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Companies are having a hard time using AI, but they are finding ways to fix the problems.

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  • Many companies face difficulties using AI, but solutions are emerging. Research shows 87% of data leaders struggle with limited AI use, but experts offer hope.
  • To overcome AI challenges, start with a clear purpose, focus on small wins, celebrate successes, and use data to prove the value.
  • People’s fear of job loss and uncertain regulations hinder AI adoption, but building strong data foundations is key to progress.

Artificial Intelligence (AI) has been a buzzword in the business world for years, promising transformative benefits. However, research indicates that many companies are struggling to harness its full potential. According to the Data Maturity Index by Carruthers and Jackson, a staggering 87% of data leaders report limited AI usage within their organizations, with only 5% having achieved high AI maturity. Despite these challenges, there is hope on the horizon, as experts offer solutions to overcome AI inertia.

The state of AI adoption

In recent years, AI has emerged as a game-changer for businesses. Its potential for automation, data analysis, and decision-making has generated immense interest. However, the reality on the ground tells a different story. A substantial 87% of data leaders reveal that AI is either sparingly used or not utilized at all within their organizations, according to Carruthers and Jackson’s Data Maturity Index.

This pervasive issue has been dubbed “AI-induced paralysis.” It stems from the challenges companies face when justifying, governing, and integrating AI into their operations. Achieving a high level of AI maturity, establishing AI departments, or implementing clear AI processes remains a distant goal for most.

Finding purpose

Caroline Carruthers, CEO at Carruthers and Jackson, suggests a path forward for organizations looking to break free from AI inertia. She emphasizes four key priorities:

1. Starting with purpose

Carruthers underlines the importance of having a clear purpose when venturing into AI. Organizations should identify the specific problems they aim to solve, the opportunities they wish to seize, and what excites them about AI. Without a purpose, they risk meandering aimlessly.

2. Focusing on targeted outcomes

Rather than attempting to tackle grandiose challenges, Carruthers advises organizations to begin with smaller, manageable problems. By concentrating on the smallest part of their purpose where they can make a difference, they can pave the way for future successes.

3. Celebrating successes

One major stumbling block in AI adoption is the reluctance of data professionals to tout their achievements. Carruthers encourages organizations to change this narrative. They should actively promote the positive results of their AI initiatives within the company, inviting others to join the journey.

4. Proving the case with data

To gain buy-in for more AI adoption, organizations must provide concrete evidence of success. Carruthers advocates showcasing the results of AI projects, demonstrating their effectiveness and value. This data-driven approach helps build the case for expanding AI initiatives.

The challenges of AI adoption

Two significant hurdles are slowing down the widespread adoption of AI in organizations:

1. The people problem

One of the foremost challenges in AI adoption is convincing employees at all levels of its value. Many associate AI with job displacement, fearing its impact on the workforce. Overcoming this inherent resistance is no easy task, even in the face of the rapid growth of AI technologies.

2. The regulatory bind

Regulatory concerns also play a significant role in the hesitancy to fully embrace AI. The Carruthers and Jackson research indicates that executives are rightly concerned about data ethics and potential, yet undefined, data laws. This regulatory uncertainty leads many companies to adopt a wait-and-see approach, postponing their full engagement with AI.

Building solid foundations

The research findings underscore the necessity of establishing strong foundations for AI adoption. A robust data strategy and data governance framework are crucial elements for understanding the implications and benefits of AI adoption.

Despite the challenges, some organizations are making strides in preparing for AI adoption. Andy Moore, Chief Data Officer at Bentley Motors, is among those leading the way. He has crafted an enterprise-wide data strategy anchored in four core pillars:

1. Governance

A clear governance framework ensures that data is managed effectively, setting the stage for AI initiatives.

2. Data cloud

Bentley’s technology stack, known as the data cloud, provides the infrastructure necessary for AI implementation.

3. Data dojo

An internal data literacy program, the data dojo, equips employees with the skills needed to navigate the AI landscape.

4. Enablement

Enablement focuses on facilitating collaboration between the data team and the rest of the business, ensuring seamless integration of AI.

Balancing expectations

Moore acknowledges the eagerness for AI but stresses the importance of setting realistic expectations. He understands the need to establish strong foundations before diving into AI initiatives fully.

While AI adoption faces challenges, organizations can overcome inertia by focusing on purpose, targeting specific outcomes, celebrating successes, and presenting data-driven evidence. Addressing the “people problem” and navigating the uncertain regulatory landscape are essential steps. Building strong foundations through data strategies and governance is critical. Progress is possible, as exemplified by companies like Bentley Motors, which are paving the way for AI integration while maintaining a balanced approach. AI may still be in its infancy for many, but with purpose and strategic planning, organizations can unlock its transformative potential.

Warning: The information provided is not trading advice.

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