Why Your AI Initiatives Won’t Deliver

by HPA Senior Advisor Bob Kaplan

Companies of all sizes are pouring time, energy, and resources into AI and analytics projects, hoping for a competitive edge. But there are two problems with this mindset: The first is that many of these companies have not created a coherent data foundation and architecture, and therefore do not have the basic material to build good models. Second, the models and analytic tools are freely available on the market, becoming commoditized rapidly and therefore conferring limited advantage to any one user over another. No company ever gained strategic advantage from Excel and today’s tools are just spreadsheets on steroids.

A more effective investment approach focuses on data engineering and building a robust data foundation that efficiently identifies, captures, curates, and makes critical information accessible. Creativity in identifying data sources, along with innovative methods of integrating and curating them, will increasingly distinguish winners from losers across industries. That’s why capturing data engineering talent and nurturing it is critical to strategic success. While data scientists are also important, don’t overinvest: skills that were once exclusive to data scientists are now becoming standard. Instead, focus on building a strong data engineering team – they’ll be your secret weapon.

The Data Dilemma

In my experience as a CEO, CIO, board member, and consultant, I’ve seen many businesses fall short when it comes to harnessing data’s full potential. Sure, they like to talk about cutting-edge analytical technology, but let’s be honest, the investment results frequently fail to make a difference to either operational performance or strategic positioning, and they rarely hit their ROI hurdles. As I previously mentioned, a root cause of this shortfall is an all-too-common oversight: not recognizing the foundational role data engineering plays in supporting these tech initiatives. Without a strong data engineering framework in place, even the most advanced front end analytical tools will struggle to provide the insights necessary to bring strategic value to a business.

To illustrate further: Focusing solely on adopting technology like predictive modeling or AI without first establishing a solid data foundation is like focusing on paint colors and tile options when building a house, while ignoring key structural components like the foundation and support beams.

Data engineering is the critical structural system that feeds the necessary inputs into operational and analytical tools. Not to be confused with data analytics, which is primarily concerned with interpreting and analyzing data to extract insights , data engineering ensures data is captured, stored, and shared in a way that enables businesses to make reliable, data-driven decisions. Without a solid data engineering strategy and functioning team, organizations risk basing critical decisions on absent, incomplete, outdated, or unreliable data. Unfortunately, many do.

The Call for a Data-Driven Enterprise

The path to unlocking the power of data requires becoming a data-driven enterprise. This means data is viewed as a strategic corporate resource. It must be backed by appropriate processes and governance and integrated into decision-making. It is not glamorous work. CEOs would prefer to discuss the bright new shiny application toys they have been sold rather than talk about the timeliness of their data capture systems. Becoming data-driven goes beyond technology adoption; it means establishing a vision and practice that aligns data initiatives with overall business goals. This clarion call involves organizations recognizing the strategic value of their data and embracing a cultural shift to foster a more agile, innovative, and competitive environment.

If you’re wondering what it specifically means to be a data-driven enterprise, here’s my take:

• Establishing a strong data infrastructure: Effective data management tools and processes are rolled out. They’re then supported by Data Engineers who identify and source data, design data architectures, build data pipelines, ensure data quality, establish data governance, and collaborate with stakeholders across the enterprise for the seamless flow and accessibility of data throughout the organization. As you can see, data is a big deal and requires start-to-finish oversight to extract the most value out of it.

• Developing a clearly stated data and analytics strategy: This strategy is developed in collaboration with each business unit or function. With this as a north star, using data capture and analytics to improve business processes is far more likely to yield success. At the end of the day, success is driven by access to and capture of more unique data then competitors.

• Driving data-informed decision-making: Again, every strategic and operational decision should be grounded in data insights, ensuring decisions are not just based on past experiences, past practices, or intuition, but on current, actionable information.

• Nurturing a data-centric culture: This involves leaders making sureemployees across all levels of an organization understand the importance of data and integrate it into their daily work. Whether it’s marketing, sales, operations, or finance, every department leverages data to enhance performance.

In the era of AI, big data, and digital transformation, the importance of a solid data foundation is undeniable. The first step in building one is to conduct a data audit by working with business units to identify what kind of data they use and need but don’t currently have. Following that, you can map your data elements to understand how they’re captured, stored, and accessed. Only then can you start to think about the kinds of reports you can generate. Lastly, hire a data engineer (or several) and build from there.

By investing in data engineering, organizations can unlock their data’s true potential and drive business success. Remember, data engineering is a strategic enabler that powers the entire data-driven enterprise. The investments businesses make in data engineering will greatly determine its ability to adapt, innovate, and lead in the market.

BOB KAPLAN is an HPA Senior Advisor with over 35 years of experience as a senior executive and management consultant. A former Managing Partner at BCG and Senior Partner at McKinsey, Bob currently counsels CEOs and other senior executives on strategy, technology, and organizational issues. Bob has held CEO and CIO roles for multiple organizations, including Motif, ITM Software, and Silicon Valley Bank. He holds an MBA from the Stanford Graduate School of Business and a BA from Yale University.