AI use. This includes establishing clear ownership and accountability for data quality.
  3. Manage Change for Adoption Success:
    • Communicate the benefits of AI to all stakeholders, including employees, customers, and partners.
    • Develop a change management plan that addresses potential workforce disruption and fosters buy-in. Highlight how AI can augment human capabilities and create new opportunities.
    • Build data fluency within your teams to empower them to understand AI processes and contribute effectively.
    • Encourage responsible experimentation with AI while setting clear guardrails to protect data, maintain work quality, and deliver on business promises.
  4. Create a Governance Plan:
    • Clearly define your brand voice and document brand governance guidelines. This ensures consistent brand representation across all customer touchpoints, including those involving AI.
    • Train AI models on your specific brand data and preferences. This allows the AI to understand and emulate your brand's unique tone and style.
    • Document common customer journeys and interactions to empower AI to handle inquiries effectively. This personalizes the customer experience and fosters trust.

Adopt a Strategic Approach to AI Implementation

By adopting a strategic approach that addresses these critical factors, organizations can significantly increase the likelihood of success with their AI initiatives. Remember, AI is a powerful tool, but it's critical to have a clear vision for how it will integrate with your existing business processes and workforce. Through careful planning, data preparation, and a human-centered implementation strategy, organizations can unlock the true potential of AI and achieve sustainable competitive advantage.

Avoiding Feature FOMO and Solution Shopping

The constant stream of new AI advancements can be exciting, but resist the urge to be swayed solely by novelty or impressive features. Focus on aligning technology with your existing business goals and processes for a more sustainable approach to AI implementation. Don't chase fleeting trends or pre-built solutions that don't address your specific needs. Instead, adopt a data-driven and value-centric approach to unlock the true potential of AI and achieve long-term success.



Disclaimer: This article was created with the assistance of AI technology using tools such as ChatGPT, Gemini, Claude, WordTune and Quillbot. We use AI as part of our proof of concept and to gain practical experience with these content tools. While AI can provide valuable insights and aid in content creation, the views, opinions, and information presented in this article are solely those of TechSense Solutions, Inc.

Pamela L. King


Pamela L. King offers compelling keynotes, panels, workshops, and briefings, blending expertise and enthusiasm for technology-driven progress.

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  • Human-Centric AI and Workforce Empowerment
  • Practical AI in Business: Applications and Insights
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