Q & A

This is an excellent question, and a critical part of the methodology we provide to our members. The BIG WINs in CX come from long-term strategies of improved engagement and experiences for customers – these impact how customers behave in terms of buying additional products, remaining loyal customers, using lower-cost channels and referring others to the business. All these attributes feed into increasing Customer Lifetime Value (CLV) over the long term and can drive multiplicative growth for the business. Results like doubling growth or revenue will never come overnight but come from transformational business approaches that take years to deliver.

However, these huge long-term gains must be supported by short-term results for almost all businesses. The demands of quarterly and annual targets are very real; few businesses have the luxury of being able to wait multiple years for good results. For this reason, our recommended approach to implementing Journey-led Transformation is to work on an incremental approach, where specific, measurable business wins are delivered every quarter as steps along the way to the big transformation. The good news about our journey approach is that by selecting the right step-by-step journeys the organization can get familiar with the concepts, approach and technology, prove incremental value to the business, and thus win additional funding to expend at the next stage.

SearchUnify Lens:

Mark highlights the core challenge that SearchUnify’s platform solves: unified indexing with our proprietary SearchUnifyFRAG^TM. Effective journey orchestration and true Agentic AI cannot succeed if customer data, knowledge, and historical interactions remain siloed. Our approach leverages over 100+ connectors to aggregate data from all disparate sources – CRM, knowledge bases, support systems, and marketing automation – into a single, coherent index. This connected intelligence provides the real-time, single data view necessary to power personalized, intelligent decision-making across every customer touchpoint.

I see Agentic AI as a very new field in which automated systems takes over running the interactions/interface between a customer and a business. Both sides can deploy such AI Agents to work for them and thus get huge levels of automation going. For example, (and this example may be a bit futuristic, but it works well to get across the concept) think about the consumer task of booking a flight to go on a business trip – you have various time constraints around meetings, you have favorite airlines, but you also have travel preferences such as non-stop flights, convenient departure times and good seat availability. An AI Agent would be able to take on the task of getting you the best flight option booked – it knows your constraints and preferences, and it searches all the options across lots of websites to find the best fit and then book it for you.

AI Agents can also be deployed by businesses in a similar way, understanding everything about a customer and helping them find the right deal or offer to suit their exact situation. Unfortunately, I see a lot of confusion in the market these days where companies are using very simple agents like chat bots, which often use limited data, work from fixed scripts/options, and provide simple automation that often frustrates the customer. The obvious gap here is that most “agents” in the market today have very little “AI” in them, and the term Agentic AI is becoming confused and devalued.

This brings us to the other part of the question – distinguishing from more traditional AI. It is usually this more traditional AI that is missing in the Agent world. Using all the available data to find important patterns and thus “know” or predict what is the best resolution or offer for a customer. Advanced analytic approaches have done this type of work for many years, sometimes called AI, sometimes not. But that intelligence is critical to get real value from Agentic AI – if we want our interactions taken over by these autonomous machines – they better know what we want and be smart about how they get it!

Data is absolutely central to intelligence, AI and any type of automation or real-time decisioning system. The good news is that most organizations are now rolling in data. As systems have advanced and become more digital there is naturally lots of detailed data available inside every company. This data is also remarkably clean – it must be to run the systems accurately – and so businesses should not get waylaid on data cleansing exercises – rather dive into the masses of operational data in the business and use it.

The area of data access and use is perhaps one of the most critical areas where AI approaches are being under-utilized. Many AI tools are excellent as accessing and processing data – understanding its format and transforming it to be used for analytic tasks. So, my biggest piece of advice in the data area is for businesses to use their AI budgets to do a better job of creating a single connected data view of the customers – this will become a key platform to then run advanced analytics, including AI and Agentic AI processes.

SearchUnify Lens:

We agree with Mark Smith that a single connected data view is the non-negotiable foundation for effective Journey Management and Agentic AI. This is SearchUnify’s core proposition. We solve the access challenge (the knowledge gap he identified) with a unified cognitive platform. By aggregating content from all siloed sources – CRM, knowledge bases, forums, and support systems – into a single, central index, we eliminate data friction. This is the Unified Indexing strategy that powers real-time, context-aware decision-making across the entire journey.

Starting with the Journey theme I think the biggest confusion in the market is around what is a “Customer Journey”, this confusion is driven by the providers of marketing solutions. It all dates back over 10 years to when Salesforce acquired the email system from ExactTarget and named the email campaign tool “Journey Builder” – even though it was just an outbound email/messaging system. OK, it can send a series of emails through time, but this doesn’t make it a customer journey! (A customer journey focuses on the customer, and supports their activity/goals, connecting every interaction between the customer and the business – in both directions, whether you call them or they call you!). Soon almost all marketing tools changed their systems from running campaigns to running “journeys”.

The reason this confusion is a big problem is because good customer journeys need to engage with marketing – they don’t just sit in the CX team – by their nature they need to touch every part of the business to be able to track all the interactions with the customer. So, a good customer journey needs to link with marketing, understand what marketing is communicating, and incorporate this in the wider journey. But having that conversation with the Marketing team is tough – they think they are already doing journeys – and indeed are “owning” them, because they bought a whole software system to do it!

The other confusion to highlight would be with AI, and we touched on this earlier. Today AI is such a hot space and a hyped buzzword that everybody wants to be connected to it. So, the term AI gets used by so many different tools and can cover a wide range of levels of analytic power. Many people now say “AI” when they mean basic math or statistical routines (e.g. predictive modelling) that has been used for years and is often now called “Predictive AI” – the real big difference today is that there is so much more, and better, data available for these tried and trusted techniques. More futuristic types of AI, such as Generative AI (think in simple terms, dealing with language not numbers) and Agentic AI (which we discussed earlier) are often getting tied up in this confusion where sometimes it seems that everything is AI…

Yes, for sure, although we again need to think about these various types of AI and how each one can be seen to help generate great results for CX teams.

Most of the examples in the market today are produced by using Predictive AI to drive intelligence into the customer journey, especially when this predictive intelligence is used in real-time. By engaging with a customer on their terms (as regards channel and timing) and offering them the right service or product, based on a predictive AI model, has been seen repeatedly to increase engagement rates by up to 50% and sales success rates by at least 10%. These types of results have been achieved in a wide range of industries – from the Automotive industry by Ford in Europe, to the Theme Park industry in the USA at Dollywood, to the retail banking industry with JP Morgan Chase on a global basis.

Some of the best-case studies also show a win-win for the business. By using AI-powered journeys improved business results can come from both increased revenue AND decreased costs – because there are no wasted interactions like with marketing campaigns, and sales get made using existing interactions in other channels. Some of the most powerful examples in this area are from retailers such as Kroger who have achieved significant sales increases while reducing communication costs by over 50%.

Case study examples for new AI approaches are less common. I have seen some excellent early results from using Generative AI to personalize messaging to a customer to fit their exact situation. The CSG product Bill Explainer does that to provide simple textual explanations to customers of changes in their bill – it has led to significant decreases in customer support costs (up to 10% of overall costs) and upside benefits such as quicker payments and lower delinquency.

Perhaps the most striking statistic in the CX marketplace is just how unhappy the typical customer is with the service they get from the brands they do business with.

Consistently around 1-2% of companies are rated by their customers as providing a great experience. This awful performance is not only terrible, it continues to decline, despite all the work done by CX departments to collect “Voice of the Customer” information.

I believe the demand for great service will only increase in the next 3-5 years – so many consumers see the great experiences delivered by the top 1-2% of companies (often ones who are new, digital-first businesses, where connecting all the dots for the customer is easier), But these few golden lights set the bar very high for everyone else – expectations for great customer experience will not diminish, and as so few companies succeed in a new transformational approach, this is what feeds the opportunity for those first movers to gain massive market share.

On the technology side, all types of AI will become increasingly central to the Journey Management and CX markets, and customers will start to expect more intelligence from brands because they will expect them to be using AI, and therefore really KNOW what each customer wants. There will also be a leading edge of consumers who will expect to see Agentic options become available so that they, the customer, can use the power of agents to support their interactions with key businesses.

SearchUnify Lens:

We view this demand for a brand to “really know” the customer as the ultimate validation of a fully implemented Agentic AI strategy. Mark Smith’s prediction aligns perfectly with our focus on Proactive and Predictive Support.

* Knowing the Customer: Our platform unifies all siloed data (the “single connected data view”) to create dynamic, personalized profiles based on user behavior, intent, and product maturity. This enables hyper-personalization at scale.

* Proactive Action: We use specialized Agents, like the AI Escalation Manager and AI Proactive Support Agent, to monitor patterns, anticipate needs, and intervene before issues spiral. This is the essence of moving from reactive service to a future-ready, proactive CX.

* Agentic Self-Service: We meet the demand for self-service Agentic options with the Agentic AI Suite and AI Support Agent, which autonomously guide customers to resolution or a seamless, context-rich human handoff.