Today, innovation-driven businesses are investing significant resources in artificial intelligence (AI) systems to advance their AI maturity journey. According to IDC, worldwide spending on AI-centric systems is expected to surpass $300 billion by 2026, compared to $118 billion in 2022.
In the past, AI systems have failed more frequently due to a lack of process maturity. About 60-80% of AI projects used to fail due to poor planning, lack of expertise, inadequate data management, or ethics and fairness issues. But, with every passing year, this number is improving.
Today, on average, the AI project failure rate has come down to 46%, according to the latest LXT report. The likelihood of AI failure further reduces to 36% as a company advances in its AI maturity journey.
Let’s further explore an organization’s path to AI maturity, the different models and frameworks it can employ, and the main business drivers for building an effective AI strategy.
What is AI Maturity?
AI maturity refers to the level of advancement and sophistication a company has achieved in adopting, implementing, and scaling AI-enabled technologies to improve its business processes, products, or services.
According to 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 year’s survey results, while 52% of organizations are actively experimenting with AI.
The report suggests that the most promising work has been done in the Natural Language Processing (NLP) and speech recognition domains – subcategories of AI – since they had the most number of deployed solutions across industries.
Moreover, the manufacturing & supply chain industry has the lowest AI project failure rate (29%), while retail & e-commerce has the highest (52%).
Exploring Different AI Maturity Models
Usually, AI-driven organizations develop AI maturity models tailored to their business needs. However, the underlying idea of maturity remains consistent across models, focused on developing AI-related capabilities to achieve optimal business performance.
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 companies can use to assess 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 test the viability of these solutions at this level.
- Level 2 – Active: Organizations are at 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 least one AI project to production.
- Level 4 – Systematic: Organizations at this level utilize AI for most of their digital processes. Also, AI-powered applications facilitate productive interaction within and outside the organization.
- Level 5 – Transformational: Organizations have adopted AI as an inherent part of their business workflows.
As per this model, companies start achieving AI maturity from level 3 onwards.
IBM AI Maturity Framework
IBM has developed its own unique terminology and criteria to assess the maturity of AI solutions. The three phases of IBM's AI maturity framework include:
- Silver: At this level of AI capability, enterprises explore relevant tools and technologies to prepare for AI adoption. It also includes understanding the impact of AI on business, data preparation, and other business factors related to AI.
- Gold: At this level, organizations achieve a competitive edge by delivering a meaningful business outcome 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 in the Path to Achieving AI Maturity
Organizations face several challenges in reaching maturity. The LXT 2023 report identifies 11 barriers, as shown in the graph below. Let’s discuss some 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 biggest barrier to reaching maturity.
2. Data Quality
High-quality training data is vital for building accurate AI systems. However, collecting high-quality data remains a big challenge in reaching maturity. The report finds that 87% of companies are willing to pay more for acquiring high-quality training data.
3. Skills Gap
Without the right skills and resources, organizations struggle to build successful AI use cases. In fact, 31% of organizations face a lack of skilled talent for supporting their AI initiatives and reaching maturity.
4. Weak AI Strategy
Most of the AI we observe in real-world systems can be categorized as weak or narrow. It is an AI that can perform a finite set of tasks for which it is trained. Around 20% of organizations don’t have a comprehensive AI strategy.
To overcome this challenge, companies should clearly define and document their AI objectives, invest in quality data, and choose the right models for every task.
Major Business Drivers for Advancing Your AI Strategies
The LXT maturity report identifies ten key business drivers for AI, as shown in the graph below. Let’s discuss some of them here.
An illustration of key business drivers for AI. Source: LXT report 2023
1. Business Agility
Business agility refers to how quickly an organization can adapt to changing digital trends and opportunities using innovative business solutions. It remains the top driver for AI strategies for around 49% of organizations.
AI can help companies 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 of the key business drivers for AI strategies. By using AI to analyze customer data, companies can gain insights into customer behavior, preferences, and needs, allowing them to tailor their products and services to better meet customer expectations.
3. Competitive Advantage
Competitive advantage enables companies to differentiate themselves from their competitors and gain an edge in the marketplace. It is a key driver for AI strategies, according to 41% of organizations.
4. Streamline Decision-Making
AI-based automated decision-making can significantly reduce the time required to make critical data-informed decisions. This is why around 42% of organizations consider streamlining decision-making as a major business driver for AI strategies.
5. Product Development
From being recognized as the top business driver for AI strategies in 2021, innovative 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 does not rely entirely on the quality of the product. Other business aspects such as high resilience, sustainability, and a quick time to market are critical to business success.
For more information about the latest trends and technologies in artificial intelligence, visit unite.ai.
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