Today, innovation-driven businesses are investing significant resources in artificial intelligence (AI) systems to advance their AI maturity journey. In line with IDC, worldwide spending on AI-centric systems is predicted to surpass $300 billion by 2026, in comparison with $118 billion in 2022.

Previously, AI systems have failed more incessantly as a consequence of an absence of process maturity. About 60-80% of AI projects used to fail as a consequence of poor planning, lack of understanding, inadequate data management, or ethics and fairness issues. But, with every passing yr, this number is improving.

Today, on average, the AI project failure rate has come all the way down to 46%, in line with the newest LXT report. The likelihood of AI failure further reduces to 36% as an organization advances in its AI maturity journey.

Let’s further explore a company’s path to AI maturity, the several models and frameworks it could employ, and the primary business drivers for constructing an efficient AI strategy.

What’s AI Maturity?

AI maturity refers back to the level of advancement and class an organization has achieved in adopting, implementing, and scaling AI-enabled technologies to enhance its business processes, products, or services.

In line with the LXT AI maturity report 2023, 48% of mid-to-large US organizations have reached higher levels of AI maturity (discussed below), representing an 8% increase from the previous yr’s survey results, while 52% of organizations are actively experimenting with AI.

The report suggests that essentially the most promising work has been done within the Natural Language Processing (NLP) and speech recognition domains – subcategories of AI – since they’d essentially the most variety of deployed solutions across industries.

Furthermore, the manufacturing & supply chain industry has the bottom AI project failure rate (29%), while retail & e-commerce has the best (52%).

Exploring Different AI Maturity Models

Normally, AI-driven organizations develop AI maturity models tailored to their business needs. Nonetheless, the underlying idea of maturity stays consistent across models, focused on developing AI-related capabilities to attain optimal business performance.

Some distinguished maturity models have been developed by Gartner, IBM, and Microsoft. They’ll function guidance for organizations on their AI adoption journey.

Let’s briefly explore the AI maturity models from Gartner and IBM below.

Gartner AI Maturity Model

Gartner has a 5-level AI maturity model that firms can use to evaluate their maturity levels. Let’s discuss them below.

Gartner AI maturity model illustration. Source: LXT report 2023

• Level 1 – Awareness: Organizations at this level start discussing possible AI solutions. But, no pilot projects or experiments are underway to check the viability of those solutions at this level.
• Level 2 – Energetic: Organizations are on the initial stages of AI experimentation and pilot projects.
• Level 3 – Operational: Organizations at this level have taken concrete steps towards AI adoption, including moving at the very least one AI project to production.
• Level 4 – Systematic: Organizations at this level utilize AI for many of their digital processes. Also, AI-powered applications facilitate productive interaction inside and out of doors the organization.
• Level 5 – Transformational: Organizations have adopted AI as an inherent a part of their business workflows.

As per this model, firms start achieving AI maturity from level 3 onwards.

IBM AI Maturity Framework

IBM has developed its own unique terminology and criteria to evaluate the maturity of AI solutions. The three phases of IBM’s AI maturity framework include:

IBM AI Maturity Framework Phases

• Silver: At this level of AI capability, enterprises explore relevant tools and technologies to arrange for AI adoption. It also includes understanding the impact of AI on business, data preparation, and other business aspects related to AI.
• Gold: At this level, organizations achieve a competitive edge by delivering a meaningful business consequence through AI. This AI capability provides recommendations and explanations backed by data, is usable by line-of-business users, and demonstrates good data hygiene and automation.
• Platinum: This sophisticated AI capability is sustainable for mission-critical workflows. It adapts to incoming user data and provides clear explanations for AI outcomes. Also, strong data management and governance measures are in place which supports automated decision-making.

Major Barriers within the Path to Achieving AI Maturity

Organizations face several challenges in reaching maturity. The LXT 2023 report identifies 11 barriers, as shown within the graph below. Let’s discuss a few of them here.

AI maturity challenges graph. Source: LXT report 2023

1. Integrating AI With Existing Technology

Around 54% of organizations face the challenge of integrating legacy or existing technology into AI systems, making it the most important barrier to reaching maturity.

2. Data Quality

High-quality training data is significant for constructing accurate AI systems. Nonetheless, collecting high-quality data stays an enormous challenge in reaching maturity. The report finds that 87% of firms are willing to pay more for acquiring high-quality training data.

3. Skills Gap

Without the best skills and resources, organizations struggle to construct successful AI use cases. In reality, 31% of organizations face an absence of expert talent for supporting their AI initiatives and reaching maturity.

4. Weak AI Strategy

Many of the AI we observe in real-world systems may be categorized as weak or narrow. It’s an AI that may perform a finite set of tasks for which it’s trained. Around 20% of organizations don’t have a comprehensive AI strategy.

To beat this challenge, firms should clearly define and document their AI objectives, put money into quality data, and select the best models for each task.

Major Business Drivers for Advancing Your AI Strategies

The LXT maturity report identifies ten key business drivers for AI, as shown within the graph below. Let’s discuss a few of them here.

An illustration of key business drivers for AI. Source: LXT report 2023

1. Business Agility

Business agility refers to how quickly a company can adapt to changing digital trends and opportunities using progressive business solutions. It stays the highest driver for AI strategies for around 49% of organizations.

AI may also help firms achieve business agility by enabling faster and more accurate decision-making, automating repetitive tasks, and improving operational efficiencies.

2. Anticipating Customer Needs

Around 46% of organizations consider anticipating customer needs as one in all the important thing business drivers for AI strategies. Through the use of AI to investigate customer data, firms can gain insights into customer behavior, preferences, and wishes, allowing them to tailor their services and products to raised meet customer expectations.

3. Competitive Advantage

Competitive advantage enables firms to distinguish themselves from their competitors and gain an edge within the marketplace. It’s a key driver for AI strategies, in line with 41% of organizations.

4. Streamline Decision-Making

AI-based automated decision-making can significantly reduce the time required to make critical data-informed decisions. Because of this around 42% of organizations consider streamlining decision-making as a significant business driver for AI strategies.

5. Product Development

From being recognized as the highest business driver for AI strategies in 2021, progressive product development has dropped to seventh place, with 39% of organizations considering it a business driver in 2023.

This shows that the applicability of AI in business processes doesn’t rely entirely on the standard of the product. Other business points comparable to high resilience, sustainability, and a fast time to market are critical to business success.

For more information in regards to the latest trends and technologies in artificial intelligence, visit unite.ai.