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The Bet: Incorporating Generative AI Into Your Broader AI Strategy

It’s been pretty hard to miss the crazy buzz around large language models (LLMs) and generative AI in the enterprise tech space.

Despite the fact that we’re already seeing the hype cycle kick in with ChatGPT’s traffic falling for the first time, it has unquestionably kicked off a mad rush among enterprise tech vendors to not be the only ones left standing outside the big Generative AI tent.

But this rush to incorporate generative AI at all costs has left many enterprise IT leaders trying to separate hype from real opportunities to leverage it and make a strategic difference in their organizations.

As the Wall Street Journal reported:

Businesses are facing an influx of new artificial-intelligence tools, many of which overlap and cause confusion for employees, as corporate-technology sellers race to capitalize on the generative AI trend.

This situation creates a somewhat bizarre bet. There’s little question that generative AI is going to enter the walls of the enterprise in some way — it’s being baked into almost everything in some fashion. So, the real bet is how much attention you should pay to it, now and in the future, and in what areas.

The Briefing

The term artificial intelligence (AI) was already fraught with confusion. It’s a catch-all term that, as a result, doesn’t mean much of anything.

While Generative AI is a little better in that it at least refers to a specific application of AI, how an organization or tech company applies it varies wildly. So, like the broader term, the mere statement that a tech company is incorporating generative AI doesn’t tell you much.

But as you evaluate your approach to generative AI, there are really three primary factors you should consider: corpus, context, and use case.

The Corpus Starting Point

If you’ve been following the evolution of this space, you’re probably starting to hear some chatter about the importance of an LLM’s corpus.

The corpus is critical, because it is the foundation on which a provider builds its LLM. As FastCompany explained in its recent primer on the subject:

When used in the artificial intelligence realm, the term “corpus”…refers to the metaphorical “body,” or collection, of data that was used to train the AI. This corpus is the material the AI reviews to become intelligent in whatever it was designed for.

Every AI’s corpus will be different, because it is humans who decide what kind of data they want to train an AI on. And the corpus the humans decide to train the AI on will depend on what they want the AI to be proficient in.

As ChatGPT burst onto the scene, the corpus didn’t matter much as it was the only real game in town. But with several competing LLMs emerging and a bevy of heavyweights promising to build their own, understanding the corpus will be essential in determining its applicability and potential effectiveness to your particular use case.

Most importantly, as Zoho’s Director of AI Research, Ramprakash Ramamoorthy, explained during our recent interview (see below), you will likely need the flexibility of accessing multiple LLMs, all with different corpora (what an amazing plural, right?), to meet the range of business use cases you’ll eventually want to tackle.

Creating Meaningful Business Context by Integrating AI Approaches, and Public and Private Data Sets

The second major consideration when it comes to how you apply generative AI is your ability to use it in context.

The most common uses of ChatGPT, or any of its newer competitors, doesn’t provide any context. You chat with the core model and get what you get. Microsoft’s Bing version and the new ChatGPT web plugin allow you to point the LLM at live search data, but that’s still pretty generic.

As Zoho’s Ramamoorthy explained in our interview, you only release the true power of generative AI when you combine the power of LLMs with two things: other approaches to AI, and targeted public data.

In Zoho’s case, that means combining its newly released, ChatGPT-powered generative AI functions with its homegrown AI tool, Zia. Zia has access to all a company’s internal data stored in Zoho’s suite of CRM, marketing, accounting, HR, customer support, and other tools. It is then able to combine that intelligence with summarization and generative capabilities from ChatGPT (without sending any data out of a customer’s environment. Ramamoorthy covers Zoho’s approach to privacy in some detail).

Likewise, he shared examples of combining targeted public data, for example a competitor’s publicly available sales figures, with your private data and then leveraging LLMs to answer questions about competitive positioning.

The key in understanding when and how you may be able to leverage LLMs is in understanding your ability to create this sort of context via various AI tools and a mix of data.

Focusing on the Use Case

The final “aha” moment for me in my interview with Ramamoorthy was at the end when I asked him about how enterprise leaders should be looking to the future.

“Process optimization and revenue maximization will be the goals of AI for any big enterprise,” he said.

I actually think there are more use cases than merely process optimization and revenue maximation that will create value for enterprise leaders (its use in strategic planning, for instance, could be very powerful), but the underlying point is critical: the effectiveness of an LLM in the enteprise will be in direct correlation to your effectiveness in applying it to the right business use case.

Of course, that sounds obvious. But much of the early talk around LLMs has centered around fairly transactional use cases. Those uses will likely drive some efficiency gains and bragging rights (for a short while).

However, the real game-changing uses of generative AI will emerge as organizations focus on how they can apply generative AI deep within core business processes, revenue generating engagements, strategic planning activities, and other places where its impact will be profound.

My interview with RamamoorthyI goes into much more detail and provides great insight into both how Zoho is approaching this evolution, and what it may mean to you as you seek to leverage it.

The Brass Tacks: Invest, Pass, or Hold?

This is a tough one for me because there’s no simple answer and no single bet.

As I mentioned, generative AI is coming into your house no matter what — and if you aren’t already, you’ll be fielding mountains of questions about leveraging it.

So, you really don’t have a choice but to invest. Being a naysayer on the sidelines is just going to get you a one-way ticket to the stands.

At the same time, some patience, skepticsm, and due diligence is prudent.

I think companies like Zoho, BigPanda (which recently introduced a native generative AI interface to its tool), and Automation Anywhere (it is leveraging it to, amonth other things, allow business users to extract information from across multiple systems) are doing some great stuff.

Still, it only matters if it matters to you. You can easily get caught up chasing every new evolution of this tech — and that would be a bad bet.

Use the three factors of corpus, context, and use case to place targeted bets on where you apply generative AI in meaningful ways. But when you place those bets, beg big.

The only way you’re going to make a significant difference with this technology is if you go heavy and get deep. Otherwise, it will just be surface fluff. And if you can’t go deep or don’t trust the bet, don’t make it.

So, that’s the brass tacks for my point of view, but what do you think? Agree? Think I’m completely off? Let me know!

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And don’t keep this conversation to yourself. Invite your friends and associates to weigh in!

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Disclosure: Zoho is a client and paid for my travel to its Zoholics event, where I conducted the above interview. However, Zoho had no editorial input into or review of this content. BigPanda is also a client.

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Note: I’m introducing a new format for The DX Report in which most issues will follow a structure of “the bet,” “the briefing,” and my recommendation. I’ll explain it all in the next issue, but I’m previewing the approach today!

The Bet: Making Privacy and a Unified Platform Central to Your IT Strategy

There are a couple of big movements afoot within enterprises — and they appear to be synchronizing.

The first is privacy. It’s a topic that’s been bubbling for a long time, but has recently come to a head with the advent of generative AI. The question that has loomed over enterprise leaders has been how much to care about it.

The second is the rising dominance of enterprise platforms — integrated suites of products that cover a large swath of enterprise needs. Here, the question is whether or not they are more good than bad. If you invest in platforms are you giving something up for the convenience and built-in integration?

Note: Keep reading for a video interview with Zoho’s Tejas Gadhia on this important topic!

The Briefing

While the issues of privacy and platforms are interrelated and increasingly intersecting, making your bets on them also entails very distinct calculations. So, let’s begin by looking at each one separately.

Privacy: Does Anyone Care?

Privacy has been an above-the-fold issue for enterprise leaders for some time. But it’s mostly been about compliance. GDPR, HIPAA, and countless other regulatory statutes have made privacy something that every IT organization must worry about.

But more recently, consumer-level privacy concerns have caused organizations to look at the issue more closely — both in customer-facing use cases and internally. Concerns around things like company trade secrets being fed into large language models (LLMs) has only heightened concerns.

Among other developments, this heightened awareness of privacy — combined with a growing understanding of how much data is harvested by the browser — has led to a rash of new privacy-centric browsers (e.g., Brave, Duck Duck Go, etc.) and a broader focus on privacy by even major browser players like Apple with its Safari browser and Google announcing the elimination of third party cookies in Chrome.

Still, ask most enterprise IT leaders how much they’re paying attention to these privacy concerns, and you’ll likely get a shrug. There appear to be much bigger fish to fry for browser-level privacy to rise to the top. Likewise, there seems to be only mild interest or concern among rank-and-file workers, who generally appear happy using whatever enterprise-mandated browser you offer.

The big question facing IT leaders, however, is whether this is one of those issues — much like most parts of the cybersecurity and risk estate — on which IT should be leading the charge? The risks certainly appear high enough to warrant consideration.

The challenge is that privacy concerns can often run counter to the other, intersecting trend: the evolution of enterprise platforms.

Platforms: At What Cost?

The best-of-breed vs. integrated suite decision has been a back-and-forth tennis match for decades. The tide has tended to shift with the emergence of new technologies.

As new technologies change the landscape, enterprises would gravitate to best-of-breed solutions to take advantage of the innovation coming out of small, purpose-built start-ups. Then, as things settled and matured, the technology would get incorporated into larger suites, negating the need for entire classes of technology and offering enterprise leaders the opportunity to simplify and standardize.

At first blush, the rise of the modern enterprise platform would appear to be the latest ball batted over the net in this protracted match.

There’s some truth to that viewpoint, but I think it also misses a larger, more important point: platforms are now the foundation of the enterprise tech estate.

The defining characteristic of the modern tech stack is its complexity. The relatively small footprint of yesterday’s tech stack made this tennis match possible. While there was plenty of hard work and costs involved, we could move between best-of-breed and integrated suites without it being a death match.

That’s no longer the case. And that’s also why we’re no longer referring to these as integrated suites and are instead calling them platforms.

The distinction is that they have become the foundation upon which enterprise IT leaders will build everything else. It’s no longer an either-or question, it’s an and. Innovation is still going to continue to occur on the edges, but it will happen on top of the enterprise platforms that are the bedrock of the tech stack.

Privacy and Platforms Intersect

It is this criticality of the platform that brings privacy to the forefront of concern for enterprise IT leaders.

If the platform is the foundation, then both which platform you choose, how you access it, and how you integrate to it become all important. Everything is connected.

And I think this connectedness is why we’re seeing some interesting moves in the market that are attempting to address privacy and platforms as an integrated whole.

The most recent — and arguably most vigorous — move has come from Zoho which recently introduced a privacy-centric browser it calls Ulaa. What is most interesting about its effort is how much it is leaning into both sides of this debate.

Not only is it unabashedly making the case that privacy should be a primary concern to the enterprise, it is also making its platform a central part of its browser strategy. While it is still in the early stages, it is planning on deep integrations between its platform and its browser to deliver improved performance and a more seamless experience.

I recently had the opportunity to discuss this interesting intersection of privacy and platforms with Zoho’s Ulaa Evangelist, Tejas Gadhia. You can watch it below for his explanation for why the company is wading into this deep water.

The Brass Tacks: Invest, Pass, or Hold?

For me, this one is pretty straightforward. I think that both privacy and platforms are winners. Let me give you my rationale in reverse order.

As I laid out above, I believe that the current complexity of the tech stack has shifted the landscape. It’s no longer a tennis match. You need to have a set of platforms that sit as your rock-solid foundation, on top of which you innovate.

In all likelihood, that foundation will represent a collection of platforms.

The reality is that you’ve probably already made those bets and there’s little advantage (and lots of cost and heartache) in unseating them. But if you have a bunch of competing and overlapping solutions, carefully choosing a set of platforms to serve as your foundation is a super strong investment to make. Platforms win.

Privacy is also a winner and something you should be investing in a lot more than you probably are today. There are two reasons. The first is that it’s unlikely that the regulatory environment is going to ease. In fact, it’s more likely to get even more stringent.

But the second reason is more important: I do think people — your customers and your employees — will begin to care deeply about the subject. While I think Apple and Zoho are on the leading edge of this, we are starting to see increased awareness across the board — and the rise of generative AI, with its incumbent privacy issues, will raise both awareness and the stakes even more.

Most importantly, as Tejas told me in our interview, what do you have to lose? There is so little downside to an investment in privacy that the greatest risk is in doing nothing.

While I first questioned Zoho’s entrance into the browser market — particularly with a privacy-centric play — I’ve come to appreciate that it may be at the leading edge of a broader shift in how the enterprise builds its tech stack: putting privacy and platforms at the foundation of it.

So, that’s the brass tacks for my point of view, but what do you think? Agree? Think I’m completely off? Let me know!

Leave a comment

And don’t keep this conversation to yourself. Invite your friends and associates to weigh in!

Share

Disclosure: Zoho is a client and paid for my travel to its Zoholics event, where I conducted the above interview. However, Zoho had no editorial input into or review of this content.

Too Much Data is Worse Than Not Enough of It

In today’s experience-driven economy, there is no greater sin than not having enough data. Data is the fuel that powers the modern digital enterprise. It enables high levels of efficiency and optimization by eliminating guesswork and traditional approaches to decision-making. And it powers your ability to create highly personal and customized experiences for your customers, employees, and partners.

So, we need data — and lots of it.

But within that statement is a dilemma. Merely capturing data is not enough. In fact, there are two reasons collecting data without a cohesive strategy can actually be worse than not collecting enough of it.

First, having a massive amount of data without both a strategy and the ability to manage and glean insights from it means that you'll be spending significant resources on all of the data mechanics — without receiving any of the benefits.

Second — and much worse — you will be laboring under the belief that you're doing the right thing and will have achieved something by having all of this data. But unless you are able to unleash that data, you will be setting yourself up for poor decisions and bad outcomes.

This dilemma is part of what I learned during the interviews I conducted for our video Unleashing Data, the second episode of the Unleashing Transformation series, presented by Zoho.

In it, Zoho's data analytics evangelist, LSP, said that the most important thing that enterprise leaders need to do is to focus on what he calls actionable insights — but what does that really mean?

(Check out the whole video below. It's totally worth 6 minutes of your time!)

What are Actionable Insights?

Like many buzzwords, actionable insights seems logical. It's something you can make sense of on the surface — but it starts to break down as you really get into it.

As it sounds, an actionable insight is something that transforms raw data into something that has these two attributes:

• It's an insight — it provides you a perspective or understanding that you didn't have before; and

• It's actionable — you can use this newfound perspective or understanding to take some action, one that will presumably lead to a positive outcome for your organization.

So far, so good.

But what does it take to transform raw data into this type of actionable insight?

To start with, it's helpful to remember (or learn about) the DIKW Pyramid. If you're not familiar with it, it's an acronym (Data, Information, Knowledge, Wisdom) that represents an evolution and refinement process that takes raw data and eventually turns it into wisdom.

While there is some debate over this idea, I believe that wisdom can be taken as synonymous with an insight (some consider an insight as a step between knowledge and wisdom). In either case, the process of going from data to insight/wisdom is a refinement process that takes a lot of raw material.

And that's where this process goes wrong for most organizations.

Why You Need a Hot Take on Your BI and Analytics Tools to Create Actionable Insights

Based on this need for lots of data, organizations have invested significant resources in various forms of instrumentation and data extraction capabilities. Needing a place to store all of this data, they have then invested in data warehouses, data lakes, lake houses and whatever term the data industry dreams up next.

To be sure, being able to cost effectively store and catalog all that data is a significant issue — and one that can quickly overwhelm an enterprise's organizational and financial means.

But it's still just the first step in the process.

The goal — the absolute need — is to refine all of that data through the DIKW process to transform it from mere data into something that is genuinely meaningful for the organization. The challenge is that many of the tools attempting to address these issues are only focused on the (undeniably difficult) building blocks of ingesting, cataloging, and enabling a presentation layer for massive amounts of data at scale. Essentially, most of the data industry is focused exclusively on the data-to-information (and maybe a little bit of knowledge) refinement process.

It's understandable.

The final stages of the refinement process — what is necessary to get to actionable insights — requires more than generic approaches to data management.

The final stages of the transformation of data into insights require context. This context comes in two forms:

• Context about the relationships between the data and the organization's operating and business model so you can understand the story the data is telling, and

• Context in the form of the questions the organization is attempting to answer.

Therefore, getting through this final step requires that you both leverage tools designed to help you provide this context, and that you recognize that it's about more than just the tooling.

Getting to Actionable Insights Requires Tooling AND Cultural Shift

One of the things I learned during my conversations with the enterprise leaders I interviewed for the Unleashing Data episode is that while the right tooling is essential, it's also not enough.

To realize this full transition, enterprise leaders need to drive a cultural shift within their organizations. This cultural shift involves changing their teams' relationship to data collection. They need to understand its importance and the context that will help it have meaning.

"It's no longer about transactional systems and productivity. It's about what can these tools help you do better," said Zoho's LSP in the episode. "There's so much accumulation of data...that it's going to open up insights that you never knew about your business."

In addition, it requires a shift in how people leverage that data to go beyond pretty graphs and instead focus on how you can transform that data into insights and then take specific, targeted action based on those insights.

"Every organization has a lot of data and they know it's going to give them insights," LSP continued. "But the biggest challenge has been data strategies. Those who figure out how to leverage data are the ones who are winning and will continue to win. Data to insights. Insights to action. It's all about actionable insights."

Note: Zoho is a client of The Digital Experience Report, but had no input or control over this analysis.

I was speaking with a senior contact center leader for some upcoming research when he made this statement. We were discussing the balance between the need for efficiency and cost reduction on the one hand, and the chorus from, as he called it, the ivory towers of punditry claiming that the customer experience is more important than anything else.

I had to chuckle. I mean, I publish something called The Digital Experience Report. I must be one of those in that ivory tower, right?

But his point was well-taken. The customer experience does not and cannot operate in a vacuum.

And it’s not just from an organization’s “must take cost out” perspective either. As another executive said, “Sometimes the customer doesn’t want to talk or need empathy. Sometimes they just want to get something done and move on.”

There’s no question that we’re in a period of evolution in all aspects of customer contact, service, and experience delivery. Volumes, costs, expectations, and the relative importance of the experience are all increasing simultaneously — all while organizations attempt to prepare for a potential market downturn.

The question for contact center, customer experience, and IT leaders is how to strike this balance between these seemingly competing needs of delivering efficiency and a differentiated customer experience.

It’s a question without an easy answer, but with some promising advancements on the horizon.

One Approach: Blended Conversations 

Striking this balance is no simple task, particularly because it comes in two forms. On the one hand, you have the need for an organization to manage costs, but to deliver a winning experience. On the other, for the customer to get what they want with no fuss, no muss, but also get empathetic, human attention when they want or need it.

The result of organizations struggling with — and failing at — striking this balance is evident in the long history in almost every organization of failed attempts that either caused experiences to fall flat or costs to soar — and sometimes both!

Everyone has their story.

Yet, it’s a balance that organizations must strike to compete in our experience-driven economy.

And technology providers across the spectrum are rushing to try and help enterprise leaders do so. One of the latest is Zoho’s recent release of its latest version of Zoho Desk, which includes what it calls Blended Conversations.

The concept is that neither humans nor machines can really meet both ends of this balance effectively. Whether from a cost or experience perspective, the company believes that both bots and humans have something unique to offer, but organizations have been forced to choose one or the other.

Instead, the company suggests, why not blend them?

According to the company, this approach “allows customer service agents to deliver the best experience in the moment by delegating the majority of manual and transactional tasks to bots, while remaining in control of the overall service experience.”

It’s an intriguing idea, and one that is already reflected to varying degrees in many major Customer Experience (CX) platforms. What is most interesting, however, is the explicit acknowledgment that this should not be a one-way hand-off and that, instead, there are some things that each does better, so we should allow them to do so in tandem.

According to the company, doing so will allow organizations to better strike this elusive balance.

The Increasing Role of Messaging 

Implicit in Zoho’s approach to blended conversations is the importance of messaging. While some forms of conversational artificial intelligence (AI) can operate in a voice environment, most bots operate in some form of a text-based interface.

Whether via a web-based chat or various forms of messaging (e.g., SMS, WhatsApp, Facebook Messenger, etc.), this type of blended approach works best when both the technology and the human interfaces are messaging-based.

That’s why Zoho has simultaneously released its new Instant Messaging Framework — and why we’re seeing virtually every major player in the contact center and customer experience space introduce some form of messaging into the mix.

Another critical factor visible in Zoho’s announcement is the importance of meeting the customer on whichever channel they prefer. Zoho’s IM Framework, for instance, supports WhatsApp, Telegram, Line, WeChat, Messenger, and Instagram. Other vendors have similar multi-channel approaches, with almost everyone in a race to enable their customers to connect on whatever channel they like.

Still, the abundance of messaging channels is as much a curse as it is a blessing.

Every channel has its own unique characteristics, which can create a burden when trying to leverage all of them to communicate with customers. That’s why organizations are increasingly leveraging platforms, such as Zoho’s IM Framework, to provide a consolidated management interface.

It’s Not About a Trade-off. You Can Do Both 

Technicalities aside, there are two critical things you must remember as you seek to strike the balance between efficiency and experience:

• It’s not a trade-off; and

• Your customer is your guiding light.

Since I launched The Digital Experience Report almost a year ago, I have spoken with countless enterprise executives about how they’re striking this balance. Invariably, those getting it right do not see it as a high-wire balancing act. Instead, they ask themselves how they can simultaneously achieve both efficiency and a better experience.

When they are doing both, they know they’re getting it right.

And that sense of simultaneously serving both your customer and the organization’s need to reduce costs also means that you’ll be striking your second note: keeping your customer as your guiding light.

Too often, the balance goes out of whack because the need for efficiency outweighs the need for a positive experience (Exhibit A: almost every self-service effort ever!). This second focus, however, will also help you ensure that you’re delivering the right level of efficiency from the customer’s perspective and not providing them with a high-touch service when they’re looking for quick and dirty.

The balancing act is never easy. And it’s made all that much more complex by the fact that both customer expectations and supporting technologies are in a constant state of change. But by adopting smart new technologies and applying them with a dual eye toward driving efficiency and a winning experience, you and your customers will come out as winners.

Disclosure: Zoho is a client of The Digital Experience Report. The Digital Experience Report maintained full editorial control of this analysis.