Let Data Be Your Guide: Why Choose an Artificial Intelligence Digital Transformation

With the high failure rate of digital transformations, the stakes have never been higher. According to McKinsey, around 70% of digital transformations fail due to, among other reasons, a lack of quality information for decision-making. When data quality is poor, it is impossible to make quick and confident, informed decisions – and transformations are doomed to fail. Yet, the extensive manual effort required to collect and analyze the massive amount of data needed for making intelligent decisions around digital transformations is simply too high a hill for most companies to climb without a guaranteed return on the other side.

This dilemma seems ripe for artificial intelligence:

If AI could be harnessed to accelerate and improve data collection quality, AI could be the missing piece of the puzzle for digital transformation.

The immense anticipation for AI ahead of 2022 was undoubtedly justified—there isn’t an industry on earth that would not benefit and grow from the use of artificial intelligence. Artificial intelligence can improve work speed, accuracy, and quality at the individual, team, organizational, and industry levels. Businesses that leverage AI effectively will likely gain a competitive edge by optimizing operations, enhancing customer experiences, and tapping into new markets.

Digital transformation and artificial intelligence also have parallels that make their interplay nearly undeniable: Both rely on quality data, boost organizational agility, and are designed to help organizations meet ever-evolving customer and market needs.

In many ways, artificial intelligence could answer the most common challenges in digital transformation. More specifically, McKinsey outlines the following challenges to explain why most digital transformations fail:

• A failure to set fact-based, high aspirations
• A lack of a compelling “why”
• Poor execution – focusing too much on activities and not enough on outcomes
• A failure to sustain the impact they’ve achieved

At the root of these challenges lies a deeper issue: a lack of quality data and the effort required to get there. It’s impossible to set fact-based, high aspirations without accurate facts. It’s impossible to align around a compelling “why” without data to back it up. It’s impossible to know which activities are driving which outcomes—and whether those outcomes are meeting their marks—without data to prove it. And it’s impossible to know how to sustain an impact without data showing which activities drive desirable outcomes.

And even if you prioritize finding the answers to all these data questions, you will quickly find a hands-on approach to this collection and analysis is an enormous undertaking that will only work to slow down your transformation.

The Economist Impact Survey found that 84% of executives recognize the need to improve their organization’s data-driven decision-making. In fact, only 14% of respondents expressed confidence in their company’s decision-making governance. This lack of confidence in decision-making stems from a lack of confidence in data.

The importance of quick and easy access to accurate data in transformation efforts cannot be overstated. Data is critical in digital transformation because it is the foundation for decision-making, innovation, and business efficiency.

A chief technology officer at a manufacturing company summarized it like this in an interview with Deloitte:

There’s a whole series of questions GenAI is triggering about data strategy, that in the past were far less important. I think we’re probably spending as much time on data strategy and management as on pure GenAI questions because data is the foundation for GenAI work.

Data enables organizations to make decisions based on accurate, quantifiable information rather than intuition or guesswork. Data helps businesses understand their customers’ preferences, behaviors, and needs, enabling them to deliver personalized experiences and targeted marketing. Data can reveal inefficiencies and bottlenecks within business processes. Data helps companies identify new market opportunities and areas for innovation. Data allows businesses to monitor key performance indicators (KPIs) in real-time, giving them the agility to respond to changes, whether market trends, customer preferences, or operational challenges.

With advanced data analytics, organizations can move from reactive to proactive strategies. Predictive analytics helps forecast future outcomes, while prescriptive analytics suggests actions to achieve desired results. This can be applied across various functions, from supply chain management to sales forecasting.

A successful digital transformation often involves a shift to a data-driven culture. When organizations prioritize data, they encourage employees to use data for decision-making, leading to a more informed, consistent, and cohesive approach across all departments.

Companies that leverage data effectively can often stay ahead of their competitors. By understanding market dynamics, customer behavior, and internal processes, data-driven companies can make strategic moves faster than their less data-savvy counterparts.

The success of any digital transformation hinges upon the careful collection, storage, and analysis of tremendous amounts of data. From data quality and integration to security and scalability, AI-driven solutions help companies overcome obstacles that would otherwise impede their ability to leverage data effectively.

AI can address data quality, integration, security, management, analytics, and visualization challenges while enhancing decision-making and fueling continuous improvement.

How AI Boosts Data Quality

AI can boost data quality in several ways. Its algorithms can perform automated data cleaning, detect and correct errors, inconsistencies, and redundancies in large datasets, and improve the overall health and accuracy of a data record. This can include identifying duplications, filling in missing values, and standardizing formats.

AI can also perform data enrichment, inferring missing data points or adding external data, providing a more comprehensive view for analysis.

Finally, AI-powered natural language processing (NLP) can process unstructured data, enabling businesses to gain valuable insights that were previously inaccessible.

How AI Supports Data-Driven Decision Making

AI algorithms can uncover hidden patterns and trends that humans may not readily detect, enabling a deeper understanding of data and better strategic insights.

Through machine learning and natural language processing (NLP), AI can assist non-technical users in accessing and analyzing data, democratizing data-driven decision-making across the organization.

AI Data Integration Use Cases

Data integration is another critical area where AI can add efficiency and speed. AI can power automated data mapping – the ability to map and integrate data from multiple sources – which makes it easier to combine data from different systems, databases, and departments.

It can also optimize ETL (extract, transform, load) processes, streamlining data movement and transformation between systems, ensuring that data is clean, accurate, and ready for use across platforms.

With AI, companies can also enable real-time synchronization between different data sources, providing a more up-to-date and comprehensive view of operations.

AI for Data Security

AI-powered systems can detect unusual patterns in data access or usage, helping to identify potential security threats, data breaches, or fraud in real-time.

AI can also assist with compliance by automating the monitoring and enforcement of data governance policies, ensuring that data handling complies with legal regulations (i.e., GDPR and CCPA) without constant human oversight.

Finally, AI can help manage access to sensitive data through advanced encryption algorithms and by setting automated access controls based on user behavior and risk assessment.

How AI Streamlines Data Management

AI can enhance and scale data management. AI algorithms can process massive datasets quickly and efficiently, enabling companies to derive insights from large data volumes that traditional methods can’t handle.

AI is also well-equipped to handle distributed data processing: AI frameworks are compatible with cloud / hybrid computing environments, making it possible to process data across different storage systems and locations.

Organizations can also use AI to optimize data storage, leveraging usage patterns to suggest optimal storage strategies, automatically archiving or deleting redundant data, and freeing up storage space.

Data Analytics Powered by AI

Organizations can employ AI to glean real-time insights and forecast trends and outcomes.

Using predictive analytics, AI can analyze historical and real-time data to forecast future trends, demand, and risks, enabling more proactive decision-making.

Prescriptive analytics can take this one step further, suggesting specific actions based on data-driven predictions and helping organizations optimize strategies for various outcomes.

Finally, with AI, organizations can generate accurate, real-time insights from data streams, enabling faster response times and agile decision-making.

AI for Data Visualization and Accessibility

AI can make data more accessible and digestible with data visualization. Using AI algorithms, dynamic, real-time visualizations help make complex data accessible and easy to understand, supporting more informed, data-driven decision-making across all levels of the organization.

AI-powered tools can facilitate access to data by translating complex datasets into more understandable formats, making insights more accessible to stakeholders without specialized technical knowledge.

AI Enables Continuous Improvement

AI models can continuously learn and adapt based on new data, becoming more intelligent and efficient. AI systems can adapt governance models as data changes, ensuring that data security measures stay current without constant manual intervention.

AI in digital transformation not only addresses immediate data challenges but also equips organizations to handle future challenges by improving data scalability, security, quality, and accessibility. This empowers businesses to leverage data as a strategic asset for long-term growth and innovation.

Building an artificial intelligence digital transformation framework involves two key steps: Creating a solid corporate AI policy and establishing data quality best practices.

Creating a solid corporate AI policy is essential for ensuring an organization’s ethical, responsible, and effective use of artificial intelligence. This process can include:

• Defining the scope and objectives of the policy
• Establishing ethical principles of fairness, transparency, accountability, privacy, and security
• Defining requirements for compliance with legal and regulatory standards
• Defining roles and responsibilities
• Setting standards for data governance
• Implementing risk management frameworks
• Establishing processes for continuous review and improvement
• Establishing standards for documentation and reporting

A strong corporate AI policy is a living document that reflects the organization’s commitment to responsible AI use. It should be comprehensive yet flexible to accommodate the rapid evolution of AI technologies and practices.

Your organization’s corporate AI policy should include clearly defined data quality best practices. Creating these safeguards provides a foundation for data-driven decision-making, compliance, and operational efficiency. This might include:

• Defining data quality standards and metrics, including data quality scorecards
• Establishing a data governance framework
• Implementing data quality tools and technology, including data profiling and cleansing tools, automated data quality checks, and master data management (MDM)
• Defining data quality processes – including data validation at entry points, standardized data entry and formatting, and regular data cleansing and auditing
• Monitoring and measuring data quality continuously with automated monitoring systems, real-time data quality dashboards, and root cause analysis
• Enabling data quality collaboration across teams
• Creating plans for data quality compliance and scalability

By establishing these best practices, organizations can build a strong foundation for data quality, ensuring that data is accurate, reliable, and valuable across all business functions. This enables teams to make better, data-driven decisions and maintain a competitive edge in an increasingly data-dependent world.

Adopting an AI-driven approach to digital transformation offers unparalleled opportunities to harness data more effectively, overcome common challenges, and optimize organizational operations.

AI can enable data accessibility and visibility in your digital transformation by bridging fragmented data silos, surfacing hidden insights, promoting faster, more intelligent decision-making, and informing strategic tasks like scenario planning and capacity reallocation.

AI can identify early warning signs, gaps, and bottlenecks by alerting potential issues, highlighting lagging projects, identifying dependencies, and providing problem-solving options.

AI can increase productivity in your digital transformation by simplifying day-to-day work tasks, streamlining insight processes, completing specific tasks (like timecards and updating dependencies), building action plans, and optimizing capacity planning.

AI can upskill talent by facilitating access to company-wide data, teaching employees about solutions or products, educating them on best practices, and applying methodologies.

With the proper framework, AI can help businesses improve data quality and integration and create a culture of data-driven decision-making that enhances agility and innovation.

Solutions like Planview Copilot enable organizations to easily converse with and question their data using natural language to glean insights, derive recommendations, and facilitate work-oriented actions. As companies continue to embrace digital transformation, AI is a critical partner in navigating the complexities of data management and propelling sustained, impactful change. Embracing AI isn’t just an investment in technology—it’s a strategic step toward building a resilient, future-ready business.