Avoid the siren song of AI[1]. Avoid advice that would make you believe that artificial intelligence (AI) projects are like any other IT project and that the approach you used to implement ERP/MRP/BFA/CRM will work here. Be wary of the advice to “start small.” Instead, think of it like this:
Start small. But start small and strategic, not small and random.
Successful AI projects require significant investments in data, technology, people skills, and culture. This means supporting these investments and staying on course as your organization learns how to apply AI to unlock and drive new customer, product, service, and operational value sources. This means that you will need fortitude.
However, you can’t build senior management support (and the necessary budget) by targeting random use cases. Senior management wants to prioritize investments in initiatives that deliver real, quantifiable business and operational value. Senior executives want to focus on what’s strategically important to the business. Senior management wants to focus on strategic business initiatives (Figure 1).
shape 1:Strategic business initiatives
Understand your AI investment requirements
Yes, the future of AI is bright (bring your sunglasses!). However, organizations must be prepared to invest in data, technology, talent, and cultural capabilities to create an AI strategy that drives business and operational success.
1) Data. Investing in data is critical to an organization’s success. This includes managing, managing, and ensuring data quality, granularity, latency, and richness. The accuracy, reliability, and timeliness of AI models are entirely dependent on the accuracy, reliability, and timeliness of the data. However, it is important that data management and governance not be applied indiscriminately. Instead, take the time to understand which data sets are most relevant and valuable to achieving your organization’s business and operational goals. Next, focus your data management and governance investments on data sets that support achieving your strategic business initiatives.
2) Technology. Organizations must be prepared to invest in new data management, data engineering, and analytical processing technologies. Although technology is not my area of expertise, please refer to Dell Technologies’ AI Design Guide for more information on technology requirements to support your AI efforts.
However, I understand analytics and how to apply it to create new sources of business and operational value. The advent of autonomous analytics (analytics that can learn and adapt with minimal human intervention) is helping improve the quality of healthcare, address environmental issues, address transportation safety and bottlenecks, and improve manufacturing excellence. transform all aspects of society, including accelerating economic growth and promoting social and economic equity. etc.
Autonomous analytics is often based on reinforcement learning (RL), which learns from experience and feedback. For example, reinforcement learning (RLHF – reinforcement learning with human feedback) allows 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 made up of data engineers, data scientists, and business people. The first key to developing interpersonal skills is to apply a common framework, such as the “Think Like a Data Scientist” framework. Based on this, people’s skills can be assessed and developed (Figure 2).
shape 2: Techniques to think like a data scientist methodology
Another key to developing people skills is to develop data engineers, data scientists, and business relationships to deliver more relevant, meaningful, responsible, and ethical business and operational outcomes. clearly articulate the roles, responsibilities and expectations of employees.
4) Cultural transformation. Even if you have a solid data management strategy and capabilities, modern and scalable technology capabilities, and the right people with the right skills, you won’t get anywhere without cultural empowerment.
Cultural empowerment means embracing ambiguity, diversity, collaboration, experimentation, and learning from failure.
Cultural empowerment drives AI success, including:
- Personalize your organization’s mission By connecting everyone to the organization’s mission and understanding how their role contributes to the success of that mission.
- speak the customer’s language Use their language to explain their aspirations, desired outcomes, needs, and challenges.
- Facilitate organized improvisation This is achieved by embracing a culture of experimentation and agility that encourages employees to try new things, learn from mistakes and quickly adapt to changing circumstances.
- Accept the idea of “AND” Here, different perspectives and approaches complement each other rather than conflict, increasing the drive for innovation.
- let everyone have a say By amplifying voices that might not otherwise be heard, creating a safe space for open dialogue, and encouraging opposing views.
- Unlocking the pyramid of curiosity, creativity, and innovation It fosters a culture of learning, exploration and invention, encouraging experimentation that stimulates creativity and allows individuals to connect seemingly disparate ideas and forge new paths that lead to breakthrough advances.
For an AI initiative to be successful, organizations need to invest in four key areas: data, technology, people skills, and cultural change. These areas are interdependent and mutually reinforcing and must be aligned to your strategic vision and goals. Data is the fuel for AI, technology is the engine, people skills are the driving force, and cultural transformation is the roadmap. By investing in these areas, you can unleash the power of AI to create value, innovate, and transform your organization (Figure 3). 🚀
shape 3: Why data management is today’s most important business discipline
Start small and strategically – Part 1: Summary
These areas—data, technology, people skills, and culture—require significant investments in time, money, and patience to nurture and grow the transformative power of AI. These investments and possibilities are important to the organization, i.e. strategic business initiatives. In Part 2, we dive deeper into strategic business initiatives and supporting (connected) use cases.
[1] Siren’s song is an idiom that refers to something that is alluring and alluring, but ultimately dangerous, deceptive, or destructive.
