AIs Have Patterns; Humans Have Memories

 

Image by Claire on Unsplash

“Nostalgia is a sweet, incurable disease,” I remember posting this on one of my social feeds sometime back.

Now, why did this particular thought—and not anything else—surface as I began writing this post?

My best guess is that this post is about memories and AI, and because I’m generally a nostalgic creature, that’s what my brain came up with. But we would never know for sure.

These days, the increasingly capable AIs remember a lot of things, including from past conversations with you. And they are getting better at providing more relevant or contextual answers to your prompts.

But…but…

For all their monstrous computational prowess and the supposed ‘smarts’ of remembering, the AIs do not have memories—certainly not in the way humans have.

What the AIs have is a vast pool of data and the blazing-fast ability to pick out a matching pattern. It’s all statistics, mathematics, algorithms…and yes, the brute force of hundreds or thousands of CPUs and GPUs.

They can do all of that pattern-matching ad infinitum. But they have zero memories. None whatsoever.

It’s humans who have memories.

It’s humans who are transported back to a joyous moment in childhood at the touch of a scent from a favorite savory. 

It’s humans who zip across time to relive their crazy youth when a song from their college days turns up on the playlist.

And it’s humans again when a blurry video—stored somewhere in the AI cloud—of their wedding makes it vivid like it was yesterday, even when it’s played thirty or forty or fifty years later. 

The sounds, sights, and smells associated with each memory come calling to the doorstep of our mind as well.

AI can mimic (read ‘steal’) our art, our stories, our music. But it can never stop us from creativity and imagination (unless all you choose to do is watch reels and give bad prompts). 

And, of course, no AI can make and cherish memories like we humans do. Thankfully.

How about SLMs - Small Language Models?

 

Image by BNP Design Studio

I know large language models, LLMs as they are called in AI circles, are all the rage these days. But lately, I’ve been thinking of SLMs — small language models.

Not that I’m the first one to think small in this way. For example, when people are angry with each other, they prefer to speak in SLMs.

Exhibit A: The husband has a mere twitch of the lips when the wifey stops him right there. “Shut up! Don’t say a word!” [I wanted to flip the gender stereotype here and wanted the hubby to be saying the shut-up command, but something told me it wouldn’t fly, so I shut that thought out.]

SLMs are especially popular with other (often better) animals than humans. A coo-oo, clack-clack or screech often does the job better than voluminous human speech.

And then, sometimes, the smallness of some models becomes so infinitesimally small that no words — and hence no language, not to speak of models — are required at all.

A sharp look in someone’s direction, a press of the hand on a shoulder that needs pressing in a certain way, or a shared moment in silence is all that is needed.

Sometimes I wonder what’s going to happen with so many towers of LLM babble being created and business models being built on top (models atop models, huh?) We might even be drowning in a frothy alphabet soup without digesting a single letter.

And just as I was going on further with this post, a voice hit out at me from somewhere: “Time to shut up now!” (Thankfully, it’s my own goddamn brain and not a Neuralink implant freaking out.)

I looked at my analog watch and felt happy to see the second hand moving, regardless of what’s going on in the world.

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.)

Will AI help companies deliver better CX in a multi-experience future?

Image: Freshworks

Customer experience (CX) may mean different things to different companies but it means only one thing to customers: whether they liked what a company offered or not in a given context or setting. And whether they are going to have that offering again—and, yes, whether they would grab anything else the company wants to sell them. Perhaps they would also spread the good word on the product or service used.

Alas, in most cases, it is the bad word that gets thrown around—often wildly and out of the company’s control into the ruthless arenas of social media.

For the past several years, most organizations have responded by throwing back more and more technology to fix their CX efforts. According to research firm Gartner, global spending on customer experience and relationship management (CRM) software reached $48.2 billion in 2018, a growth of 15.6% over the previous year.

But, despite the rising expenditure on tech and the best intentions of companies, the struggle to get a handle on customers and delight them with exceptional support and service continues. So, what is going on here? What challenges are companies facing in putting together a complete picture of their customers and serving them better? Whatever happened to the promise that up and coming technologies such as chatbots and artificial intelligence (AI) were supposed to hold in equipping organizations with the wherewithals to delight their customers (and do so at lower costs)?

We spoke to a few industry analysts and experts to dig deeper and see what gives.

One of the fundamental problems, they say, has to do with the ability to use the right data to get a comprehensive view of the customer. “After all these years, having a 360-degree view of the customer is still on the agenda for companies. One of the issues here is, what do you understand by a 360-degree view? Is it an electronic Rolodex? Is it an extended set of data about the customer, something that different vendor tools are now increasingly exchanging? Or is it something else?” says Brian Manusama, a senior director analyst at Gartner.

On his part, he offers a simple, functional definition. “If you ask me, it is the right data in order to serve your customers well. It can have just two or three components or even hundreds of components, including different metrics such as customer sentiment or behavior,” he avers.

There are other aspects to this challenge. According to Ray Wang, principal analyst, founder, and chairman of Constellation Research, “On the one hand, most companies don’t have access to all their internal data. This lives in siloed departmental systems that rarely talk to each other. On the other hand, most companies now rely on more external data which is often seen as not secure, not as safe, and in different data formats. The last part is that data often does not tie back to business processes or journeys—which means it’s hard to determine a recommendation or next best action.”

The ‘recommendation or next best action’ typically refers to suggestive responses provided by the AI engine that is increasingly getting embedded in chatbots, CRM, and other business software. Such recommendations are based on a knowledge repository comprising standard answers mapped to frequently asked questions, previous customer interactions, etc. It is now common industry wisdom that for better recommendations, it is necessary to have a rich data repository and a finely tuned machine learning model.

Wang points to a basic flaw in how most organizations have traditionally dealt with customer experience. “Most [customer] journeys have been designed for internal efficiency, not external efficiency. Customers don’t care what department you are in and this means the design point must revolve around the customer,” he says. To correct this anomaly, a lot of organizations are now “retooling” to support this from an internal process and technology point of view.

Another big headache for companies is to make their disparate CX systems talk to each other and work as an integrated solution. Today, there is a dearth of holistic solutions that can manage the entire customer lifecycle—from acquisition to retention to life-time value (LTV) management. “There are different piecemeal offerings from different solution providers. For example, there are a lot of sales analytics companies out there who help sales teams optimize their processes; likewise, there are a lot of marketing attribution and automation software that have AI capabilities to help marketers spend their budgets more optimally and so on. Similarly, on the customer success side, there are tools for churn prediction and other areas, but the overall customer journey stack is broken,” says Swaminathan Padmanabhan, director of data science at Freshworks.

According to him, it will be of fundamental value to customers “if we can tie all these capabilities together.”

A multi-experience world
Customers are now interacting with brands through a complex mesh of interfaces and touchpoints—physical as well as digital. “Do you know how many different ways one can order pizza from Domino’s? Twenty four!” says Manusama by way of an example. Such ordering ease includes the use of phone, text, social media, and voice assistants, besides showing up at physical stores and giving the order over the counter.

“We are moving toward a multi-experience world with three different modalities of customer experience across multiple digital touchpoints—gesture, text, and voice,” he says. At Gartner, analysts now call upon tech leaders to get ready to serve ‘the everything customer’—one who requires conflicting things at the same time: to be treated like everybody else but served on their own unique terms, to be connected yet sometimes left alone.

When it comes to customer experience, companies are compelled to move from a reactive way of working to a more proactive way. And while this complexity is generally good for customers, as it gives them more choice and hands them greater control over how they want to be served, it leaves companies in a constant state of flux.

The growing role of AI
Analytics and AI are playing a more important role than ever in improving customer experience, according to Wang. “We are moving from gut-driven to data-driven decisions and this requires a ton of analytics to quantify and anticipate customer needs and requirements,” he says. The rising capabilities of AI offer hope to organizations. “Over time, machine learning will support precision decisions, which means better personalization, fraud detection, and customer  experience,” says Wang. He doesn’t hesitate to call AI “the biggest shift” in CX.

Padmanabhan refers to a six-layer maturity model of AI to lay out the path ahead for customer engagement. In increasing order of sophistication and capabilities, these layers are Data Representation Layer, Knowledge Layer, Ranking and Relevance Layer, Forecasting Layer, Recommendation Layer, and Autopilot Layer. In his opinion, most companies and systems today are operating at the Ranking and Relevance Layer.

“For example, when a customer query comes up, the bot ranks the different solution artefacts and suggests the best solution artefact. Similarly, when you have a bunch of sales leads, the lead scoring system ranks them according to their probability of conversion,” he explains.

As the AI system matures, one can expect AI-based recommendations such as “increase the ad budget by 15% to 20% for a 10% increase in customer acquisitions” or “use this workflow to optimize customer experience” and other actionable insights like these.

The pinnacle of AI capability, according to Padmanabhan, would be realized in the Autopilot Layer. As the name suggests, at this level, AI can replace some common functions performed by service agents or other team members. Rather than recommend something to be done, an AI can execute it as well.

 Not that AI will possibly replace humans fully—nor is that the direction taken by companies or recommended by experts. “Today, we don’t say that we are going to completely replace human labour but say there are a lot of repetitive tasks that are involved in the support workflow or the sales workflow or the customer success workflow which can be automated. So the agents’ time can be better spent by using AI,” says Padmanabhan.

Keeping the human element in customer engagement while still using AI is “actually a question of service design,” says Manusama. What customers want are four things in how they are served: effortless, quick, convenient, and seamless across different channels. “Many companies are discovering that they can do this through self-service. However, for more complex situations, having the human touch will often be more relevant or appropriate. Basically, companies need to answer this question: Where is the business value getting generated for my customers?” he adds.

Another trend he sees is customer service vendors consolidating their solutions into engagement clouds. “Silos that existed previously are getting broken down,” he observes.

Wang’s bet is on a future built around “ambient experiences”. What we have to ask ourselves, he says, is this: When do we automate, when do we augment with humans, and when is something a pure human interaction.

The role of engagement clouds or customer engagement platforms assumes greater significance in this context.  “We need common data models, great integration, and very good journey orchestration. You can do it in platforms or you can do it with really good tooling. I’m betting that the platforms will do 80% of the work and the tooling will carry the other 20%,” says Wang.

Whichever way organizations tilt, AI is likely to play a greater role in a multi-touch, multi-experience world. Now, depending on how they are able to lend a helping hand—through automation with a smile or by being pesky or ‘unintelligent’—customers will choose to give them a thumbs up or thumbs down.

(This blog post, which I wrote as a lead editor in the corporate marketing team at Freshworks, first appeared on www.freshworks.com.)

Digital, AI tools easing up legal work for companies

Image: Pixabay.com

When Vaishali Lotlikar joined Wanbury Ltd’s legal department sometime in 2014, little did she know that locating a particular contract or assembling a legal brief would involve sifting through piles of documents, and wastage of precious hours and money in the process. “There was a lot of employee churn in the legal and marketing departments. Nobody really knew where the contracts were kept and what was there in each for the company to keep an eye on,” she recalls.

To streamline operations, Lotlikar and her team gathered all the contracts, digitized the same and put them into a document management system after tagging them for keywords, so that they could be easily searched when accessed from the firm’s servers by authorized personnel. The tool was purchased from PracticeLeague Legaltech Pvt. Ltd, a specialized provider of software and cloud-based solutions for law firms and corporate legal departments.

“I had used their technology at Glenmark and USV,” says Lotlikar, adding that the familiarity helped her get up to speed. “Today, if our international business head wants to know the particulars of a contract, I can get those details within minutes on my laptop—irrespective of the city I’m in.”

This is simply a case in point. As the volume of compliance and other legal requirements increases for companies across industries, technology tools that can ease the legal burden are in great demand. Khaitan and Co., for instance, has been experimenting with technology for quite some time now, according to its chief operating officer Nilanjan Ghose. “We were the first among law firms in India to use software for accounting. Around 2007, we started working with PracticeLeague to develop our own time and billing solution, which is core to our operations.” Over time, he says, the billing solution morphed into what is now known in legal circles as practice management software (PMS). He likens it to an enterprise resource planning software used for operational management by a majority of companies.

For Khaitan and Co., PMS helps in all kinds of processes, including accounting, billing, collections, administration and human resource functions. The company is now integrating new modules into it—an attendance module, for instance.

Digital tools are also helping law firms expand in size or scope. “Many of our clients acknowledge that it is because of technology that they could grow from a 50-60 person law firm to one employing 500-600,” says Parimal Chanchani, founder and director of PracticeLeague. And while there are several technology providers operating in the legal space—LexisNexis, LegalSoft, Thomson Reuters (ProLaw), Jurisnet and dozens of others—Chanchani says “nearly 60% of the corporate law departments and over 40 top law firms in India” use its software.

“You cannot manage a compliance workflow through Excel sheets; everything is now getting automated,” says Chanchani. “What we have is a complete, cloud-based solution sitting on Microsoft servers (Azure cloud). Customers can simply start using any module by just plugging into the platform.”

Role of artificial intelligence (AI)
PracticeLeague has also begun embedding AI into its software. For this, it has opted for Watson—an AI tool developed by International Business Machines Corp. (IBM). Praveen Kulkarni, who heads technology design and delivery at PracticeLeague, says Watson is implemented if a client wants to analyse a contract sent to it by, say, one of its suppliers.

For instance, if a firm wants to become the supplier of a pharma company, it will be required to submit several documents. Based on these submissions, the pharma company will send it back several documents to sign such as a non-disclosure agreement or a supplier registration agreement. If done manually, a person from the legal department would need to pick up the relevant content (from the submitted documents) and “draft and redraft the agreement that would take several hours”. With PracticeLeague’s Document Assembly, a Web link is sent to the supplier. “Once the required documents are uploaded through the link, the tool starts asking questions such as the category of supplier, payment terms, etc. After these questions are answered, a ready contract is automatically prepared through the system for the legal department to review and approve,” says Kulkarni.

However, if the pharma company wants an analysis or summary of the multiple documents it receives from suppliers themselves--the documents are exchanged by both parties for signing--then PracticeLeague uses Watson. What Watson does, explains Kulkarni, is “extract certain portions” of the agreement—for instance, contract type, liabilities or jurisdiction, or a termination clause. “So instead of manually picking up these details, they appear on the screen in front of the person reviewing them,” he adds.

But what if the system fails to understand any particular detail? For such situations, PracticeLeague has built an interface through which the reviewer can feed additional information back into the system so that the same can be picked up correctly by Watson the next time. “AI gets better with more and more data fed into it,” says Kulkarni.

Wanbury and Khaitan and Co. are yet to start using the AI tool, but acknowledge the role AI can play in further improving efficiencies for them. “While I have not used the AI tool, I believe it can automate repetitive tasks performed by legal professionals and also suggest the possible options to be taken in a legal case,” says Lotlikar of Wanbury. Nevertheless, she adds that while all of that can be done in the legal field, “strategies thought of by human beings are also important and cannot be fed into a system”.

“AI can help us in faster turnaround times for cases and in due diligence on contracts,” concurs Ghose of Khaitan and Co., but adds that human intervention and checking will also be required. “For example, certain words could be misspelt and thus be unreadable by the machine, or certain clauses could be interpreted differently. So you need somebody to go through the clauses manually,” he adds.

Other law firms using AI include Cyril Amarchand Mangaldas which signed up with Canada-based Kira Systems for the latter’s AI technology in January 2017. On its part, PracticeLeague is now working with Google and Amazon to integrate their AI technology into its solution and, after that, plans to work with Microsoft as well.

(Note: The above article first appeared on Livemint.com.)