AI Digital Transformation

Preparing for Four Types of Enterprise AI in 2024

AI enables us to solve problems at scale that were previously unsolvable. That’s why many see 2024 as the “Year of AI”.

Companies are moving quickly to allocate budgets toward AI investments. 40% of organizations surveyed by CapGemini said they’re already funding AI endeavors – and another 49% plan to do so within the next 12 months, and 74% believe the benefits will outweigh associated concerns. 

In this blog, we’ll seek to understand four categories of AI application and what’s required to be successful with each type.  The businesses that create the right building blocks for GenAI early stand to reap benefits that we can’t even yet comprehend. 

Four Categories of Enterprise AI Applications

Any observations in this exponentially evolving space become dated very quickly. With that in mind, our team has observed four discrete categories of AI solutions entering the market. We’ll discuss these below and recommend how an organization can best incorporate them.

  • Category 1: First-generation GenAI products
  • Category 2: Product-enhancing GenAI 
  • Category 3: AI-enhanced business workflows
  • Category 4: Enterprise-connected AI 

Categories 3 and 4 offer the most potential for organizations seeking to gain a competitive advantage through AI. We’ll discuss the details of all four types below.

Category 1: First-generation GenAI products

These products would not exist without AI. Their main purpose is to provide broad, direct access to GenAI Large Language Models (LLMs) for end users. Examples of this category include ChatGPT, Midjourney, and Bard.

These solutions come from large tech companies that have invested heavily in creating and training their own LLMs to power commercial product offerings. The barrier to entry for using these types of solutions is low. (The barrier to creating them yourself from scratch, however, is quite high!) Organizations that wish to leverage them only need to train employees on their use for boosting productivity by eliminating tedious tasks.

Competitive Differentiation: Low
Complexity of Rollout: Low

Category 2: Product-enhancing GenAI

Companies are rushing to embed GenAI capabilities into existing products to capture more license revenue by adding value to existing product capabilities. An example is Salesforce’s GPT extensions to its sales, service, Slack, and other portfolio products. 

These solutions make existing software offerings better and are very logical places to start since they own the screen pixels that drive user workflows. The tradeoff is that value is siloed to those products and can’t impact the enterprise at scale. 

Organizations wishing to leverage these can upgrade existing licenses (if necessary) to key software offerings from Salesforce, Adobe, or Microsoft. They then need to provide employees with training and enablement on the newly surfaced GenAI features.

Competitive Differentiation: Low
Complexity of Rollout: Low/Medium

Category 3: AI-enhanced business workflows

Savvy companies are beginning to leverage APIs from LLM providers like OpenAI, Microsoft, or Google to improve upon existing business processes in ways that were impossible without GenAI. Here are a few examples:

  • An HR department that uses GenAI to score resumes against a job posting and provide gap assessments to recruiters
  • A home insurer that automates common underwriting tasks by searching documents using context and reason
  • A quoting engine capable of detecting the intent of the user and aligning it to the right potential product offerings, increasing conversion, sales velocity, and CSAT

When applied to the right use cases, this category of AI solution allows a business to eliminate process bottlenecks, reduce overhead costs, increase employee effectiveness, and ultimately provide a significant impact on critical revenue and profitability drivers. 

Organizations serious about these solutions should take inventory of problematic, high-impact bottlenecks impacting the bottom line. These include pipeline management, inventory and supply chain, customer onboarding, call center workflows, and talent acquisition processes. GenAI will enable many solutions to these problems that weren’t previously possible.

Competitive Differentiation: Medium/High
Complexity of Rollout: Medium

Category 4: Enterprise-connected AI 

These future-generation solutions will allow AI to perform strategic actions on behalf of an employee or customer persona. This will enable interaction with the brand in ways impossible before GenAI. 

A great example is a chatbot that can book a travel plan to a user’s granular travel requirements. GenAI will perform tasks that would’ve taken a travel agent half a day in a few seconds.

This type of AI application promises to revolutionize how we interact with brands. It stands to cause a monumental shift in how we think of human-computer interaction. We’ll take a deeper look into this specific category down below.

Companies looking to harness this type of AI solution should start by taking a fresh look at poor user experiences driven by task complexity. GenAI promises us the ability to detect the intent of the user and act on their behalf. In other words, there are now better ways to drive complex workflows in a more user-friendly way.

Competitive Differentiation: High
Complexity of Rollout: Medium/High

Differentiating with AI

Categories 3 and 4 of AI solutions unlock the most enterprise value for organizations. Innovative solutions have the potential to provide enormous competitive advantage when executed correctly. These solutions will require companies to address an age-old problem: systems integration.

Modern cloud software like Salesforce, Dynamics365, and NetSuite powers business user workflows. And as long as an implementer stays within the platform’s best practices, it’s rare to find any major issues in the SaaS tier.

The technology obstacles that cause issues – the ones that take months or even years to address – are almost always caused by a lack of interoperability with the enterprise’s hundreds or thousands of other systems and solutions. 

Unfortunately, too many organizations today brute-force these solutions to get them to “just work.” The result is that these systems integrations are inherently brittle and sloppy. This leads to an immense level of fragility. It’s a major reason why 80% of IT leaders surveyed by MuleSoft said that integration hinders digital transformation.

As MuleSoft Founder Ross Mason said, without connectivity, AI is merely a “brain in a jar.”

Next Steps

When you consider the load that AI integration will put on interconnectivity, you can see how the integration issue is the biggest hindrance to adopting GenAI at scale in the enterprise. Tune in next week for the second part of this blog series on how to prepare your systems integration strategy with AI in mind.

Ready to learn more about riding the Generative AI wave? Download our comprehensive white paper on AI Readiness and start your AI Superhero training today.

AI Digital Transformation

A decade ago, my team delivered the #1 mobile e-comm site in the big box electronics and appliances category. How we did it can teach you about success with AI.

AI is changing technology as we know it, and it’s only going to speed up from here. Winners and losers will be created by this shift. Leaders everywhere are scrambling to figure out how to prepare their organizations to consume this change. 

A similar disruptive situation unfolded with smartphones in the 2012 – 2014 timeframe. Entering those years, there were very few mobile-specific web experiences. Just about everything required “pinch/zoom” navigation, and as mobile traffic began to increase materially in this timeframe, brands knew they had to adapt quickly. How to adapt and common obstacles for delivering mobile experiences were still big mysteries. 

With OpenAI CEO Sam Altman frequently implying we’re only at the beginning of an exponential period of innovation and with everyone agreeing that AI will be a game changer, this situation repeats itself. Brands know they need to adapt quickly to AI–even more so than mobile–but how to prepare for AI is largely a mystery to most organizations. 

Read on to discover lessons learned from my history working with companies to adopt mobile and how to apply these lessons as you prepare for AI.

Look to the past as a crystal ball to the future

Think back to the days before smartphone usage hit critical mass.

Calling an Uber. Scrolling Instagram. Looking up directions on Google Maps. Tracking your steps with a smartwatch. Watching high-definition video. None of these now-everyday activities were obvious when the iPhone 2G was released by Apple and Steve Jobs in 2007. 

Thinking back, I recall much less fanfare than there was skepticism. We had to watch it unfold in the wild, gradually resetting what the idea of normal was and layering idea on top of idea in this new normal. 

We believe the same is currently true for AI. We don’t know exactly where it’ll go. But we know its impact will be widespread, like nothing we’ve experienced since the smartphone disrupted every major business. 

40% of organizations surveyed by CapGemini said they’re already funding AI endeavors – and another 49% plan to do so within the next 12 months, and 74% believe the benefits will outweigh associated concerns. The businesses that create the right building blocks for GenAI early stand to realize value from AI first. 

So 2024 corporate AI objectives will be well-funded, but most organizations I talk to are searching for a roadmap to success. Understanding of major hurdles will be critical to ensuring these budgets are put to use wisely to enable high-value AI use cases. 

An old, boring, and (largely) unsolved IT problem

In the summer of 2013, I had the privilege of leading a team of engineers at a major big-box electronics and appliances retailer. Like every retailer in that time period, evolving buyer web traffic patterns shifting to mobile meant that our customer needed a mobile site to be competitive, and it needed it now. Our goal was to deliver this experience in a very narrow timeframe for an October go-live so that we had ample time to tune it before Black Friday, which was like retail’s Super Bowl.

Our technical challenge was that desktop websites at that time rendered entire megabyte-heavy Web pages in one shot and that Browser technology was still evolving from its infancy. Mobile required a lighter-weight solution that enabled devices to fetch new data without a full page refresh to avoid overtaxing the limited resources of the iPhone 3G and 4. As a result, the industry shifted to multi-tiered apps with heavyweight back-ends exposed by APIs, and front-ends driven by newer JavaScript frameworks.

We were trailblazers at the beginning of this shift with our client. The mobile site we delivered received a lot of accolades and won the prestigious JDPower award for its category, beating out much bigger budgeted brands like Home Depot, Lowes, Costco, and Best Buy. It was praised for its user experience, page speed, and for taking advantage of newer device and browser capabilities. Most importantly, it produced great revenue funnel metrics for our customer and was easily scalable to handle the huge traffic spikes of retail. 

In a world of poorly-operating mobile sites, what was our secret? The same secret that will enable AI: a flexible, big-picture systems integration strategy.

None of what we achieved would have been possible without the flexibility created when our engineering team prioritized solving the integration problem as a necessity for solving everything else. Solving integration enabled our rapid delivery schedule, unlocked features to lift the buyer journey, and enabled our site to perform at scale. We knew it was a must-have for our client to be successful.

Fast forward a little over a decade, and we sit at the precipice of another huge technological shift with AI. And with AI, the integration demands will be even higher. For it to have a widespread impact, it needs to understand which systems have which data and which systems are used to trigger which business actions. 

In 2023, Shyam Sankar, CTO of Palantir, said “The popular view is that [systems integration] is a boring and solved problem, but it might be a boring and highly unsolved problem, where people are just duct taping everything together.”

Sankar goes on to link this problem to AI enablement, implying that only companies who solve this problem will reap the biggest benefits from AI. This belief is shared by MuleSoft Founder Ross Mason, who wrote about it in a 2018 blog. Green Irony’s own experience building custom AI solutions to address high-value use cases also backs up the need for scalable systems integration. 

Unfortunately, too many organizations today brute-force integration strategies to get them to “just work.” The result is that these systems integrations are inherently brittle and sloppy, serving only the narrow needs of each integration consumer instead of being flexible enough to quickly meet the needs of ANY consumer. 

Engineering teams must prioritize unlocking key systems in a scalable way if their organizations are to be successful with AI.

Getting your house in order

Acknowledging a problem is the first step toward solving it. 

GenAI solutions, much like mobile, will presuppose that this integration problem has been solved. Savvy enterprises will learn from mobile and ensure that this presupposition is correct, paving the road to AI success for their organizations. 

Don’t be caught off guard. Educate yourself by taking a deep dive into this topic with our AI Enablement White Paper. Learn more about AI enablement and why a flexible, composable integration strategy is critical to your success.

Interested in a discussion on this topic? Reach out.

AI Digital Transformation

It’s Groundhog Day: Lack of an Integration Strategy Will Inhibit AI, Too


The undisputed hottest topic in technology now and for the near future is LLMs and Generative AI. There’s a lot of talk about how to design prompts and get ChatGPT4 to do cool things for you, but not a lot about how to get value across an entire enterprise. 

That’s what we’ve been up to with our R&D team at Green Irony.

Killer Use Cases Beyond the Imagination

Today’s Generative AI capabilities unlock high-ROI use cases that were unthinkable a year ago. Green Irony’s R&D team has many in the works that we can’t wait to show you. For now, imagine the following scenario in the Travel & Hospitality industry:

A business traveler named Chris is currently in Dallas for some client meetings. It’s summertime and not only is there significant demand for air travel but there is also severe weather including tornadoes, hail, and severe thunderstorms wreaking havoc on the major airline hubs of the midwest. This passenger has a family emergency and needs to get back to Tampa, FL as soon as possible, no matter the cost.

Prior to November 2022’s release of ChatGPT, this would have required a sophisticated travel agent with expert knowledge of flight and rental car booking systems and lots of time with Google Maps and other utilities. Possibly even a spreadsheet, and who wants to use one of those?

What if instead Chris could just say this to an AI service?

“Give me your top 3 options for getting me from my current location to Tampa, FL, by 5 PM tonight. Car travel should be limited to 120 miles or less. Optimize first for arrival time, then for premium seat availability, then for driving distance. Do not risk any connecting flights in the midwest.”

So what’s stopping us from doing this RIGHT now?

Hint: It’s the same thing that always stops us, and MuleSoft Founder Ross Mason was prescient enough to warn us about it for AI in 2018.

Last decade’s disruptor: Mobile and Tablet Devices

2013 saw a huge shift of web traffic from desktop to mobile and this was incredibly impactful to businesses, especially those like retail that rely on web funnel conversion metrics as key business drivers. Mobile was arguably the first major disruptor that truly exposed the perils of siloed enterprise technology systems and lack of a clear, scalable integration strategy. 

I remember seeing it first hand. As my team and I worked with retailer HHGregg to deliver the #1 mobile experience in their space, what do you think our biggest inhibitor was to providing this exceptional customer experience?

If you said “integration”, you’re catching on to the theme of this blog. Attempting to get the right data to drive the right mobile user interactions and respond to these user actions within systems of record was by far our biggest inhibitor. Despite the fact that web browsers and JavaScript frameworks to drive the right mobile behaviors were still considered “cutting-edge” back then (Angular 1.2 was released November 15, 2013!), they were still the easy part. 

Integration was our clear-cut biggest hurdle to creating an award-winning e-commerce mobile experience for our partners at HHGregg.

Today’s Landscape for Scalable Integration

History again repeats itself, but this time with much higher stakes. Insert your preferred doom and gloom messaging about AI and existential risks to the viability of businesses and a high-stakes, zero-sum game arms race here.

The needle simply has not moved fast enough for most organizations over the past decade in terms of scalable integration. Most of them now are stuck with Ross Mason’s “big ball of mud” and every project, big or small, becomes bogged down by this situation. 

Just about every conversation I have with customers about a failed Salesforce implementation comes down to one thing: lack of access to data and business capabilities in other systems. Most of the time, in-house IT teams have built some Frankenstein’s Monster set of data synchronization capabilities that fail to scale with the purchase of new systems and software, evolution of business processes, and marketplace disruptions due to new cutting-edge technologies. In an ironic twist of fate, their Salesforce implementation team tends to spend their time attempting to solve systems integration problems that are not solvable within the Salesforce platform itself.  

These Salesforce implementations are inhibited by the same challenges we saw with mobile e-commerce websites in 2013.

AI will be no different, because today’s LLM’s like OpenAI’s GPT4 heavily reward enterprises with composable integration strategies.

Anybody Heard of This ChatGPT Thing?

Enter the most disruptive technology any of us will ever see in our lifetimes, a technology disruptive enough to disrupt even Google’s core business

Clearly, it’s important for businesses to get this right, and the sooner they get it right, the greater their competitive advantage will be. But without a scalable, composable API strategy that unlocks the capabilities of the business for consumption, broader AI efforts won’t get off the ground. 

In the aforementioned article from Mason, he wrote the following:

“Organizations first need to become more composable, building a connected nervous system called an application network, which enables AI to plug in and out of any data source or capability that can provide or consume the intelligence it creates. The point-to-point integrations of the past won’t be practicable in the AI-world, where things can change in an instant. Instead, organizations need a much more fluid approach that allows them to decouple very complex systems and turn their technology components into flexible pieces.”

Our R&D efforts with OpenAI, which we plan to showcase extensively in the coming weeks, have reinforced Mason’s point of view on this topic. GPT-4, the engine behind ChatGPT, can generate fantastic answers to questions that rely on generally available, non-specialist knowledge. In order to equip it with Enterprise-wide capabilities, we need composable APIs that allow us readily-available access to business capabilities so that GPT can solve real, enterprise-level problems. 

It’s Groundhog Day and we’ve seen this all before. Every innovative breakthrough levies additional burdens on an IT organization’s integration strategy. With the game-changing nature of AI, composable integration is needed now more than ever before.

We’ve Got So Much More to Say On This Topic

We hope you enjoyed the first post in this series. At Green Irony, scalable integration is in our DNA and we are incredibly passionate about this topic and the benefits it provides to our customers. Future posts will cover:

  • A real-life, in-production application that Green Irony has used to exponentially scale its candidate interview process and save hundreds of hours across key members of staff while increasing its applicant quality.
  • A prototype for the Travel & Hospitality industry that unlocks a vastly superior way to purchase products and services in this industry.
  • Several deep dive spotlights into the engineering behind both solutions, showcasing how the Green Irony OpenAI Connector can be used broadly for any OpenAI use case.
  • A comprehensive white paper on why composable integration is an absolute must for companies who are serious about leveraging AI that builds upon the concepts in Ross Mason’s 2018 blog.

Supercharge your AI readiness by combining our MuleSoft OpenAI Connector with an AI-ready integration strategy.

Digital Transformation Platform Migration & Modernization

Slack Is Not a Chat Tool

Slack Is Not a Chat Tool

“But I use it to chat with my entire organization every day!”

You sure do. In fact, if your company uses Slack, you likely use it as a chat tool hundreds or even thousands of times per week. 

You also likely have an iPhone or Android device within two feet of you right now that you use hundreds or even thousands of times per week. How much do these devices have in common with a rotary telephone? 

Breaking Paradigms

One of the iPhone’s core, foundational use cases is basic calling. If the iPhone didn’t do that core use case well, it never would have replaced the Motorola Razr that lived in my pocket in 2009. Its initial value, to me as a consumer, was that not only could it replace my Razr, but it could also replace my iPod and provide a superior SMS experience. 

That was 2012. Do you think my personal paradigm around my iPhone has shifted since then?

Of course it has. Like everyone else, I’ve come to rely on the iPhone to manage my day-to-day life. But what does this have to do with Slack?

One of Slack’s core, foundational use cases is basic chat. If Slack didn’t do that core use case well, it never would have replaced GChat within our organization in 2018. Its initial value, to me as a business owner, was that not only could it replace GChat, it could also replace “internal Green Irony” SMS messaging, add logical channel groupings for focused collaboration, and, of course, add Giphy animated gifs to the mix. Mix in some basic integrations like Google Calendar and it was a no-brainer.

That was 2018. Do you think my personal paradigm around Slack has shifted since then? 

More importantly, do you think Salesforce CEO Marc Benioff shelled out $28 bil for a chat tool with animated gifs? 

The Adoption Problem

Just like starting with calling and text got the iPhone into pockets and at-scale usage to unlock more valuable use cases, Slack started with a chat tool, obtained at-scale usage, and is now at the beginning of the innovation curve where it will unlock much more valuable use cases. Slack will become a staple in putting needle-moving enhancements in the hands of users as quickly as possible to impact key business drivers. It will give users the tools they need to do their jobs much more effectively by providing in-context actions paired with the targeted, rapid collaboration that already takes place within its “chat tool.”

Doesn’t Salesforce already do all this stuff? 

It sure does. In 2017, we wrote that Salesforce is not a CRM. We’ve always seen it as a world-class platform for delivering key business workflows. With five additional years of Salesforce-specific digital transformation experience under our belts since then, what we’ve found is that our biggest challenge in delivering on these promises isn’t the technology platform, it’s adoption. And we’re not alone in this observation.

Within the last month, Gartner has applied a heavy focus on the CRM adoption and data problem with a keynote titled “CROs: It’s Time to Solve the CRM Data Problem.” My key takeaway from this presentation re-enforced what I’ve seen in the field: there’s a “spiral-like” impact between CRM data quality, user experience, and adoption. Poor UX drives low adoption, which drives poor data quality. Poor data quality further magnifies UX challenges, driving even lower adoption. The problem feeds upon itself. And with the CRM more and more connected to the rest of the IT system landscape, the problem is only getting worse. 

The dollars and cents to the business here are immense. This adoption problem exists outside of the CRMs as well, but we’ll narrow the focus to CRMs for this blog. Let’s examine a few examples of the impact of this problem: 

  • Lost rep productivity from sub-optimal tooling and manual workflows, resulting in lower deal size per rep, win rate per rep, and overall revenue per rep. 
  • Poor revenue attainment due to loss of basic or advanced CRM capabilities, depending on severity of issues. 
  • Poor employee experience across key CX-impacting sales and service reps with long-term impact on overall brand equity

No matter the industry, all of these are clearly high-impact areas to a business.  

Salesforce and other CRMs have become more like Systems of Record, catering to “thick work”  like sales forecasting, opportunity management, and complex product support. “Thin work” like collaboration, appointment scheduling, basic task automation, and data entry can pivot to a more natural, in-context usage pattern, solving the adoption issue. 

The Future of Work

We see “thin work” being forced onto users in “thick work” systems is the #1 driver of this adoption issue. We see Slack as THE key technology platform for solving it.

Because its first use case was chat, Slack is already ingrained within users’ day-to-day workflows. It serves as the communication lifeblood of an entire organization. Adoption of productivity-enhancing workflows for users will be a much lesser challenge. These workflows will all produce clean business data, powering more advanced workflows, and, in the future, AI.

Does anyone’s sales organization have a Slack adoption problem? If you’ve heard of one, please reach out and let me know. You’d be the first.

If the key inhibitor to the value proposition of the CRM is adoption, moving pieces of our workflows to a platform with sky-high adoption rates and allowing users to perform actions in-context seems like an area with massive potential. 

A few key questions to ask yourself as a business leader:

  • How could our user productivity and happiness be impacted when we can automate common pain points within our critical revenue-driving and customer-impacting processes? Imagine the at-scale impact of being able to automate the build of a first-call deck so that a rep only has to spend 5 minutes validating and fine-tuning the output.
  • How will our sales KPIs like deal velocity and close rate benefit from our additional rep productivity? Imagine the business impact if reps saved an hour per-day on poor CRM UX and applied that time savings to relationship building, prep, and additional prospecting.
  • How will key EX metrics across reps be impacted by eliminating annoying tooling issues? Everyone’s CRM has a workflow that is hated by every rep because of how much time it wastes. Imagine the morale boost of eliminating it with in-context time-savers in Slack.
  • How will the increased data quality as a result of adoption allow me to better analyze and fine-tune my key business workflows? All of the promises of a fine-tuned CRM that are rarely realized due to adoption and bad data can be realized when these challenges are overcome.

Finally, a question from the blog author: why are very few companies taking advantage of this huge opportunity?

Perhaps we’re just early in the innovation curve and it’s only a matter of time. Or perhaps another reason is a challenge we are all too familiar with: Integration.

Integration Drives Innovation

Through the lens of a CIO charged with innovation, if Slack allows me to design user workflows that impact key business drivers and adoption is no longer a huge challenge, what’s my next challenge?

It has to be integration.

On the technical side, the biggest implication to this type of shift is an even greater strategic need for a scalable integration strategy focused on business agility. We’ll have a need to touch a large number of systems within our technology landscape in order to design the best possible workflows to impact these key business drivers. 

Slack is simply the user control panel, allowing users to perform work in-context more easily. It places an even greater emphasis on what’s happening “behind the scenes” to make the in-context work actionable. We’ll get into the nuts and bolts of what’s required to build custom integrations in Slack in a future blog from Green Irony’s engineering team, but suffice it to say, APIs become much, much more important in this sort of environment. If our goal is to avoid the negative impact of our users wasting time in systems to perform specific in-context actions, we must have a well-designed API network. Without one, the one-off integration requirements levied to the engineering team will kill our delivery timeline and derail our key business priorities. 

This means we need a scalable integration strategy focused on delivering reusable assets that can be used to create a composable business and automating key business processes across the IT landscape. 

Doesn’t this sound like the value prop of something else you’ve seen Marc Benioff acquire in the past five years? 

Betterer Together

We’ve seen the “Better Together” MuleSoft and Salesforce campaign for a few years now. We fully believe in it ourselves, having created a fictional story to highlight the concept at a high-level technically

It’s our view that Slack and MuleSoft are even “Better Together” than the previous pairing. The reason for this is because Slack’s #1 use case in every organization is always going to be chat. But that doesn’t mean it’s a chat tool. It means that savvy organizations with control over their API strategy can leverage this “chat tool” to ensure they conquer the adoption problem that impacts all businesses. Conquering this problem is going to be a bigger and bigger competitive advantage in today’s digital world.  

We’ve written ad nauseum about MuleSoft’s ability to shine in key areas and won’t repeat them here. Some reading for interested parties can be found below:

In a nutshell, MuleSoft allows technology departments to create reusable API assets that model the company’s core business. These assets can be composed into critical business processes and orchestrations of the behind-the-scenes technology interactions required by these processes, dubbed an “Application Network.” These processes can then be surfaced in the exact way necessary for a consumer to interact with them.

This is exactly what’s needed technologically to capitalize on the immense business value unlocked by the Slack platform. This pairing was meant to be.

Are You Prepared for the Future of Work?

All visionary executives must ask themselves one critical question: am I prepared for what’s to come with the future of work? 

If the answer is no, you are essentially relying on your competition to be equally unprepared. Because if they are, you’re toast, aren’t you? Your competition will be ahead of you in several key areas:

  • They will have the business agility to competitively differentiate themselves rapidly under new market conditions such as pandemics, geopolitical situations, or inflationary impacts.
  • They will have a superior set of data upon which to make actionable business decisions and drive future AI scenarios.
  • Their workforce will be much more productive than yours, dollar for dollar.
  • They will be able to continually improve high-impact metrics like revenue, CX, EX, and cost of sale by making the employee workflows and communications that drive these metrics more effective. 
  • They will be able to quickly open new revenue channels and attack new business models without the friction of user adoption.
  • They will see the business results that get organizations to buy into these types of technology platforms and 8-figure “digital transformation” or “modernization” initiatives.

Does this sound like a competitor you want to deal with from your current position, or do you want to win by planning, executing, and evolving?  Talk to us today about gaining your competitive edge through a scalable integration strategy tied to your business outcomes.