Beyond the Hype: Real-world Insights into AI/ML Integration Across Industries
Tags: ai, research
Published: 2024-03-03 16:15:58

We're witnessing a significant paradigm shift with the pervasive integration of AI into various aspects of our lives. Examples like Netflix's recommendation systems, ChatGPT and other chatbots, generative art transforming text into images or videos, and chatbots impersonating customer service agents online are becoming increasingly prevalent, reshaping our day-to-day experiences. Many leading companies across diverse industries are capitalizing on these advancements, recognizing them as the new engines for value creation.

While AI adoption was relatively low in previous years due to limitations in technology and supporting infrastructure, the landscape has evolved dramatically. Today, we're observing a surge in adoption rates. According to Forrester's Data and Analytics Survey (2022), 73% of data and analytics decision-makers are actively implementing AI technologies, with 74% reporting positive impacts within their organizations. This trend spans various sectors, including healthcare, finance, transportation, manufacturing, marketing, education, and retail.

We're currently at a pivotal moment in history where AI is poised to fundamentally transform company operations and drive organizational evolution. Those who swiftly embrace this change stand to gain a competitive advantage over their peers. Given AI's vast potential, its rapid adoption comes as no surprise.

To delve deeper into the current and future landscape of AI adoption, this article seeks to offer a comprehensive overview. Aimed to explore the current state of AI adoption, elucidate its benefits and challenges, and provide insights into future trends and predictions surrounding AI integration.

> _AI is the big one. I don't think Web3 was that big or that metaverse stuff alone was revolutionary but AI is quite revolutionary_
-Bill Gates

### Which Industries Currently Utilize AI the most?


![Image 1](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/jmssjskxpiaurknl4dti.png)

Companies that use AI are motivated by three factors: the ability to cut expenses, develop faster, and grow profitability. However, each industry’s approach to AI applications, as well as its problems and outcomes, may differ.

Also we can see that businesses that are advanced with AI (“Leaders”) are pioneering widespread adoption of AI in comparison to other companies in the market

### AI Adoption In Practice By Categories



![Image 2](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/ojpgiq8l00gg1hkusksq.png)

Younger generations tend to adopt AI technology in their professional life easier and faster


![Image 2](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/mhv5z3ucswglbe5w25am.png)

### Current availability of AI technology


![Image 3](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/s9b5ctq66v1wph8443xb.png)

At present, AI is predominantly integrated at the feature level rather than at the infrastructure level. This means that AI technologies are primarily utilized to enhance existing products and services by introducing new functionalities like voice or image recognition. These features are developed on top of existing infrastructure and leverage AI algorithms to execute specific tasks. While there have been endeavors to embed AI more profoundly into the infrastructure level, such as through edge computing, the bulk of AI usage remains concentrated at the feature level.

### Biggest Challenges when Adopting AI



**Fragmented Technology Stack:** No standard for deploying AI systems. The AI community has not converged yet on formats and interfaces across the AI/ML stack

**Misguided Strategy:** Performance cannot be guaranteed on an ongoing basis. Lack of clear definitions of business goals and inflated expectations

**Evolving AI Regulation:** The technology environment is rapidly changing. Lack of GRC (government, risk & compliance) standards

**AI Methodology:** The definition of AI's proven playbooks, including designs, best practices, and technology pipelines.

**AI-Business Alignment:** Clear definitions of KPIs and KRis which are subject to ongoing assessment, evaluation and re-design.

**New Business Requirements:** Identifying new requirements and insights as they evolve. Embrace uncertainty

### Current Regulation Highlights

![Image 4](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/t83zdnotqygc8sxfzgwg.png)

### What’s Hindering AI Adoption?


![Image 5](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/a1n189dogc2n5jr5jzpa.png)

### How to Mitigate Potential Risks?

**Identify unique vulnerabilities:** Determine where bias could creep into your datasets and algorithms and where it could cause major damage

**Control your data:** Pay special attention to issues in historical data and data acquired from third parties. This includes biased correlations between variables.

**Keep governance up to speed:** Governance should be continuous and enterprise-wide. Set frameworks, toolkits and controls to help spot problems before they may proliferate.

**Validate independently and continuously:** You can use wither an internal independent team or a third party to analyze your algorithms for fairness.

**Diversify your team:** Building diverse teams helps reduce the potential risk of bias falling through the cracks. People from different racial and gender identities and economic backgrounds will notice different biases.

### Near-Future Value Capture Opportunities

**Decision Making:**Computer-based processes that uses AI to mimic and simulate real-world scenarios or system, allowing for the testing and optimization of various strategies, outcomes, and performances.

**Simulation:** The ability to process significant amount of historical data and produce and analyze future optional decision trees scenarios. Adoption rates are significant when it comes to areas of technology, operations and maintenance, and also CX and strategy.

**Data Analysis:** AI nowadays can help automate and streamline the data analysis process (e.g. cleansing) as well as significantly improve predictive models and analysis of unstructured data.

#### Where Will Adoption Increase Most?

![Image 3](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/kfb5h7et22tvxxcha8i0.png)

#### Key Takeaways & Conclusion


> **Ethics & Governance**
- Algorithmic and data biases will likely be regulated in the near future and will create uncertainty regarding usage of certain AI models
- Increased AI models regulations will force change in many companies’ systems and infrastructure
- Applications of AI will not be fully implemented into enterprises until business cases and expected ROI will be fully understood
- Industries with more consumer regulatory pressure will have lower AI adoption rates
- AI governance will likely join cybersecurity as a board-level topic


> **Trends**
- AI is recession-resilient and continued AI investments will continue in 2023, particularly among business impacted by economic and supply chain disruptions
- In 2023 low/no code AI tools will be more involved in the software development lifecycle
- Image editing is going to be changed dramatically

> **Workforce**
- In the short term - AI will free up employees to focus on value-add tasks and will improve job satisfaction
- AI applications still require human supervision and therefore it is unlikely that we will see dramatic HR changes in the near future, except certain functions

> **Management**
- Management’s decision making processes will not change significantly in the short term
- 25% of tech executives (e.g. CTO/CIO) will report to board/committee on AI governance

> **Adoption**
- AI adoption will probably still remain low in 2023-2024
- 10% of Fortune 500 enterprises will generate content with AI tools in 2023-2024
- Company’s ability to completely change its processes will be hard, and therefore we expect adoption to be slow. It will be likely to be easier for companies to integrate AI into their core processes if they can spin off certain functions or form brand new business units
- AI models still considered as black box for non-technological employees which will require training and upskilling
- AI infrastructure challenges will surpass data associated issues as the biggest challenge for scaling AI/ML


AI tools tend to be highly accurate, but they are definitely not perfect and can make bizarre mistakes. Maintaining human oversight during the implementation and afterwards is crucial to ensuring quality, both for model training and for the final correction of the output in downstream processes. Leaders must stay vigilant about the potential risks and cognizant of the need for proper training and corporate governance. And we ordinary people need to actively participate in the discussion and determine our future ourselves.

Sources:
- Forrester Predictions 2023: Artificial Intelligence, run:ao 2023 state of AI Infrastructure

- “Understanding algorithmic bias and how to build trust in AI” - PwC US

- The state of Machine Learning at the end of 2022 (cnvrg.io)

- IBM Global AI Adoption index 2022

- World Economic Forum. 

- Gartner