8 Practical Tips to Build a Competitive AI Advantage

February 17, 2023

Today, the 21st-century business landscape is rapidly evolving. Organizations are increasingly adopting artificial intelligence (AI) to gain insights from vast amounts of data, improve their business processes, and deliver better customer experiences.

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The widespread adoption of AI technology has increased competition in nearly every sector, resulting in a growing market size. In fact, due to this growing demand, the AI software market is estimated to reach $126 billion by 2025, compared to $51.27 in 2022. Hence, building a profitable and competitive AI advantage demands significant effort from businesses.

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The article lays out detailed practical steps companies can adopt to build competitive advantage using AI. We’ll also discuss its importance and benefits and share how Unleashing.AI is helping companies build competitive AI advantage.

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How to Build a Competitive Advantage with Artificial Intelligence?

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Building a competitive AI advantage requires a significant investment of time, resources, and expertise. Therefore, businesses must be willing to invest in the necessary talent to support their AI initiatives and be patient and persistent in facing potential setbacks and failures. With the right approach, companies can extract maximum benefits from their AI workflows.
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Let’s discuss the ways that will help businesses build a competitive advantage with AI.

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1. Develop a Vision for AI Transformation

Building a comprehensive AI transformation vision helps organizations maximize the advantages of AI.

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What practical steps can your organization take?

Start by defining goals and identifying areas where AI can improve existing business operations.
Create a clear road map outlining the steps needed to achieve goals along with their estimated completion timelines.
Identify the risks and opportunities associated with AI adoption.
Communicate the AI vision with stakeholders to ensure their full support and collaboration.

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2. Ensure the Availability of High-Quality Data

Data fuels AI. It comes in different shapes and sizes, including structured, unstructured, nominal, ordinal, batch, and streaming data. Also, it comes from multiple sources, such as organizational processes, third-party analytics, and external sources.

Therefore, it's essential to ensure the availability of high-quality data. Also, how you optimize your data and where you keep it is critical for building high-quality AI products and workflows.

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What practical steps can your organization take?

Accurately gather data requirements as per the business problem.
Identify different data sources and formulate a plan to bring them together.
Build automated pipelines for cleaning data and validating data inputs.
Ensure data quality and integrity by removing duplicate or incomplete data.
Build automated workflows to monitor data quality.

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3. Leverage AI-Enabled Data Management

Data management is a labor-intensive task that involves data cleaning, extraction, integration, cataloging, labeling, and organization.

Poor data management can have negative impacts. On average, organizations incur a cost of $12.9 million every year due to poor data quality, which often stems from poor data management.

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What practical steps can your organization take?

Identify data management requirements and goals.
Consider the quality and quantity of data.
To automate and optimize data pipelines, evaluate different data solutions, such as ETL-based data warehouses, data lakes, and data lakehouses.
Consider the level of expertise and resources available to implement and maintain AI-powered data management systems.

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4. Build Critical Organizational Capabilities

Establish the following critical capabilities that will support the long-term vision of the company:

Adequate Skills: Acquire and nurture talent in data science, build cross-functional teams for AI, and provide education to functional leaders on AI concepts.

Available Resources: Having access to high-quality data and in-house expertise to apply AI to business use cases.

Organizational Culture: Fostering a work culture that supports experimentation and innovation and recognizes the value of data and cross-functional collaboration. (More on this in the next section)

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5. Create a Culture of Innovation & Participation

Organizations can take several steps to build a culture of innovation and participation to unlock the full potential of their AI initiatives. Such as:

Communicate the importance of AI and its potential benefits to employees at all levels of the organization.
Provide training and education about AI and how to apply them to specific business challenges.
Provide employees with the necessary resources and support to try our new ideas.
Democratize data and AI adoption by breaking down silos and fostering cross-functional collaboration.
Promote a continuous learning and improvement culture by encouraging employees to stay updated with the latest trends and technologies.

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6. Build Scalable AI Infrastructure

Building scalable AI infrastructure is critical for the success of AI initiatives. According to an Accenture report, 84% of executives believe they won't achieve their growth objectives unless they scale AI.

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What practical steps can your organization take?

Implement a robust data infrastructure that can expand as per business needs.
Ensure that the data and analytics strategy aligns with business goals.
Build and empower dedicated, specialized teams focused on building scalable AI.
Adopt a cloud-first strategy, as it offers a scalable and flexible infrastructure to meet AI demands.
Support AI teams by providing them with AI monitoring and maintenance platforms (MLOps tools).
Build a company culture ready to upskill/reskill to meet evolving AI demands.

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7. Prioritize Ethics and Transparency

AI is used in making decisions that affect our daily lives. Hence, it is critical to build trust and transparency in AI models.

For that, numerous compliance standards, such as GDPR and CCPA, are in place that requires organizations to handle personal data securely, enabling them to prioritize trust and transparency.

For instance, last year, Clearview AI was fined €20 million by GDPR for not having a legal basis for its data processes and for lack of transparency concerning the processing operations.

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What practical steps can your organization take?

Practice explainable AI, which requires organizations to explain how AI models get to a decision.
Detect hidden data bias by evaluating training data
Continuously monitor and test the model’s decision-making.
Regularly monitor and resolve data, model, and concept drift issues in deployed AI models to improve their performance.
Embed ethics in the organizational culture, address core values, and integrate AI governance frameworks in business workflows.

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8. Hire & Retain Top AI Talent

Between 2022 and 2055, 97 million AI-related jobs are predicted to be created in different business domains. With the growing demand for AI talent, attracting and retaining the best candidates can be challenging.

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What practical steps can your organization take?

Offer competitive compensation packages for AI teams.
Create a positive and supportive work environment where AI teams readily have access to relevant business resources.
Incentivize AI teams with adequate career growth and development opportunities: training, professional development, and mentorship to have them stick around for a long period.
Recognize and reward top performers with bonuses, promotions, or any kind of reward suitable for your organization.

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Importance of Building Competitive AI Advantage in Today’s Business Landscape

AI can significantly enhance operational efficiency, improve decision-making, and provide insights that would be impossible to achieve otherwise. By building a strong AI capability, organizations can differentiate themselves from their competitors, drive innovation, and position themselves to thrive in the years ahead.

Let’s discuss the benefits of building a competitive AI advantage.

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1. Enhanced Operation Efficiency & Greater ROI

A competitive AI advantage enhances business efficiency by automating tasks, personalizing experiences, providing valuable insights for better decision-making, and optimizing business processes. This, in turn, leads to cost savings, increased revenue, and improved business performance.

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2. Distinguished Data Dominance

Organizations that strive to achieve AI excellence can build data dominance in their industry because AI and data are complementary.

The more data a company can acquire, the more advantage it has over its competitors. Often, companies put a lot of time and money into acquiring more data to level the playing field.

If this data is used effectively, it can help build better AI products, leading to more customers, which in turn, leads to more data, and the business growth cycle continues.

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3. Continuous Improvement With Predictive Analytics

By using advanced AI analytics and predictive models, organizations can gain valuable insights into their operations and identify areas of improvement.

Also, AI makes it easier to process the feedback collected from customers, employees, and stakeholders. Organizations can use this information to make data-driven decisions and position themselves for success in the coming years.

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4. Enhance Time-Efficiency With Automation

AI technologies can automate repetitive and time-consuming tasks and reduce human error. As a result, employees can focus on higher-level work. For example, AI-powered chatbots can handle customer inquiries and support, reducing the need for human customer service representatives.

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5. Increased Security & Safety

AI's ability to identify and track patterns allows systems to quickly detect security breaches or threats related to data protection, machine operations, or surveillance. In cybersecurity, AI-enabled solutions can predict and prevent threats more accurately and identify anomalies with greater precision.

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Unlock the Potential of AI with Unleashing AI

Let’s explore how Unleashing AI helps organizations unlock the full potential of AI to build competitive products.

1. How Unleashing AI Helped a Giant Retailer Achieve Cost Savings Worth $500K?

Unleashing AI helped a retail company save over $500k in customer support costs by integrating machine learning and natural language processing (NLP) technologies to create a call assistant that could handle inbound customer calls.

The call assistant could understand customer inquiries and respond in a natural, human-like manner. The company was also able to track customer feedback and satisfaction levels. Customer satisfaction levels remained high while reducing customer service costs.

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2. How Unleashing AI Leverages NLP to Increase Retention by 12%?

Unleashing AI helped a San-Francisco based startup with over 50 team members and 10M+ users improve retention by 12%. We leveraged the latest advances in deep learning and natural language processing to upgrade their conversational AI-based dialogue engine.

They partnered with us to collect dialogue data, design a system for their dialogue engine, optimize their software performance, and provide guidance on the next steps for model improvement. We improved the quality of their dialogue engine and designed a new system, resulting in a production-ready solution that boosted their revenue.

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