Become an AI-fueled Organization

One of the most frequently cited leading practices for AI transformation is an organization's need for a bold, enterprise-wide strategy. This process is transforming rapidly, but not entirely. Few organizations can claim to be fully AI-powered, but an increasing percentage has begun to exhibit behaviors of being an AI-driven organization. This sprawling corporate experience raises our belief that meaningful developments and impacts can occur over the next few years. This is valid for organizations starting to use AI to solve some of their most critical and challenging business challenges.

AI capabilities have advanced significantly, from being an app that often tells employees what to do or points out their mistakes. Thanks to the power and speed of cloud-based data hosting and computing, it matured to serve as a co-pilot, independently executing emerging insights and trends.

Today, some supply chains are self-managed. Algorithms can independently balance financial portfolios. Support centers can know customers' problems before they call. Cumulatively, with these developments, businesses will be freed from time constraints by predicting human behavior. Organizations lagging behind in AI talent development will therefore be at risk of losing their competitiveness in the not-too-distant future. No matter where organizations find themselves in the journey of becoming an AI-supported organization, they should know that they will encounter significant challenges in this transformation process.

Key Profiles

AI-powered organizations use data to systematically deploy and scale AI into core business processes to be human-centered. They use the power of data-driven decision-making to gain a competitive advantage and constantly innovate. Being an AI-powered enterprise requires understanding that the transformation process is never complete but rather a journey. This process goes through continuous learning and improvement. An organization's AI maturity can be profiled based on the number of applications deployed and the results achieved. How organizations around the world are moving towards becoming AI-driven companies, and how many types of AI applications a company has implemented at full scale. An organization's AI maturity can be profiled based on the number of applications deployed and the results achieved. As a result of these analyzes, four main profiles can be mentioned.

Transformers

This group, which has transformed but not wholly transformed, has identified and widely adopted leading practices associated with the most substantial AI outcomes. Transformers are considered the "AI-powered" leading group on their way to becoming AI-powered organizations.

Pathseekers

This is the profile that shows types of AI applications that have achieved a high level of output but are distributed at a low level. Path seekers have adopted the skills and behaviors that lead to success. But it makes fewer attempts on the subject. They're ventured, but they don't scale to the same degree as Transformers.

Underachievers

Significant development and distribution activities characterize this group. However, it can be said that they did not adopt enough pioneering practices to help them achieve meaningful results effectively.

Starters

A late start to building AI capabilities characterizes this group. They are least likely to exhibit prominent application behaviors. They are developing or researching different types of AI deployments and have achieved highly underrated results through their AI initiatives.

Analysis of these groups revealed the behaviors most associated with solid outcomes. They are classified by the categories of Strategy, Operations, Culture and change management, and Ecosystems. Success is built on the foundation of a clear strategy that is communicated and encouraged by the highest leadership. Operations and culture plus change management are identified as leaders in this classification, and their collaboration is essential.

What to do Differently to Drive Success?

To be a core leader practice, first and foremost, leaders need to define a clear corporate strategy in which to use AI capabilities to create opportunities and competitive advantages. Establishing an enterprise-wide system by setting a bold vision increases the likelihood of achieving high-grade results. Companies that seek out ways to help AI achieve differentiated strategies will continue to believe in their unique ideas. Determining the use of AI will also significantly improve competitiveness. As you implement your plan, approach transparency, your workforce, information about your market strategy, and trade-offs. In this process, there are also pitfalls that institutions should avoid. Waiting for your current data scientists and IT team to create an AI strategy from scratch. Build solid business strategies by hiring or consulting senior business leaders and experts. In addition, adopt innovation-oriented perspectives by balancing your productivity goals with growth.

If we take a look at the most powerful AI strategies, we'll see that they tend to start with no mention of AI. These strategies should begin with the organization's north star. Basic business strategy. Afterward, the process should be advanced in close collaboration with the relevant leaders in all business divisions and the focal point of employees at all levels. In this respect, In the early 2010s, Jeff Bezos mandated Amazon to plan how they would use AI and machine learning (ML). This imperative led to unparalleled innovation. It counts as a step forward as the catalyst for Amazon's rise to become an AI leader today.

If we take a look at the most powerful AI strategies, we'll see that they tend to start with no mention of AI. These strategies should begin with the organization's north star. Basic business strategy. Afterward, the process should be advanced in close collaboration with the relevant leaders in all business divisions and the focal point of employees at all levels. In this respect, In the early 2010s, Jeff Bezos mandated Amazon to plan how they would use AI and machine learning (ML). This imperative led to unparalleled innovation. It counts as a step forward as the catalyst for Amazon's rise to become an AI leader today.

Looking at the current examples, many of the most powerful AI strategies start out in similar ways. They can identify gaps by directing clear goals to business leadership. Evaluate the opportunities in each department and make it a priority to implement AI as a solution. With the combination of all these, mutual goals and initiatives will be aligned with the primary business strategy, and their interoperability will increase.

Leading Practices

To provide the necessary combination of efficiency and value-creating results with AI, it must be integrated, scaled up and expanded across the enterprise. Over-indexing on productivity can lead to your target getting out of balance and missed opportunities. The scenarios in which organizations are most successful are seen when both efficiency and value creation goals are combined. For this, it shows that higher successful organizations emphasize growth-oriented goals, while less successful organizations focus more on efficiency or cost goals. It is required to pay attention to the art and mindset of the possible, such as creating new products and offers, entering new markets, and increasing customer satisfaction. This allows them to seize and take advantage of opportunities missed by organizations that are over-indexed to productivity or support mundane business. 

In this process, it is necessary to have a diversified portfolio of questions in order to choose the right questions and increase the impact. So you get a longer-term, sustainable impact and show value sooner. For example, making this company's vision public can influence investor perceptions, increase success, and signal to the capital markets and competitive talent market that an organization is investing in a bold future. Besides all these, the most important topic is to stay dynamic. Continue to respond to ever-changing market and technology developments by constantly renewing your AI strategy. And never forget, developing this strategy is not a one-shot effort. As the organization's core strategy and AI capabilities mature, leaders must update objectives and increase the use of competitive differentiators. 

Make Transformation an Everyday Work.

AI involves adhering to a well-calibrated MLOps framework, while the AI lifecycle includes documenting release strategies and updating workflows, roles, and team structures. A successful AI solution must be designed to fit a new workflow to improve value delivery.

Some organizations have made good strides toward becoming an AI-driven organizations by creating new roles to help transition between business stakeholders and model development teams. A knowledgeable person in both business and analytics, he acted as a bridge between the overarching business strategic goals and the technical requirements of artificial intelligence. Thus, these newly created roles and functions became visible in high-achieving companies.

Rethinking operations: a catalyst for AI transformation Establishing appropriate structures, roles, and working relationships across an enterprise could be one of the most essential efforts in bringing an AI transformation to life: getting the organizational structure right and a data-driven one that accepts the use of AI on its journey to becoming an AI-driven company. Creating a corporate culture will be a great accelerator.

Culture and Change Management

Over the past few decades, change in business and technology has accelerated, requiring employees to adapt, continually learn new skills, and have a solid ability to make decisions amid increasing uncertainty. These changes and adaptations have also challenged the cultures of organizations. General components of organizational culture; trust, data fluency, agility.

Trust & Data Fluency

High-achieving organizations have more fear than low-achieving organizations. The focus of this fear is job loss or machines replacing people. To overcome this fear, successful organizations invest heavily in training and change management. From this perspective, fear is a positive indicator that an organization's AI vision is bold. A confident culture shows agility even when it's scared. Here, we can say that trust is based on competence and intention. Essential in this process is raising the basic level of data literacy at all levels of the organization. This builds trust and deeper trust in models and AI, which helps organizations drive positive results. Of course, the model results will continue to be reviewed continuously through live testing and validation.

Agility

AI-powered organizations often do more than rely on data; they demonstrate a willingness to quickly turn insights into action and quick experiments. With investments in AI and digital transformation in general, there is a need to try and learn from failures.

The need for change management AI is dramatically changing the way business is done. This journey of change can be uncomfortable and tiring at first. When we look at the transformers that have been successful here, it is seen that they have invested two times in the change compared to other profiles. A change management program can be said to be poorly designed if it fails to build on the suitable methods to improve its ability to deliver value with the experience of business sponsors and new technology. A powerful program; he spends time identifying all the behaviors he wants to encourage, and these are then used to create a multi-layered communication, education, resources of support, incentives, and nudges. Thus, new norms are created in your culture. Of course, it would be best not to expect a miracle in this process. Because this process, no matter how well designed, proceeds depending on the consent and participation of the employees. Celebrating successes, resolving failures quickly, tracking progress, and setting the correct KPIs are vital to achieving a culture that can drive AI-powered success.

Organizational progress has depended on employees' ability to envision a new vision for the future and identify current opportunities. We are fast approaching the days when artificial intelligence will save the options brought by our limited perspective. For this reason, organizations that are laying the foundations for an AI-powered future now will be rewarded in the near future. Currently, very few organizations in the market have made mature transformations. Many leaders today have a lot of work to do to lay strong foundations. The first is to set a business-wide strategy. Set bold, enterprise-wide goals for AI that align tightly with the core business strategy. Next, update your operations and document new ways of working. Make sure business sponsors take the lead in AI-powered initiatives. Evaluate your culture in the age together. Ensure your workforce has sufficient support to learn new skills, talents, and ways of working through multi-layered change management approaches that target unique audiences. Finally, diversify your ecosystems. Design your ecosystem in such a way that it will always protect and guide your differentiation from the competition and clarify the paths. 

Are you starting to imagine incredible AI achievements for you right now? In this process, Cool Digital’s enthusiastic team is there to take you beyond your limits and bring your ground-breaking ideas to life.