How CXOs Can Navigate the Heady Mix of AI, Crypto, Cloud...

Photo Imaging by Sanjay Gupta

If the recent hype around ChatGPT is anything to go by, the world seems to be reaching an inflection point in artificial intelligence (AI) and associated tools. (GPT stands for Generative Pre-trained Transformer, a large language model for generating text using deep learning.)

But AI is just one of several pathbreaking tech tools that CX and IT decision makers have at their disposal today to take their businesses to even higher levels of efficiency and agility. What will the future hold for contactless commerce and how is the customer experience being shaped and reshaped in retail? Should they experiment with the metaverse and non-fungible tokens (NFTs) and if so, how? What caveats lie ahead in a world pummeled by privacy challenges and user-trust issues?

Thankfully, insights from Harvard Business Review are at hand for CXOs to navigate the present with an eye on the future—in the form of a slim yet powerful guide of a book titled The Year in Tech 2023.

The book is neatly arranged into four sections with a view to providing some holistic crystal-gazing across a chosen set of emerging and mature technologies. The sections are named perceptively: The New Fundamentals (covering the metaverse, NFTs, stablecoins, contactless commerce, and the talent question); Fresh Takes on Mature Tech (the cloud, cookies, and ransomware); AI for the Rest of Us (data quality, no-code platforms, warehouse automation); and Trust Me (digital design choices and variation of digital trust around the world).

One of the best things about HBR’s content is its clarity and simplicity, and the same is reflected in this book—something that should be appreciated by the ever-pressed-for-time decision makers. And if they want to go in-depth into any topic that particularly interests them, there’s a ton of information on the web already.

Another highlight of the book is that it often seeks to present scenarios with an ethical lens. Socially responsible and forward-thinking enterprises will be able to benefit from such treatment.

Let me now give you a sampling of the insights gleaned from it.

One of my favorite passages is how the nature and function of the retail store will change dramatically in a contactless world [not fully contactless, I believe, but a mix determined by caution and convenience]. “It will become a space festooned with interactive displays and kiosks, virtual reality zones, and an array of robotic helpers, with fulfillment done from off-site warehouses or direct to the customer.”

A key factor in capitalizing on the opportunities and mitigating the risks, according to the book, will be the extent to which retailers can create “immersive, content-rich experiences that are highly personalized” for individual consumers.

Creating such personalized customer experiences will, of course, rely on the growing capabilities of AI tools. And while we are nowhere near generating $13 trillion of value each year (by 2030) predicted by the McKinsey Global Institute, the renewed interest in AI ever since ChatGPT broke onto the scene will only accelerate the competition among providers and the adoption among users.

Businesses of all sizes will play a role in such an accelerated adoption—and not just the Googles, Amazons, Facebooks, and Microsofts of the world who wield enormous compute and data power in their sprawling server farms.

The question is, How?

An interesting answer is given by Andrew Ng (of Baidu, Coursera, and Google Brain fame) in the chapter AI Doesn’t Have to be Too Expensive or Complicated. He posits that for far too long, much of the AI research was driven by software-centric development (also called model-centric development). In this model, the data is fixed and teams aim to optimize or invent new programs to learn well from the available data. Companies, especially tech giants, with large data sets used it to drive innovation. At AI’s current sophistication levels, however, argues Andrew, the bottleneck for many applications is getting the right data to feed to the software. In this context, it may be more fruitful to make sure companies have “good data” and not just “big data.”

This shift in approach implies that the data should be reasonably comprehensive in its coverage of important cases and labeled consistently. “Data is food for AI, and modern AI systems need not only calories, but also high-quality nutrition,” he writes. He calls the new model “data-centric AI development.”

To extend the benefit of AI to small and midsize businesses, no-code platforms that have been gaining traction of late will become increasingly important, the book notes in another chapter in the same section: “Where a team of engineers was once required to build a piece of software, now users with a web browser and an idea have the power to bring that idea to life themselves.” Most importantly, low-code platforms are making it possible to deploy AI without hiring “an army of expensive developers and data scientists.” (So ‘data scientist’ may not continue to be the sexiest job of the century after all!)

Among the mature technologies, the cloud will become even more compelling to business leaders in terms of embracing it for more workloads and use cases. The book cites how the cloud enabled the rapid development of the Covid-19 vaccine for Moderna, a relatively small firm compared to the pharma giants. Thanks to the flexibility and power of the cloud, Moderna was able to build and scale its operations on the cloud, and was able to “deliver its first clinical batch to the National Institutes of Health for phase one trial only 42 days after initial sequencing” of the virus.

Let’s switch back to an emerging star that continues to bewilder and bemuse CXOs across industries: the metaverse. For one, the book offers a relatively clearer definition of the metaverse: any digital experience on the internet that is persistent, immersive, three-dimensional, and virtual. Metaverse experiences enable people to play, work, connect, or buy (while the experiences are virtual, the things bought can be virtual or real).

Beyond the obvious use cases of gaming, virtual showrooms, and fashion shows, the book urges leaders to “look for applications” in less explored areas. “Almost every chief marketing officer already has made, or will soon make, a public commitment to sustainability-related environmental, social, and governance goals, and they will soon be measurable. What can you pilot in the metaverse that allows you to test more sustainable approaches to serving your customers?”

Such questioning by various stakeholders can open up the floodgates to innovative use cases of the metaverse and NFTs. The latter, driven by blockchain technology, have enabled a whole new range of ownership and trading activities in the digital realm.

Last but not the least, the book’s section on building and promoting digital trust, Trust Me, not only looks at interesting data on consumer attitudes and behaviors on digital trust around the globe, it stresses on the need for brands to make their design choices more carefully.

“When making design choices on a platform, managers should step back from short-term and narrow metrics like conversions and think through the broader questions about the value they create for their stakeholders,” it says. To get going, there are five questions brands must consider:

1. Are you transparent about prices and fees?
2. Do you make it easy to cancel your service?
3. Do you use default settings in a way that is genuinely helpful for customers?
4. Do you frame choices in a misleading way?
5. Do you create content that is addictive? [especially social media and video]

Most of the big tech platforms are routinely scrutinized and censured these days by regulators around the world for engaging in practices for short-term commercial gains that are harmful to consumers in the long term. We constantly hear of lawsuits, fines, and penalties.

However, businesses and brands that care for the long-term value they give to customers don’t have to wait for regulation to catch up—and make a fresh start themselves by following the best practices in developing digital trust and wellbeing. They can answer the above five questions honestly and take more proactive steps to protect consumers as well as their own reputation, and build lasting value for multiple stakeholders.

Thank you for reading and wish you all the best in treading the tech path in 2023 and beyond with caution, care, and accomplishment!

(Note: This post was first published on www.freshworks.com under a different headline and cover image.)

5 Ways CIOs Can Transform Their Companies into Data-Driven Enterprises

Data, data everywhere—not just the right kind to make effective decisions! It wouldn’t be wrong to assume that this is the common lament in most enterprise decision-making circles today.

On the one hand, companies are drowning in an unprecedented flood of data, structured as well as unstructured. And, on the other, CIOs, CMOs and other CXOs are struggling to get a handle on all that data, put it into the right perspective, extract and massage it into a usable form and take quick, effective decisions. The ones that can earn their firm the much-prized moniker of an agile business or a data-driven enterprise.

While making decisions in any enterprise involves a whole battalion of executives, LOB heads, managers, supervisors and many others, I think the job of enabling the whole organization to take decisions based on analytics rather than hunches (and perhaps, lunches) is most suited to the CIO. The reason is simple: who else has an across-the-board view of the data ecosystem of the company? And that too with the additional knowhow of how the information systems work (or can be made to work)?

So, without further ado, here are five ways CIOs can enable an environment for adaptive, data-led decision making in their organizations:

Making speed count: I know one thing for sure: organizations of all stripes today collect all sorts of data. Through all sorts of forms. By making innumerable number of calls to customers and prospects. And by sources such as the usual enterprise data captured through ERP and other operational systems. But how fast are you with the data you collect? Does it lie buried into file cabinets or dusty disks? Simply putting the data to quick use can make a huge difference to the organization. Following up on a hot lead in quick succession of the data collection process, for instance, will translate into revenue; too much delay, on the other hand, will make the prospect turn to your competitors.

Knowing your data from your metrics: This may sound simple to some and unnecessarily complicated to others. Yet this article on the Harvard Business Review site illustrates the difference and the significance of the difference quite clearly. Authors Jeff Bladt and Bob Filbin cite in the article the example of a YouTube video, asking the reader to guess as to how many views would qualify a video as a success. Now, the particular video in question had garnered 1.5 million views but it failed to do what it was supposed to do: encourage young people to donate their used sports goods. So, despite the impressive views, only eight viewers signed up for donation—with zero finally making the donation!

Not all results (or metrics) will turn out to be in such low extremes. But the point is well-made: you need to specify clear metrics in any data collection or numbers related exercise that will reliably give the true measure of success for the initiative.

Data is data is data, right? Wrong: When data is to be put at the heart of decision-making in an enterprise, it matters all the more that the data be accurate, consistent and timely. So, one may be under the impression that all the data required for a project, say, a marketing campaign, is available, if the data quality is not up to the mark, the results of the campaign would certainly be below expectations.

According to a data quality study by Experian Information Solutions, 32% of U.S. organizations believe their data to be inaccurate and further, 91% of respondents believe that revenue is affected by inaccurate data in terms of wasted resources, lost productivity, or wasted marketing and communications spend. If that’s the case with such a data-rich economy, one could imagine how bad the shape of things would be in a country like India, where data collection and research are relatively new fields and far from being mature scientific disciplines. In this context, the need for best practices as well as tech tools in maintaining high data quality cannot be over-emphasized.

Democratization of analytics: How many of you can remember the era of generating sporadic MIS reports for the consumption of the privileged few? Well, that era is long gone. However, most companies are still chary of sharing key statistics or analytics data beyond the confines of top or senior mid-management. But gradually, this state of affairs, too, is set for a bold change. Some call the coming wave as the democratization of data or analytics, in which actionable data percolates to the lowest links in the organizational hierarchy.

Having said that, democratizing data does not mean dropping a huge spreadsheet on everyone’s desk and saying, “good luck,” as Kris Hammond, Chief Scientist at Narrative Science points out in this article. On the contrary, he explains what it involves simply and emphatically: “Democratization requires that we provide people with an easy way to understand the data. It requires sharing information in a form that everyone can read and understand. It requires timely communication about what is happening in a relevant and personal way. It means giving people the stories that are trapped in the data so they can do something with the information.”

Point well made: unless people can take “informative action,” the analytics tools or the extracted data will have little value for the people or the organization.

Analyzed this, have ya? Now visualize that, too: I’m not sure if you noticed but the Internet has been flooded with a new tool of information dissemination in the past couple of years. It’s called the infographic. For most of your searches on Google, there are now an eye-load of infographics, those illustrative diagrams that give you the needed information with icons, pictures, graphs and anything non-text.

Much less noticeable but equally important, a similar movement is underway within enterprises in the context of data analytics. Vendors such as Tableau Software and Qlik Technologies are leading the charge in this emerging segment, referred to as the visual analytics market

According to specialist consulting firm Atheon Analytics, visual analytics “brings together computer science, information visualization, cognitive and perceptual sciences, interactive design, graphic design, and social sciences.” (To see the power of visualized data in action, watch this slightly old but enormously impactful video, the Joy of Stats, of Swedish statistician Hans Rosling, who is often referred to as the “Jedi master of data visualization.”)

The above are only a few of the multiple ways in which CIOs can bring the hidden power of data to the forefront of organizational ability and agility. There are plenty of tools and technologies available but each organization must find its own best-fit path to data-driven success. The key is to start the data journey as early as possible and do so in right earnest.