The Applied AI Advantage: Unlocking Value for Businesses

What is Applied AI?

Applied AI is a simple concept that deals with how AI can be used to solve real world problems. This distinction is necessary as AI refers to the broader concept or domain of study which is involved with the development of algorithms. The transition of this research into real-world usage is the idea behind applied artificial intelligence.

As an applied domain, it prioritizes measurable impact (real-world output) over how closely the algorithms mimic human thinking (internal processing). Hence, it focuses on combining intelligent capabilities with commercially viable software solutions. It aims to gain user trust and industry adoption of AI by transforming business processes.

80% of AI Solutions Fail - Why

The importance of applied AI becomes apparent when you realize that around 80% of AI projects fail in practice. Let us delve further to look at scenarios where AI applications fail.

Forward-looking technologies that are unrealized

The realm of quantum computing promises to bring vastly superior processing capabilities, revolutionizing fields such as cryptography and material science. However, in the current day it is in nascent stages and doesn’t solve any enterprise challenges yet.

Underutilized applications

While voice assistants like Siri, Google Assistant, or Alexa are available to millions of users, their use is limited to simple queries. The powerful NLP (Natural Language Processing) models of these assistants have limited impact in everyday life.

Background tasks

AI in enterprise applications aim generally aim to optimize factors like manual dependency, costs, and workflow efficiency. However, in many implementations they only optimize few parameters, rather than delivering organization-level value.

Limited application to real problems

IBM Watson for oncology to revolutionize cancer treatment, providing personalized treatment recommendations for patients. However, the complexity of medical practice and lack of high-quality data led to inaccurate results and insufficient adoption.

Barriers to Successful AI Implementation

Despite big promises, AI projects fail to deliver the value they promise. Between the proof-of-concept phase to deployment, certain practical limitations are often realized.

From an implementation standpoint, AI solutions fail because of these reasons:

Low Reproducibility of Results

This happens when there is discrepancy in results between the PoC (proof-of-concept) phase and later phases. A model with low reproducibility is not reliable for real use and does not garner trust of users.

Scalability Issues

Any AI implementation at an enterprise level will have to scale beyond testing conditions to provide real value. AI integration is an expensive and long-term commitment for companies and should accommodate business growth and increasing complexity.

Implementation Barriers

The entire design to deployment process involves multiple stakeholders. For example, the model design and training is carried out by data scientists, whereas MLOps manages the production environment. Visibility of the entire stack is necessary to ensure effective implementation of the system.

Applied AI in Action: Proven Practices for Impact

There are a few core tenets of AI application that match the commercial need for market viability and quick time-to-market.

• Impact on real-world problems
• Immediate availability
• Positive impact on stakeholders
• High accuracy and user trust
• Scalable with business needs

These factors effectively encourage better accessibility and utility to a wider set of stakeholders, leading to the democratization of AI. Hence, proper implementation of AI involves the underlying processes, systems and people to serve diverse needs across the organization.

Let us look at some of the key considerations which lead to a more effective application of AI.

Data Management

• Data quality: To get accurate outputs from applied AI, the data fed into it must also be good. Good data measures such as completeness, validity, consistency, and timeliness need the data engineers’ focus. This would ensure that the following steps such as data cleaning, preprocessing, labelling and so on also produce good data.
• Scalability with growth: This involves the creation of a centralized data repository in order to support the massive data volumes that come with enterprise AI systems.
• Centralized guide for data attributes or features: Standardized data definitions remove confusion over ambiguous terms and labels. This is necessary as the data flows between different stakeholders (data scientists, engineers, business analysts, product managers, etc) as the project progresses.
• Optimized AI/ML data pipelines: Data pipelines need optimization as they will allow for quick and reliable delivery of data, ultimately resulting in lower latency and processing costs.

Data Processing and Training

As discussed above, the quality of the model’s output depends on how good the data is. For this reason, data processing is one of the most crucial steps before training begins. Processing of data involves many steps.

• Data cleaning: Raw data contains many errors, inconsistencies, duplicates, and missing values. Data cleaning involves activities such as handling these missing values, correcting outliers, deduplication, and removing irrelevant data.
• Data validation: After cleaning, data must be validated to ensure adherence to the set standards. Data types are verified, format consistency is checked, and data integrity rules are enforced.
• Data transformation: Depending on the model’s purpose, the data format used by the model can be specific. Hence, data must be transformed to compatible formats so that the model can interpret it effectively. There are many techniques used to transform data, such as normalization, encoding categorical data, and feature engineering.
• Feature selection and reducing dimensionality: This step reduces the complexity of data by further trimming down irrelevant data. Once again, this area has standardized techniques such as principal component analysis (PCA), random forest, etc.

Networking Infrastructure to Support AI Requirements

As more advanced AI models such as LLMs have appeared, their needs of storage, compute, and networking have increased. Traditional networks are struggling to handle this increased demand. Networks need to offer high throughput with low latency to handle these huge data volumes.

Hence enterprise network infrastructure needs to evolve, with measures such as optimized protocols, edge computing, network slicing, and software-defined networking (SDN).

Governance

As we know, regulators have started becoming more stringent with tech players and are more closely observing their actions. It has become necessary to be compliant with standards such as GDPR and the EU’s AI Act.

• Ethical standards and responsible AI: Clear ethics guidelines must be decided at an organisation or team level even before the AI design process begins. Factors like fairness, transparency, accountability, and inclusion are also in the spotlight, and applied AI models also need to reflect these values.
• Data governance: Since AI models work with large volumes of sensitive and personal data, it is very important to keep this data secure. Safeguarding the sensitive user data from attackers and data breaches is also necessary.

Algorithm transparency and explainability

This is an aspect AI researchers currently struggle with, as AI models are becoming increasingly complex. Basically, it should be easy to trace back how a model arrived at a specific output. For better explainability and trust, clarity is needed on how the model is making decisions.

Value Creation with AI

AI is the future. With AI becoming more complex, beneficial, and irreplaceable for businesses and individuals, it will unlock new opportunities for value creation. A study commissioned by Microsoft, gives insights into how AI is impacting the bottom line.

• 71% of companies are using AI in one form or another, and 22% of those not using AI are planning to implement it in the next 12 months.
• Investment in AI is resulting in a 3.5x return, and this can go as high as 8x for the top performers. The average return on investment (ROI) is achieved in 14 months.
• The time taken for AI transition is mostly below 12 months, with 40% of cases taking less than 6.
• Organizations are already seeing the benefits of AI, with an 18% increase in factors such as customer satisfaction, employee productivity, and market share.

The insights shared above show that AI is clearly delivering value to businesses and their customers. AI insights help leaders make better decisions to drive efficiency and growth, optimize operations, drive innovation, and enhance customer experiences among other factors.

With applied AI, industry leaders have a framework to explore new business models, and build profitable and sustainable businesses. As it leads to better quality of output, proper implementations can give greater visibility and insight into challenges.

Whether its applied to operations or understanding customer journeys, it can bring efficiencies and help businesses adapt to changing markets. As more firms explore how AI can apply to their business, they will see maximum impact from strong governance of AI. It holds potential not just as a tech advancement, but also one that can unlock new value for its stakeholders.