AI customer experience: Doing AI versus actually being an AI organization
The truth nobody wants to say out loud: To be an AI organization, you will fail if you don’t change.
In the last decade, many businesses have tried to transform into data-driven companies. However, only a small percentage can claim they’ve achieved this goal. According to a report from Harvard Business Review, there’s been no significant increase in the percentage of executives who report their companies are driving business innovation with data over four years.
There are many reasons for this stagnation, from the big tech investments required for such a transformation to company cultural changes that need to occur in parallel. But advancements in generative AI are about to change this, helping companies innovate through data driven decision making.Even though companies are currently focused on using generative AI for efficiency, this focus on cost savings will shift. The technology will have a bigger impact on the bottom line through either:
- Enabling completely new products and services
- Adding more value to existing products and services
The evolution of data driven decision making
Business data analysis has evolved tremendously since the turn of the century. When making decisions, executives have gone from gut feelings to basic statistical models and, finally, sophisticated AI-driven insights –all the while moving towards a more data-driven mindset.
This was accompanied by a growing need to shift towards business models with more dynamic AI-supported processes, allowing companies to optimize efficiency, be more agile, and take advantage of new market opportunities.
Generative AI is the key technological advancement businesses need to achieve this change. It provides leaders with real-time actionable insights while helping teams become more efficient by automating parts of their work – all through a simple interface.
Generative AI-driven data analytics
Generative AI models can process vast amounts of data quickly, thanks to their ability to “learn” from both structured and unstructured data.
As a result, companies can now feed all their data to the model, which helps to break down data silos, but also uncovers new insights. Since the model has access to data that was kept in various systems, it can reveal patterns that were previously invisible.
In addition, while traditional data analytics systems focus on analyzing past performance and making predictions based on that, generative AI systems can go a step further. These models can create new “synthetic” data based on the patterns in the data it learned from.
This new data allows companies to generate future scenarios by changing certain key variables, and then plan contingencies for each one. For example, a retailer can test how different versions of a new product would perform and then pick the best one before launching it on the market.
Finally, generative AI enables a total refresh of existing processes. By allowing the machine to take over repetitive and time-consuming work, you ensure your people have the time to focus on work that brings more value to your company.
Changing minds, changing culture
We’ve all seen the widespread coverage of generative AI and its many benefits. CEOs of big AI companies have embarked on a media tour, trying to convince everyone of the great new future their solutions will bring.
As a result of the media blitz, many companies report that their executives and staff are more open to experimenting with AI and data solutions in general. This change in mindset is critical, as it brings a shift in corporate culture – something that’s repeatedly been reported as one of the greatest challenges to data-driven transformation.
Since generative AI makes it easy to analyze data and get insights, it’s helped people on all company levels to embrace data. By making data more understandable and accessible, the tech enables people to contribute to data-driven decisions and discussions, regardless of their technical background.
When people feel included in this way, it boosts their morale and engagement while also bringing a variety of new and diverse perspectives to the decision-making process – resulting in better decisions for the company.
In addition, leaders who recognize the significance of generative AI are starting to invest in wide-scale employee education programs. Safety and security are top-of-mind for companies right now, ensuring teams know how to use these systems safely and with maximum impact.
In doing so, they’re strengthening the push towards a more data-driven mindset at every level.
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Generative AI best practices
Generative AI is a transformative technology, and as such, comes with a set of risks and challenges. Data privacy and security concerns are top-of-mind for many executives.
Companies creating the most widely used AI models have invested heavily in security, especially their enterprise models. However, that’s only half the story.
Companies purchasing these solutions must ensure they’re using them properly and that the data they’re handling is kept safe at all times. This is especially crucial if businesses handle critical customer data – for example, if a customer can pay for a product through a generative AI shopping assistant.
Some best practices include:
- Anonymizing customer data
- Using only tested and secure LLM models
- Training employees on how to use genAI properly and responsibly
Data driven decision making + AI: Getting started
Right now, most companies are still experimenting with individual use cases focused on small efficiencies. However, businesses that consider themselves market leaders are already starting to focus their efforts more holistically, allowing their whole organization to enjoy the benefits.
Business leaders should start by forming a governance group with people from different departments affected by generative AI. This group should be tasked with designing, guiding, and monitoring genAI initiatives and ensuring their learnings are shared with the rest of the company.
Here’s where leadership must make two critical decisions. The first concerns technology partnerships, as you need to choose a solution and a partner you can trust with your data and the data of your customers. On the other hand, leaders need to make strategic decisions that guide the whole endeavor, making sure the organization is aware of the details and able to handle the situation.
Finally, focus on use cases that provide the highest value to your company. There are many that sound good in theory – and have worked wonders for others – but have zero practical application for your context.
By taking an informed, strategic approach to generative AI, companies can become truly data driven businesses to drive innovation and growth.
