Decision Intelligence Culture: A 7-Step Plan for Businesses

We explain how you can develop a decision intelligence culture in your company in 7 steps and how you can set the first milestone of your transformation.

Thorsten Heilig

CEO & Co-Founder, paretos

The implementation of Decision Intelligence (DI) provides companies with a competitive advantage and greater profits, but just setting up the technological infrastructure is not the end of the story. Discover how you can develop a Decision Intelligence culture within your company in seven steps to reach the first milestone of your transformation.

The sheer volume of an organization’s complex data that is collected nowadays makes it impossible to keep evaluating and utilizing it in the traditional way. Every company needs to optimize its business decisions and so turn to DI to boost their operations to new levels – from the production floor up to the board of directors. AI technologies have the ability to complement the ‘flawed’ approach of human decision-making.

Why humans are unable to decide objectively

A good decision is based on three main elements:

  • It is effective and addresses a solution to an underlying problem.
  • It is made at the right time.
  • It serves the cause and takes into account all available information, the particular context and all stakeholders.

However, when the human aspect comes into play it’s difficult to make decisions without bias.

  • The biases that influence our decisions are based on the skills, personal preferences and experiences of each stakeholder.
  • The approach to problem solving and the understanding of the impact each decision has on multiple stakeholders – from colleagues to business partners – differs from person to person.
  • Whether and to what extent all available data and insights are accessible plays a major role in the quality of a decision. If employees do not have an overview of whether decisions are strategic or tactical, and if they cannot understand how one influences the other, they will not be able to draw sufficient conclusions from the data analysis.
  • When huge amounts of data is analyzed manually it is extremely difficult, if not impossible, for a human to react appropriately to changing circumstances in terms of time.

The gap between theoretical perfection and human imperfection can be closed by DI. It is an Artificial Intelligence (AI) discipline that, thanks to Machine Learning, is able to analyze data, evaluate events, make predictions and recommend courses of action. Based on these processes, all stakeholders are able to understand how decisions are made and how the results are evaluated, managed and improved through feedback. The motivating factor is obvious: automated decisions can significantly shorten decision-making processes within your organization – which ultimately saves costs and makes your business more competitive in the long term.

The two pillars that enable the use of DI

Restructuring your decision-making processes requires a variety of changes. If you want your business to utilize Decision Intelligence successfully, two organizational pillars are crucial for sustainable deployment:

  • Organizational Capabilities: creating prerequisites within the organization
  • Decision Culture: establishing a DI mindset
Decision-Intelligence-Kultur Decision Intelligence Culture Infographic
Decision Intelligence Culture (paretos, 2022)
Say goodbye to old structures

Without a corporate environment that engages employees in the use of DI and gives them room to explore and share knowledge, the implementation of sustainable DI processes will probably fail. It is essential to delegate responsibilities to allow every employee to participate in the decision-making process at their necessary junctures.

The foundations for this are laid at the highest management level – your DI culture will stand or fall with your courage and willingness to establish and promote a new mindset and to achieve this you must build trust in the use of AI technologies. Appoint ambassadors to educate your teams about the benefits of AI-driven data analytics and empower them to apply it step by step optimally and sustainably in different application areas.

In an organization where data-driven decision-making practises have been established, decisions are no longer made just at a high hierarchical level – IT and data experts no longer rule over data analyses and strategically important insights alone. Employees of all departments must take on more responsibility. The foundations for a company’s decision-making culture include its values, the desire for transparency in decision-making and the culture of continuous improvement.

This is what the DI culture looks like

In a company that recognizes the value of its data, every employee – from business analysts, sales managers and HR professionals to production managers – are able to make informed decisions based on data. A culture that welcomes critical questioning and curiosity will pave the way for the use of data-driven decisions to become the norm, not the exception. Individuals at all levels develop their data literacy via hands-on work and application when given space to experiment in the early days.

A self-service model is recommended as a basis in which employees can access and evaluate all the data they need whilst taking security and governance aspects into account. The final step is active advocacy from leadership, a community that supports and implements data-driven decisions and successful pilot projects in small teams that spur others to follow suit.

Hierarchies that cement decision-making authority at certain levels must also be softened so as not to hinder the decentralized nature of DI practice. The mindset shift begins at the top.

7 steps to establish a DI culture

In order for the three dimensions – people, processes and technology – to interact efficiently at all levels, the expansion of a classic decision culture is an absolute necessity. By adopting the following steps and measures, you can lay the foundations for a DI culture within your company:

  1. Relevance, transparency and resilience are the most important components
    It is imperative that you implement DI in a way that supports its impact and longevity. Transparency is the key. Focus on the sustainability of cross-enterprise decisions by building models based on principles that are traceable, replicable, relevant and trustworthy.
  2. Rely on DI ambassadors early on
    Include well in advance all stakeholders who will be directly effected by the new decision-making processes. Get feedback from your managers. Educate them from the beginning on the functions and benefits of the new decision models that will automate all steps of your future decision-making based on data using self-learning algorithms. After all, if managers understand the models they will be better able to convince other company employees. Also establish DI ambassadors within individual teams to promote the new DI culture with knowledge and commitment.
  3. Goals and global impact must be known to all
    The focus of a sustainable DI culture within your company should be on the ‘big picture’ – global impact. This is because both macro and micro decisions affect the entire company and all stakeholders who act in and with it. For example, the launch of a new product is a macro decision that naturally impacts multiple departments, but even a micro decision, like changing the main message on a website, needs to be assessed for its global impact. Leaders should ensure that colleagues and direct reports keep the global outcome in mind when working with decision-making models. The idea of achieving a global outcome is that even highly localized decision models always contribute to the big picture.
  4. Enhance the competencies of your employees
    Adopting a data-driven approach to decision-making means that people skills need to be aligned with this goal. So in addition to core job skills there is now a need for understanding data-centric functions (analytics, data science, information management, etc.) as well as knowledge of how to use and support the technology infrastructure.  Design training and professional development opportunities enable employees to gain data literacy and increase their analytics quotient.
  5. Comprehensible and responsive DI technology infrastructure
    Reduce the complexity of your technological infrastructure to enable access by all stakeholders. It’s especially important that your tools provide two-way communication so that you can consistently and automatically provide feedback on the data and refine automated models.
  6. Monitor decision-making scope collaboratively
    DI transforms information into action in a scalable way that pays off in terms of business goals so it’s necessary to constantly monitor how the new processes and decision-making capabilities of employees are impacting on your business. Reinforce successful approaches and work with others within the organization to adapt and improve processes as and when needed.
  7. Use SaaS models: Quick start through relevant technology
    With paretos, for example, companies get the opportunity to tackle their smart data offensive independently and without high costs. The AI-based software is a game-changer for data analysis. The Heidelberg-based tech startup makes analytics processes as accessible and integrable for businesses as an email program. With the help of a Software-as-a-Service (SaaS) tool, companies ranging from startups to midsize businesses to large corporations can perform comprehensive data analyses without prior knowledge or the expertise of data science specialists.

Thorsten Heilig

CEO & Co-Founder, paretos

Technology is crucial to solve complex challenges and help scaling businesses – from accelerating business growth to mastering agile transformation or managing change.

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