In the foreseeable future, it is expected that every major organization will embark on an AI transformation journey. Being said at Sartify LLC we are #StandOut to help them out. Having assisted numerous companies in their modernization efforts for AI, we have observed significant distinctions between those who achieve success and those who do not. 

This article highlights three prevalent patterns of AI transformation as done by most organizations that have proven ineffective or being failure approach . 

The primary reason for the failure of AI transformation lies in the endeavor to implement significant organizational changes gradually by relying on existing personnel and processes. 

1. FAILURE APPROACH ONE : JUST BUILD DATASCIENCE TEAM

The initial failure pattern is frequently observed among midsize companies with limited technology infrastructure and small IT teams. Such companies often have a small group of team members with the title of "data scientist" who predominantly engage in data analysis for marketing, constructing dashboards, or performing traditional statistical tasks like actuarial modeling. 

When such organizations embark on an AI transformation journey, they typically establish an isolated data science team to spearhead the process. The team is usually assigned the responsibility of creating a model to address a business problem. 

However, this approach is inadequate since AI transformation necessitates more significant changes than simply constructing models. The data scientists in such cases typically receive inadequate direction, feedback, or engineering support to effectively execute their tasks. Consequently, after a year or two, minimal progress is achieved, and the top data scientists leave the organization.  

2. FAILURE APPROACH TWO : UPDATING THE EXISTING TECHNOLOGY LEADERSHIP STRUCTURE

The second failure pattern typically begins on a promising note, with the C-suite or board mandating an AI transformation. Unfortunately, executive buy-in alone is insufficient. Given the high cost of recruiting new talent, leaders attempt to execute the transformation by leveraging the existing company structure. Teams receive more substantial budgets for AI projects and hire a few engineers. 

However, this approach fails for the same reasons that most corporate innovations falter ): 

• The organization lacks the appropriate technology workforce.
• When more immediate problems arise, AI takes a backseat.
• The teams attempt to manage risk by rigidly adhering to agile methodologies (Agile methodologies won't work in AI field).
• The involved teams hold numerous meetings and develop roadmaps and plans but take little action. Any action taken is usually incremental work on existing projects.

In the best-case scenario, leadership recognizes the problem early and begins to make changes. However, in most cases, key talent leaves for better opportunities, and the program never progresses beyond incremental improvements as the years tick by. The program never makes more than incremental improvements . 

3. FAILURE APPROACH THREE : BEING THE LONE-WOLF AI LEADERSHIP STRUCTURE

AI transformation leaders at various companies typically come from diverse backgrounds, such as innovation, technology, or an existing line of business. However, despite their different origins, they share a common vision for the potential of AI to transform every business. 

These leaders assume the role of AI evangelists for their organizations. In failure approach 3, the management provides the AI evangelists with limited resources to commence the AI transformation. As a result, the AI leader is only able to hire a few team members and recruit part-time talent from within the organization. 

Regrettably, the difficulty of implementing AI far exceeds the available resources. The AI leader struggles to compete for the attention of the company's most capable engineers. Product teams are unable to assist much due to pre-existing commitments. Meanwhile, the rest of the company remains unconvinced about the need for change. Consequently, the AI leader is frequently engaged in unproductive meetings about topics like AI ethics and model governance, long before the team deploys a solution. 

The AI leader expresses dissatisfaction with the situation, but the company is hesitant to invest more until the team demonstrates a return on investment for the AI initiative. The absence of dedicated teams and business-partner commitment results in limited progress. Eventually, key personnel departs, and the project comes to an end . 

BIG CHANGES REQUIRES BOLD MOVES ) :

We are currently witnessing the dawn of a transformative technological shift that promises to bring about far-reaching changes. As with the advent of the internet, computers, and electricity in previous eras, AI is poised to revolutionize businesses in our time. 

Attempting to initiate an AI transformation using the same organizational structure with agile methodology as before would be akin to devising an electricity strategy based on gas lamps. Big changes require bold and decisive moves. . 

At Sartify LLC, we beleive AI innovations is of it's different since major changes pose big risks. They require strong leadership, culture changes, new team members, and a vision. Few organizations make the necessary commitment until it’s too late.

One of success pattern at Sartify LLC we help our clients with, is to think big, start small, build a great team, get traction, and then scale.

Need real digital transformation with AI, but don’t where to start or have already started but don’t see the value or need experienced artificial intelligence professionals to execute, let’s talk
info@sartify.com