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“It’s not enough to just train employees or buy technology – there must be a strategy behind it and long-term commitment to making it part of every employee’s day-to-day work and job function”.
says Sheryl Jacobson, Practice Leader for Strategy, Deloitte Consulting LLP
How are data-fueled technologies such as AI widening competitive advantage?
In an age of collaboration between humans and machines—what we call the “Age of With”—organizations can gain an advantage by designing systems in which humans and machines work together to improve the speed and quality of decision-making based on data-driven insights. By leveraging that type of information in their strategy, companies can identify opportunities to streamline operations, better understand their employees’ needs, and increase business efficiency. These benefits combined improve both the bottom line and ability to attract and retain employees – uniquely positioning them to beat out competitors in the areas of both profit and talent.
But not every organization is optimizing the opportunities available in the Age of With. Some do little or nothing with data to aid their decision-making. Others carry out analytics projects in pockets of the business. Far fewer consistently embed analysis, data, and evidence-based reasoning into their decision-making process.
Also read: Smart Ways to Fast-track AI Implementation
We recently conducted a survey, “Analytics and AI-driven enterprises thrive in the Age of With,” to see how many organizations fall into the top of our insight-driven maturity scale. The results were staggering: fewer than four in 10 (37%) of executives believe that their companies are in the top two categories – analytical companies and analytical competitors – and only 10% fall into the highest. While the remaining 63% are aware of analytics, they lack technology infrastructure, are still working in silos or are expanding ad hoc capabilities. In addition, 67% of executives surveyed are not comfortable accessing or using data from their existing tools and resources.
As per the report, only 10% of companies surveyed are at the top of the analytics maturity scale. Why do you think this is happening?
A lot of companies haven’t reached this level of maturity for many reasons – our study found that 63% are aware of analytics, but they lack technology infrastructure, are still working in silos or are expanding ad hoc capabilities. Some that do have technology in place only have specific people within the organization (one or two data scientists) that can interpret data or leverage it for meaningful actions and results. Others do not have leaders that champion data-driven efforts and lead by example: 67% of executives surveyed as part of our research are not comfortable accessing or using data from their existing tools and resources.
It’s not enough to just train employees or buy technology – there must be a strategy behind it and long-term commitment to making it part of every employee’s day-to-day work and job function.
Looking at the companies that are at the top of the maturity scale, these are organizations that have fully invested in a completely holistic analytics-driven culture. They have made a commitment to embedding technology and data science into all parts of their organization, trained employees on how to leverage those tools, evaluate their workforce on the use of data and results and have C-level executives that spearhead these initiatives.
Sixty-four percent rely solely on structured data from internal systems or resources; this does not seem to be enough. Why are firms not using data from interactive media like social media, audios or videos?
Only 18% of the organizations we spoke with for our survey have taken advantage of unstructured data from sources like social media, customer phone calls and more. This unstructured data delivers a more comprehensive understanding of customers and factors outside the organization that can impact business results.
Some of this has to do with the complexity of unstructured data: This data type can be difficult to put in the row-and-column relational format characteristic of traditional data storage, but over the last decade several new technologies have emerged to address that, including open source projects, cloud-based architectures, approaches to managing streaming data, and new storage hardware environments.
And, while unstructured data can be more challenging to interpret, it can deliver a more comprehensive and holistic understanding of the bigger picture. In fact, our survey also showed that executives who incorporate unstructured data into their approach are 24% more likely to exceed their business goals.
What will it take for companies to change their culture to be data-driven? Can you describe some steps that companies can take to integrate analytics into their businesses?
While there is no “one-size-fits-all” solution to build a data-driven culture, several steps can help companies integrate data and insights into every aspect of their business. These include:
- Hire or promote leaders who embrace analytics-based strategy and competition.
- Educate employees at all levels and in all functions about the importance – and value – of analytics in business decision-making.
- Encourage leaders to model examples and even go as far as to designate an executive sponsor (ideally, the CEO) to help change-resistant middle management get on board.
- Use “nudges,” social proof concepts, and other tools – including rewarding and embracing risk-taking – to inspire action and motivate employees to make analytics a core part of their jobs.
- Know the limits of analytics and communicate them accordingly: If you don’t have the data, you can’t gain the insights.
Together, these steps can help companies address data-driven culture challenges and become data-driven organizations at their core.
How can companies ensure AI and human intelligence work together to provide optimal products, service and delivery?
Our research showed that culture is one of the most common challenges keeping businesses from making the most of AI technologies, even as technical challenges persist. It’s not that hard to identify and use analytics tools; it IS hard to change behavior and encourage the business to “re-wire” itself to prioritize and embrace these technologies. That means that solving the culture challenge can help companies ensure AI and human intelligence work together effectively.
At the same time, the human element is absolutely essential in any successful AI project, and humans bring skills to the table – creativity, empathy, leadership, and more – than even the best AI can’t match. That’s why it’s essential for humans and machines to make the most of their respective strengths because together they are stronger and create unimaginable value for the business.
Education, sponsorship, championing insight-driven behaviors, and more can all ensure that companies make the most of both AI and human capabilities together in the Age of With. These problems are deeply intertwined; solutions to the “culture question” also help foster fruitful, positive, and innovative human-machine collaboration.
How can enterprises drive the ‘Age of With’ on ground?
Incorporating AI, machine learning, and cognitive technologies are about people working with machines, not in opposition to them. That means designing and enacting a system that best enables humans and machines to collaborate and work together effectively.
The “insights trifecta,” as we like to call it – data and tools, talent, and culture – is essential to creating and realizing effective AI integration in the Age of With. All three of these allow organizations to embed the insights they derive from data into decisions and actions.
Sophisticated tools help companies dive into and understand new sources of data, including unstructured data. By both recruiting and training talent to embrace AI technologies and insights, data science becomes a team sport where everyone in the organization is on board, data-savvy, and making the most of insights. And, crucially, a data-driven culture helps businesses act on those insights and generate more, which improves business processes, efficiency, and results.
The data backs this approach up. The Insights Survey found that a majority of the most mature and highest-performing analytics-driven organizations had trained all employees on analytics. These were the most likely by far (56%) to have used AI – and with that familiarity, employees grow more comfortable with the new technological advances (and new “tools”) at their fingertips.
That also tends to mean that organizations are more likely to meet business goals. Eighty-two percent of companies at which all employees are responsible for analytics insights exceeded their business goals, according to the Insights survey. By working with senior leadership to champion AI and make clear its value to both employees’ daily tasks and the organization as a whole, business leaders can help bring about the “Age of With” and get employees on board.
“In an age of collaboration between humans and machines—what we call the “Age of With”—organizations can gain advantage by designing systems in which humans and machines work together to improve the speed and quality of decision-making based on data-driven insights.”
Sheryl Jacobson, Chief Strategy Officer at Deloitte Consulting
Sheryl Jacobson is the Chief Strategy Officer for Deloitte Consulting. She is leading Deloitte’s strategy for AI – scaling existing businesses, cognitizing our services (automation, etc.), ecosystems and alliances, and starting up net new businesses. Sheryl has over 20 years’ experience in business and technology strategy and innovation.