Stop looking for AI-shaped holes!

Why enterprises need to reimagine what’s possible

If you talk to enterprise leaders about artificial intelligence today, a familiar pattern emerges. Most conversations start with a very concrete question: Which problem should we solve with AI first?

That question feels sensible. Enterprises are built around prioritization, ROI, and risk management. But when it comes to generative AI, this framing can also be a trap. Starting with narrowly defined problems often leads to narrowly defined outcomes – and that’s not where the real value of this technology lies.

Generative AI is not just another optimization tool. It’s a new way of designing work, coordination, and decision-making. And enterprises that treat it as a solution in search of a predefined problem risk missing much larger opportunities for innovation.

The limits of “problem-first” AI thinking

In many organizations, AI initiatives begin by looking at an existing workflow and asking whether AI can make it faster or cheaper. Reduce average handling time. Deflect a percentage of tickets. Summarize documents more efficiently.

All of that is useful – but it assumes the underlying process is fundamentally sound.

History suggests that transformational technologies rarely deliver their full value that way. Email didn’t just speed up memos. Cloud computing didn’t just reduce data center costs. Smartphones didn’t simply digitize paper workflows. Each of these technologies changed how work was structured in the first place.

Generative AI belongs in that same category. Its real impact comes not from incremental improvement, but from rethinking how work flows across people, systems, and data.

So what can we do differently? What specific steps could we take to NOT look for the AI-shaped hole, but to truly use this technology at its full potential?

Here are a few practical shifts to consider.

1. Change the mechanics

For many enterprise stakeholders, “generative AI” still means one thing: a chatbot that looks and behaves like ChatGPT. You ask a question, it responds with text, and you iterate in real time.

That model is familiar – but it is only one possible interaction pattern, and often not the most effective one in an enterprise setting.

Much of enterprise work is asynchronous. Requests arrive through email, internal portals, or messaging tools. Context accumulates over time. Decisions are rarely made in a single back-and-forth interaction. I have written about this in the past – and stand by it.


The distinction between synchronous and asynchronous communication media opens the door to a different kind of innovation: changing the mechanics of interaction. AI agents that operate via email, messaging platforms, or background workflows often align far better with how work actually happens. In many cases, the AI doesn’t need a conversation at all – it needs access to context, ownership, and the ability to act.


Simply changing the communication medium can already unlock major gains in adoption, efficiency, and user satisfaction—without changing the underlying AI model.

2. Taking the process helicopter view

To go further, enterprises need to zoom out. Instead of examining AI opportunities task by task, it helps to take a “process helicopter” view of the organization.

From 50,000 feet up, two questions become especially revealing.

• Which processes are currently performed by humans, are repetitive in nature, and consume a significant amount of time.
• Which processes are performed by humans, deal primarily with unstructured information – emails, documents, chat messages, requests – and also consume a lot of time.

These questions cut across roles and departments. They surface work that exists not because it creates value, but because humans have historically been the only way to connect systems, interpret context, and move work forward.

With the current state of AI technology, many of these process steps are highly automatable. More importantly, they are often redesignable. Platforms like DevRev illustrate this by treating conversations, work items, and systems of record as part of a single operational fabric, rather than separate silos.

And tools like DevRev’s Agent Studio take the grunt out of designing these processes – and make it super easy to “land” the helicopter, and improve your processes.

What this looks like in practice

To make this more concrete, consider a few common enterprise scenarios.

• In customer support organizations, a large amount of work happens before an issue is ever resolved. Tickets are triaged, clarified, routed, enriched with context from product and CRM systems, and handed off between teams. Traditionally, this coordination work is invisible but time-consuming. AI can take ownership of much of this orchestration – reading incoming conversations, creating and updating work items, linking them to the right customers and products, and escalating only when human judgment is truly required.
• In product organizations, feedback from customers often arrives as unstructured input scattered across emails, support tickets, call transcripts, and chat logs. Humans manually summarize this information and attempt to translate it into roadmap decisions. AI systems can continuously ingest these signals, cluster them by theme, and connect them directly to product work – shortening the distance between customer conversations and engineering action.
• In internal operations, think about onboarding, access requests, or policy questions. These are rarely complex, but they are highly repetitive and distributed across multiple systems. Instead of creating yet another portal or chatbot, AI agents can operate across existing channels, interpret intent, gather context, and execute actions across systems – while keeping humans in the loop only when exceptions arise.

In all of these cases, the real innovation is not “AI answering questions.” It’s AI owning work, understanding context, and moving processes forward end to end.

3. Structured ways to innovate with AI

As a last point, I would also like to remind you that thinking differently about AI requires more than inspiration – it requires method. There are lots of different methods out there that can structurally help you innovate, and I am sure every other professional will have their preference. But it’s pretty clear that you can take a deliberate approach, and strategically structure your thought process and come up with different ways to innovate in your organisation, using AI.

Let me give you a few examples of approaches you could take:

• One useful approach is Jobs To Be Done. Instead of focusing on tasks or tools, teams ask what job a process is truly trying to accomplish. AI often makes intermediate steps unnecessary, allowing entire workflows to collapse.
• Another powerful technique is process inversion. Teams map a workflow and then ask what would remain if humans were removed from it entirely. This quickly reveals which steps exist because of historical constraints rather than real value creation.
• A third approach is zero-based process design. Teams design workflows from scratch under the assumption that AI can read, understand, and act on unstructured information by default. Humans are then reintroduced intentionally, rather than by habit.

Finally, some organizations flip the question entirely by starting with AI capabilities instead of problems. They map what AI can reliably do today – classification, reasoning, summarization, decision support, autonomous action – and explore where those capabilities could enable entirely new operating models.

A different question to ask

Perhaps the most limiting question in enterprise AI strategy is: Where can AI help us with our current problems? Don’t do that - it only leads to self-limiting options! A far more powerful one is: If AI were native to our organization, what would we never design this way again?

Enterprises that ask that question won’t just automate faster. They will operate differently. And over time, that difference – not incremental optimization – is where lasting competitive advantage will be built.

At DevRev, we ask for nothing better than to work together on answering that intellectually challenging question. Let’s engage, and instead of solving problems, work together on unlocking opportunities!

Cheers

Rik

The Enterprise Dilemma: Building vs. Buying AI-native CX Solutions

In today's every changing and evolving business landscape, enterprises face a critical decision when it comes to implementing AI-native CX solutions: should they build custom solutions from scratch, or buy existing platforms?

The Traditional Build Approach to AI in CX

Building custom CX solutions offers enterprises complete control over their implementation: the can fully customize their implementation, perfectly align with specific business processes, maintain proprietary intellectual property, and have direct control over feature development.

However, this approach comes with significant drawbacks: building custom AI solutions comes with significant challenges including high development and maintenance costs, extended time-to-market, and resource-intensive updates and improvements. Plus: whether you like it or not, there’s quite a bit of complexity to creating a solid AI solution - which is only to be met with significant skill!

The Traditional Buy Approach to AI in CX

Purchasing existing CX solutions provides several immediate benefits. Companies can deploy these solutions rapidly, leveraging proven functionality that has already been tested in the market and that has been engineered by highly specialized staff with very specific skills. These solutions come with regular updates and improvements managed by the vendor, and typically require a lower initial investment compared to building from scratch.

However, this approach also comes with notable limitations. Organizations often find themselves restricted by limited customization options and become dependent on the vendor's roadmap for new features and improvements. Additionally, there's a risk of misalignment between the pre-built solution and specific business needs, which can impact operational efficiency.

DevRev’s Hybrid Solution: A New Paradigm, validated by the industry

Modern platforms like DevRev are pioneering a hybrid approach that combines the best of both worlds: lots of out-of-the-box functionality that relieves you of the boring infrastructure related tasks, combined with extensive customization capabilities to tune the platform to your needs.

This innovative approach offers several distinct benefits: core functionality is available immediately while maintaining the flexibility to customize and extend the platform according to specific business requirements. We can summarize this like this:

This is not just DevRev saying this: McKinsey's 2024 "State of AI" survey shows that 75% of enterprises prefer solutions that offer both out-of-the-box functionality and extensive customization capabilities. This confirms the trend that we have seen: it aligns perfectly with the hybrid approach offered by modern platforms like DevRev.

Conclusion

The traditional build-vs-buy dichotomy is becoming obsolete. Modern enterprises need AI-Native solutions that combine immediate functionality with the flexibility to adapt to specific business needs. Platforms that offer this hybrid approach, like DevRev, represent the future of enterprise CX solutions.

By choosing a hybrid solution, enterprises can accelerate their digital transformation while maintaining the ability to differentiate their customer experience - truly offering the best of both worlds.

Let me know if you have any questions or comments. Would love to discuss.

All the best

Rik

Impedance Matching for DevRev

 

New, innovative products like DevRev are fascinating. They involve immeasurable quantities of hard work by lots and lots of people to get to market. But once you get there, how do you make it Super Easy(™) to communicate and make your audience understand the fruits of all that work? That. Is. Not. Easy.

This past week I have spoken to so many people, friends and contacts old and new, about this fascinating new adventure that I have embarked on. And I have felt like I really had to iterate multiple times to better tune the message of what it is that we provide to our customers. Communicate. Fail. Rinse and repeat. Until it works. Until it clicks.

In order to find that “click”, I was thinking of the idea of the Impedance Matching. Those of you that have an engineering background immediately understand: you need to match your messages to the audience that will be receiving it, or else … stuff will get lost :) … Too little detail and people will be frustrated - too much detail and they will be overwhelmed.

So that’s why I started to think about different “levels of communication” for different “levels of audiences” that  would understand different “levels of messages” for our different DevRev offerings. Here’s what I came up with.

Industry level - We want to make work matter. We want to connect builders to customers. We want to help build the world’s most customer centric organisations.

These may sound like different objectives - but they aren’t. Especially for people that have seen the complexities of building digital products in today’s day and age, it will probably ring true. How many software engineers never see the fruits of their work in the hands of a customer? How many of them have actually never seen or heard the voice of their customer, literally? That’s not a very satisfying place to be. What if we could shrink that distance between builders and customers? What if we could give builders and buyers, dev’s and rev’s, a true voice in the conversation?

Company level - We want to solve the problem of Information Asymmetry in digital product building organisations: different teams have different access to different information. This problem is the root cause for many Customer Experience problems: siloed teams lead to a frustrating client experience that effectively limits growth.

Great companies excel at customer focus. They are obsessed with their customers’ success, with the value that they derive from the product - and will walk through fire to help the customer get there. There is no substitute for that - but there are lots of barriers to get there. Information siloes are real, in fact they have gotten worse since the moment SaaS 1.0 made it dead easy for every department to automate their departmental processes with yet-another-cloud-platform. Where did the holistic view of the customer go? That’s right - it disappeared. And with it, so did the truly exceptional customer delight. 

CxO level - We want to offer new growth opportunities, by enhancing the customer experience at a lower cost. This means breaking down silos between tools and teams, bringing the data together, and using the latest Agentic AI technology to automate the automatable.

At DevRev, we make this a reality, today, by integrating the different tools in your different departments in a comprehensive Knowledge Graph that connects all the dots. Using that data, we can offer holistic search that reduces the information asymmetry, automated workflows and analytical capabilities on top of that. Using AI, we automate the time-consuming tasks, and make the cross-cutting information accessible through conversational interfaces. 

Customer Support - we want you to be able to help more customers quickly and efficiently, using the full information that is needed to do so, and leveraging AI assistance whenever possible. 

Leveraging DevRev, customers have seen significant drops in resolution times, much higher call deflection rates, faster customer service and as a consequence, a higher net promoter score. As a result, the company can turn support from a cost into a revenue generator.

Product Management - we want to break down the barriers between devs and revs, and make sure that you have all the information to better tune your development and support resources to your most valuable product parts. 

Understanding what is wanted and needed by your customers is not trivial, especially when you have layers of Chinese whispers standing between the engineers and their customers. With DevRev’s knowledge graph, a holistic customer view becomes accessible and actionable. With AI, we can aggregate requirements and align your resources. We can tune in to the customer voice, and foster long term success.

Head of data - as digital product organisations become successful, as their departments grow, they become more complex. To deal with that complexity, many organisations have implemented departmental tools to optimize departmental processes - and by doing so we have lost the overall picture. SaaS 1.0 has created data silos - we now face a real data integration challenge.

Using patented “Airdrop” technology, DevRev has successfully implemented a bidirectional syncing system for most sources of enterprise data in the cloud. CRM data from Hubspot or SalesForce, Customer Support data from Zendesk, Freshdesk or ServiceNow, Product data from Jira / Github, it all comes together in a fully synced up Knowledge Graph. This repository is searchable and actionable, and can drive new business processes in real time using AI and AI Agents. This will allow us to lever the holistic view on the  data as additional context for better human and AI decision making.

Head of AI - leveraging the potential of AI is on everyone’s radar. Not doing AI is not an option - you do NOT want to fall behind. But how does one operationalise this amazing technology, without spending an arm and a leg and months/years of development time? How do you limit the risk, and ensure compliance? How do you prevent hallucinations and reputational damage? 

Turns out you don’t have to do it all yourself. DevRev has spent hundreds of person-years in design and engineering time to build a product offering that does it for you, fast, and at a much lower cost. Leverage the benefits, but don’t run the risks. We help you implement AI efficiently and effectively, and together we will unlock its potential for your organization.

I am hoping that these messages are a bit clearer. We have an incredible story to tell, but it’s like so many beautiful stories: there is more than one storyline. By tuning the story to the listener, by matching the impedance, I have been trying to make it easier to understand - whatever your background.

Looking forward to many more discussions in the next couple of days, weeks, months to come. It’s going to be an incredible journey.

Rik