Is your organization exhibiting “AI fever”? “Symptoms” include people asking questions like:
- “How can our organization take advantage of AI-enabled capabilities?”
- “Where can we leverage AI to automate our workflows?”
- “How can we use AI to improve efficiency and reduce costs?”
- “What happens if our competitors beat us to the market with an AI-based solution?”
These are legitimate questions. Effective adoption of AI-capabilities is changing the way that companies work, by driving workflow efficiencies and automation, providing data analysis and insights that weren’t before possible, improving work quality, personalizing experiences, and more.[i] The market is crowded with solutions, all of which make it seem so simple to fully take advantage of AI capabilities. It’s easy for organizations to get lured in.
But the potential results from AI adoption don’t excuse organizations from doing the needed critical thinking before making solution decisions. Before jumping on the AI bandwagon, organizations must answer questions like:
- What problem will AI solve for the organization?
- Do customers want an AI-based solution, and if so, how should it help them?
- Is the necessary foundation in place within the organization to benefit from AI adoption?
- How will AI fit into the “big picture” of the organization?
The big picture must come first
Understanding the big picture must come first before considering any AI solution. Why is having a holistic (or big picture) approach so important, especially with AI adoption?
First, AI adoption can have a significant impact on the entire organization. Many organizations only think of AI as something that will only impact IT or technology, but the impact can be much more than that. AI impacts multiple areas of an organization, and having a holistic approach and strategy ensures that AI initiatives are aligned across all parts of the organization.[ii]
Without taking a holistic approach to AI adoption, organizations risk implementing a point solution within a workstream that is neither sustainable nor enhances organizational efficiency. While there may be local benefits, the actual outcome is that the overall workstream becomes less efficient. Implementing a solution in this way would be like speeding up only the middle of a conveyor belt. While widgets may move along faster on the conveyor belt, the constraints at the beginning and end of the conveyor belt become highlighted and problematic.
Data and ethics are two of the more significant considerations for the effective use of AI. AI needs data – lots of it – and that data must be of high quality and integrity if results are to be trusted and reliable. The use of data also presents ethical considerations, with issues like data privacy, bias, and appropriate use. These factors mean that data quality and data governance are also part of the AI big picture.[iii]
To fully use the capabilities engineered into AI solutions, these solutions typically require training to function effectively. People within an organization must develop skills and competencies to use the solutions. But here’s perhaps an unexpected twist – not only do the people using the solutions require training, but the solution itself requires training as well. These solutions must be trained using the datasets provided by the organization and tailored for organizational-specific tasks and use cases. Having people with the skills and knowledge for data preparation, tuning algorithms, data visualization, and problem-solving are needed for training the AI.
AI adoption challenges
While AI can bring a lot of capabilities to enhance an organization’s performance, it is far from a “magic wand.” Here are a few challenges organizations are facing with AI adoption.
- People. Will people feel threatened by AI adoption? Or will they feel enabled? The best AI capabilities are worthless unless they enhance the ability of people to do their work. Effective and comprehensive organizational change management as part of AI adoption is critical.[iv]
- Good AI will not fix broken processes. Having well-defined processes is the foundational structure for AI adoption. It’s straightforward: bad processes result in poor AI adoption and poor outcomes. Without clearly defined workflows, how can an organization identify valuable use cases or strategically expand AI-enabled capabilities, such as automation, or agentic AI.
- Missing the skills needed for success. This article from ARM highlights the impact of the lack of in-house AI talent is having on AI adoption. Nearly one-half of leaders feel that the lack of skilled talent is a primary barrier to successful AI implementation.
How to treat AI fever
Do you have that big picture view of your organization? Is your organization prepared to take advantage of the right AI capabilities that elevate organizational performance, efficiency, and capability? Now is the time to get ready. Here are four tips for ensuring a holistic approach to AI adoption.
- Map your value streams. One of the most significant challenges to adopting and exploiting AI capabilities is that organizations don’t understand their value streams, much less have mapped those value streams. Value stream maps depict the big picture of how work and value flow through an organization. It also identifies where constraints may be within the organization. With this holistic understanding of its value streams, organizations can better figure out if AI solutions will enhance performance…or enhance constraints.
- Focus on ongoing learning and development. “Training as an event” cannot keep pace with the rapid changes in AI capabilities. The rapid pace of AI enhancements demands a continual learning approach to training and development. Adaptive learning is an educational approach that tailors educational experiences to meet an individual’s unique needs, skills, and learning pace, facilitating a continual learning experience.[v]
- Invest in your current staff. The best source of knowledge about how work is currently done is the people that are doing the work. This is the exact knowledge that is needed to train AI. Invest in reskilling or upskilling current employees in AI concepts and technologies, enabling them to take on new career opportunities that will emerge with AI adoption.
- Answer the “why.” Building a business case not only answers how and why AI adoption will address a business challenge, but it also drives the needed commitment from senior management for making it happen.
AI capabilities are evolving at a dizzying pace. Having a holistic or big picture view of the organization is the best way of ensuring that your organization can take advantage of the right AI solutions at the right time to deliver the right results.
[i] “Artificial Intelligence is changing how companies work”, https://www.deloitte.com/ch/en/Industries/technology/perspectives/artificial-intelligence-Is-changing-how-companies-work.html , retrieved April 2024.
[ii] “Why adopting AI needs a holistic approach”, https://betanews.com/2025/02/03/why-adopting-ai-needs-a-holistic-approach-qa/ , retrieved April 2024.
[iii] Ibid.
[iv] “Adopting AI-driven Change Management: Key strategies for Organizational Growth”, https://voltagecontrol.com/articles/adopting-ai-driven-change-management-key-strategies-for-organizational-growth/ , retrieved April 2024.
[v] “Understanding Adaptive Learning: How AI is revolutionizing personalized education”, https://elearningindustry.com/understanding-adaptive-learning-how-ai-is-revolutionizing-personalized-education , retrieved April 2024.
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