Avoid the AI siren song[1]. Avoid the advice that leads you to believe an artificial intelligence (AI) project is just like any other IT project and that the approach you used for your ERP / MRP / BFA / CRM implementations will work here. Be cautious of the “start small” advice. Instead, think:
Start small, but start small and strategic, not small and random.
AI projects require significant data, technology, people skills, and culture investments to succeed. That means you will need the senior management support and fortitude to support those investments and stay the course as your organization learns to apply AI to derive and drive new customer, product, service, and operational value sources.
However, building senior management support (and the necessary budget) will NOT happen by targeting random use cases. Senior management wants to prioritize their investments in initiatives that deliver substantial, quantifiable business and operational value. Senior management wants to focus on something strategically crucial to the business. Senior management wants to focus on strategic business initiatives (Figure 1).
Figure 1: Strategic Business Initiatives
Understanding Your AI Investment Requirements
Yes, the future of AI is bright (grab them sunglasses!). However, organizations must be prepared to invest in their data, technology, people, and cultural capabilities to create an AI strategy that drives business and operational success.
1) Data. Investing in data is crucial for the success of your organization. This includes managing, governing, and ensuring your data’s quality, granularity, latency, and enrichment. The accuracy, reliability, and timeliness of your AI models depend entirely on your data’s accuracy, reliability, and timeliness. However, it’s essential not to apply data management and governance indiscriminately. Instead, take the time to understand which data sets are most relevant and valuable for achieving your organization’s business and operational goals. Then, focus your data management and governance investments on the data sets that support the achievement of those strategic business initiatives.
2) Technology. Organizations must be prepared for investments in new data management, data engineering, and analytics processing technologies. Technology is not my space, but check out this AI design guide from Dell Technologies for more information about the technology requirements to support your AI journey.
However, I do understand analytics and how to apply analytics to create new sources of business and operational value. The emergence of autonomous analytics – analytics that can learn and adapt with minimal human intervention – will transform every aspect of society, including enhancing the quality of healthcare, tackling environmental challenges, addressing transportation safety and bottlenecks, accelerating manufacturing excellence, fostering social and economic equity, and much more.
Autonomous Analytics is often based on reinforcement learning (RL) that learns from experience and feedback. For example, Reinforcement Learning (RLHF – Reinforcement Learning with Human Feedback) enables Generative AI products such as ChatGPT, Bing AI, and Google Bard to learn and adapt from human interactions.
3) People Skills. Data science is a team sport comprised of Data Engineers, Data Scientists, and Business Stakeholders. The first key to developing your people skills is applying a common framework, like the “Thinking Like a Data Scientist” framework, around which people’s skills can be evaluated and developed (Figure 2).
Figure 2: The Art of Thinking Like a Data Scientist Methodology
The other key to developing your people skills is clearly articulating the roles, responsibilities, and expectations of the Data Engineers, Data Scientists, and Business Stakeholders to deliver more relevant, meaningful, responsible, and ethical business and operational outcomes.
4) Cultural Transformation. Even if we have a solid data management strategy and capability, modern and extensible technology capabilities, and appropriately skilled people…, we still have nothing unless we have cultural empowerment.
Cultural empowerment means embracing ambiguity, diversity, collaboration, experimentation, and learning from failures.
The force multiplier for AI success is cultural empowerment, which includes:
- Personalize the Organization’s Mission by ensuring everyone is connected to the organization’s mission and understands how their role contributes to the success of that mission.
- Speak the Language of Your Customers by using their language to describe their aspirations, desired outcomes, needs, and challenges.
- Foster Organizational Improvisation by embracing a culture of experimentation and agility, where employees are encouraged to try new things, learn from mistakes, and adapt quickly to changing circumstances.
- Embrace an “AND” Mentality where different perspectives and approaches are complementary rather than conflicting, fueling the drive toward innovation.
- Ensure Everyone Has a Voice by amplifying voices that might otherwise be unheard, creating safe spaces for open dialogue, and encouraging dissent.
- Unleash the Curiosity-Creativity-Innovation Pyramid that fosters a culture of learning, exploration, and invention, encouraging experimentation that fuels creativity, allowing individuals to connect seemingly disparate ideas, and forge new paths leading to breakthrough advancements.
To embark on a successful AI journey, your organization needs to invest in four key areas: data, technology, people skills, and cultural transformation. These areas are interdependent and mutually reinforcing and need to be aligned in lockstep with your strategic vision and goals. Data is the fuel for AI, technology is the engine, people skills are the drivers, and cultural transformation is the road map. By investing in these areas, you can unleash the power of AI to create value, innovate, and transform your organization (Figure 3). 🚀
Figure 3: Why Data Management is Today’s Most Important Business Discipline
Start Small and Strategic – Part 1: Summary
These areas of data, technology, people skills, and culture require substantial investments in time, money, and patience to nurture and grow AI’s transformational power. Something with that sort of investment and potential should be targeted at something important to your organization – a strategic business initiative. In Part 2, we will deep-dive into strategic business initiatives and their supporting (tethered) use cases.
[1] A siren song is an idiom that refers to something alluring and tempting but is ultimately dangerous, deceptive, or destructive.
