Part 60 - How are customer expectations changing?
When artificial intelligence makes knowledge, comparison and advice available in seconds, the customer expects more from the entire business
The customer has always had expectations.
That the product delivers what the business promises.
That the service works.
That the price feels reasonable.
That the information is correct.
That it is possible to get help when something goes wrong.
Digitalization has gradually placed new expectations on top of the old ones.
The online store made accessibility a given.
Search engines made information easier to find.
The smartphone made business accessible almost everywhere.
Social media made it easier to share experiences.
Digital payments made the actual purchase faster.
And throughout this development, something important happened:
When the customer experienced a better digital service in one place, she eventually began to expect something similar in other places.
Artificial intelligence can reinforce this development.
Because now it's not just businesses that are getting new tools.
The customer gets them too.
She can use AI to investigate a need.
Learn the technical terms.
Compare options.
Summarize tests.
Understand complicated products.
Prepare questions.
Analyze differences.
And perhaps ask your own AI assistant to find out which options are best suited.
Thus, the business encounters a customer who is potentially better informed, more efficient in obtaining information, and less willing to spend time on unnecessary friction .
That doesn't mean all customers become experts.
It also doesn't mean that everyone wants a conversation with artificial intelligence.
But the frame of reference for what a good digital customer experience can be is changing.
And when the customer changes, the business must understand what happens to expectations.
Artificial intelligence can reduce customer costs by searching for and comparing information
As information becomes more readily available, the expectation for quick and relevant answers increases.
Time has always been part of the customer experience.
How long does it take to find the right product?
How many pages does the customer have to visit?
How many documents does she have to read?
How many filters does she have to understand?
How long does she have to wait for an answer?
Traditionally, the customer has done much of this work themselves.
She has applied.
The click.
Read.
Compared.
Opened new tabs.
Tried to understand technical concepts.
And put the information together yourself.
AI can reduce some of this search and information cost .
The customer can instead ask:
"What are the main differences between these three options if I'm primarily concerned with light weight and long battery life?"
When such tasks can be solved more quickly, the customer's tolerance for poor information experiences can also decrease.
What was previously acceptable because "that's how websites work,"
may eventually be perceived as unnecessarily cumbersome.
The customer's expectation is shifting from finding information to getting help understanding it.
The future of digital customer experience is more about decision support than access to information
The Internet largely solved the problem of access.
The business could publish information.
The customer could find it.
But access to information is not the same as understanding.
A customer who is going to buy a camera, insurance, software, tools
or running shoes can find thousands of pages of information.
The problem is not necessarily a lack of information.
The problem may be understanding:
What does this mean for me?
This is where AI can change expectations.
The customer may be less impressed that the business has 300
product pages.
She can more easily expect help in understanding which of the 300 products suits her needs .
This is the transition we saw throughout Part 7:
From information...
to knowledge...
for decision support.
Personalization with AI increases the expectation of relevance, but also the need for boundaries
The customer can expect the business to understand the situation without having to explain everything again.
Digital personalization is not new.
Online stores have long recommended products based on previous purchases,
clicks or what other customers have viewed.
AI can make personalization more conversational and contextual.
The customer can describe the need directly:
"I'm going to buy my first electric bike. I cycle about eight kilometers to work, have a steep hill on the way home, and would prefer to avoid a lot of maintenance."
There is far more information than just clicking on the "electric bikes" category.
A good digital service can use context to make the advice
more relevant.
But here too a limit arises.
The customer wants relevance.
She doesn't necessarily want surveillance.
She may appreciate the business remembering something she has asked it to do.
remember, but react negatively if the system seems to know more than
expected.
This makes the balance between personalization, privacy, and control even more important.
The customer expects consistency when AI is used across channels
Product data, customer service, online store and AI advisor cannot give four different answers
Imagine that the customer asks a digital customer advisor about a product.
The KI advisor says that the product is suitable for outdoor use.
The product page says nothing about it.
The FAQ gives a different answer.
And customer service says the product should only be used indoors.
Who should the customer believe?
When AI makes information more accessible, inconsistent information also becomes easier to detect.
This means that the business needs clearer control over:
product data,
professional knowledge,
conditions,
prices,
delivery information,
customer service information,
and other authoritative sources.
AI thus not only makes the interface more important.
It makes the quality of the knowledge base more important.
Customers increasingly expect businesses to know their history without losing the human connection.
Omnichannel customer experience takes on a new meaning when AI can connect knowledge and dialogue
A classic frustration in customer service is having to start over.
The customer explains the problem in the chat.
Will be forwarded.
Explaining the problem again.
Calls.
Explaining the problem for the third time.
AI can potentially make it easier to summarize relevant history and provide the next employee with the necessary context, within sound privacy and data processing frameworks.
Thus, the expectation can change from:
“The business has many channels.”
to:
“The company should understand that I am still the same customer.”
Omnichannel is then less about how many contact points the business offers and more about the connection between them.
the contact points .
Artificial intelligence can make the informed customer even better informed
The information asymmetry between the business and the customer can be reduced
In many markets, the business has traditionally known far more about the product than the customer.
Economists describe such differences as information asymmetry .
The Internet reduced some of this asymmetry.
Customers gained access to:
price comparisons,
product tests,
reviews,
user experiences,
competitors' offers,
and enormous amounts of subject matter.
AI can make this information easier to process.
The customer does not necessarily need to read fifteen tests.
She can get help comparing them.
She can ask for counterarguments.
She can ask what weaknesses a product has.
She can get difficult technical terms explained.
She can prepare before the meeting with the seller.
This could further shift the balance of power.
"If you make customers unhappy in the physical world, they might each tell 6 friends. If you make customers unhappy on the Internet, they can each tell 6,000 friends." — Jeff Bezos
The digital customer gained a stronger voice.
AI can also give the customer stronger analytical capacity .
The customer can start delegating parts of the buying journey to their own AI assistant
The business must prepare for a customer who doesn't always visit the website first
This is perhaps one of the most interesting future perspectives.
We have long designed digital services with one assumption:
The person visits the website.
She is searching.
Navigating.
Filtering.
Reader.
Comparing.
Clicks.
But what happens if the customer instead starts with their own AI?
assistant?
She can ask:
“Find three options that meet these needs, explain the differences, and tell me what questions I should ask before buying.”
Then the AI system can become an intermediary between the business and the customer.
This can have major consequences for:
product search,
content strategy,
product data,
branding,
search engine optimization,
customer journey,
and digital visibility.
The business must not only make the knowledge understandable to
people.
It must increasingly ensure that the information is accurate,
structured, credible and accessible to digital systems .
The customer journey can evolve from click-based navigation to intent-based dialogue
The customer expresses what she wants to achieve, while AI helps find her way through the information
Traditional websites are largely built around navigation.
The customer must understand how the business has organized the information.
But the customer does not necessarily think in categories.
She thinks in terms of need.
Not:
"I want category 14B."
But:
“I need something to solve this problem.”
AI makes it possible to meet the customer more closely in this language.
This means that intention can become a more important entry point into the digital customer journey.
But menus, searches, filters and good information architecture are not going away.
Customers have different preferences.
Some want dialogue.
Others will quickly scan a page.
Some people know exactly which product they should have.
Others need extensive guidance.
The good customer experience of the future must therefore be able to handle multiple ways of reaching a decision .
The customer's expectation of speed must be balanced against the expectation of quality
The fastest AI response is not necessarily the best customer response
AI makes quick responses possible.
This can create a temptation to make speed the main goal.
But the customer doesn't always need the fastest answer.
She needs the right answer.
For a simple request, seconds can be excellent.
In the event of a complicated complaint, the customer may have a greater need for
Some actually understand the situation.
In the case of an expensive purchase, thorough advice may be more important than immediate response.
In cases of high risk, human control may be necessary.
Thus, the business should distinguish between:
speed as a value
and
speed as just a measurement .
The best customer experience occurs when the response time fits the task.
AI can increase the expectation of availability without requiring human customer service to be available 24/7
Digital assistants can handle some needs continuously and escalate others to humans
A digital advisor may be available at three in the morning.
It could be valuable.
The customer can get help with:
product information,
delivery issues,
simple comparisons,
instructions for use,
or common problems.
But this does not mean that all inquiries should be automated.
A good service can distinguish between what AI can handle responsibly and
that a human should take over.
This way, the customer can get both:
increased availability
and
human help when it is actually needed .
When AI makes it easier to switch suppliers, customer experience and trust become more important
Lower search costs can make bad customer experiences more expensive for the business
If it becomes easier to compare alternatives, it will also be easier to discover that other businesses offer something better.
A customer who previously spent an hour researching competitors may be able to do it much faster with AI support.
This can reduce some of the friction that previously held the customer back.
Then loyalty becomes more difficult to build through informational barriers .
The business must, to a greater extent, deserve it.
Through:
good products,
relevant knowledge,
good service,
trust,
predictability,
relationships,
and good experiences over time.
"The purpose of business is to create and keep a customer." —Peter F. Drucker
Technology can change the customer journey.
The fundamental requirement of creating value for the customer remains.
Customer loyalty in the AI age must be built through value, relationships and experiences
Personalization and automation can support the relationship, but they cannot replace trust
AI can make communication more personal.
But "personal" is not necessarily the same as personal relationship .
An automated message can use the customer's name.
An algorithm can recognize previous purchases.
An AI can formulate the text in a friendly way.
But trust is built through experience.
The business keeps its promises.
It treats the customer properly when something goes wrong.
It does not recommend unnecessary products.
It respects customer data.
It makes it easy to get help.
It takes responsibility.
These are not just communication tasks.
These are characteristics of the entire business .
Customer expectations are shaped by the best digital experiences – not just by the business’s closest competitors
A good customer experience in one industry can change expectations in a completely different one.
The customer does not live in one industry at a time.
She orders food.
Uses online banking.
Buying clothes.
Book trips.
Communicates with the municipality.
Streaming movie.
Shop for groceries.
Book a doctor's appointment.
And use digital assistants.
She takes her experiences with her.
When one service does something very simple, other services can suddenly
feels more cumbersome.
Thus, businesses compete indirectly against the customer's best experiences , not just against direct competitors.
"People don't want to buy a quarter-inch drill. They want a quarter-inch hole.” — Theodore Levitt
Technology is changing.
But the customer still evaluates the business based on how well it helps her solve what she is actually trying to get done.
Customer insights become more important as artificial intelligence makes it easier to produce solutions
The business must understand what problem the customer is actually trying to solve
If AI makes it cheaper and faster to produce:
content,
software,
campaigns,
analyses,
product texts,
and digital services,
a paradox arises.
The business can do more.
But what should it do?
That's where customer insight comes in.
If the business does not understand the customer, AI can make it possible to produce the wrong solution much faster.
Therefore, good customer insight can become even more important in a world with
generative AI.
Not only:
What did the customer click on?
But:
What is the customer trying to achieve?
What makes her insecure?
What creates trust?
Why does she choose an alternative?
Why is she abandoning the purchase?
What happens after the purchase?
What problems arise?
What does she tell customer service?
Technology can help us analyze.
But businesses still need to ask the right questions .
Magne's perspective – the customer has never been a channel, a click or a conversion
Digital customer orientation starts with understanding the person on the other side of the screen
Through decades of digitalization, it has been easy to reduce the customer to numbers.
Visitor.
Click.
Conversion rate.
Average order value.
Bounce rate.
Customer satisfaction.
All of these goals can be useful.
But behind every data point there is a human being.
A person trying to understand something.
Find something.
Buy something.
Solve a problem.
Get help.
Or simply get on with your day.
For Magne, this has been a common thread throughout several technological
changes:
Technology must start with the customer – not with the technology.
AI changes the tools.
It does not change this basic principle.
The buddy's perspective – AI can understand patterns in customer data without understanding the customer as a human
Artificial intelligence can support customer insights, but human judgment and context remain crucial
The buddy can analyze thousands of customer comments.
Find recurring themes.
Group questions.
Summarize problems.
Identify linguistic patterns.
Compare options.
But the data doesn't necessarily tell the whole story.
A customer may click without purchasing.
Why?
The price?
Insecurity?
Delivery time?
Lack of trust?
She was interrupted by the child?
She was just going to investigate?
She bought the product at the store the next day?
Behavioral data shows what happened .
They don't always explain why .
Therefore, the business still needs:
qualitative insight,
conversations,
observation,
professional competence,
experience,
and human interpretation.
AI can make customer insights more powerful.
But it should not make the business less curious about the customer.
Four key expectations emerge in the AI-supported customer journey
Relevance, coherence, transparency and control will become important qualities in the customer experience of the future
When we summarize the developments, four expectations stand out in particular.
Relevance. The customer expects information and help that is appropriate to the situation.
Coherence. She expects the business's channels, information and services to be interconnected.
Transparency. She wants to understand recommendations, terms, and how AI is used when it matters.
Control. She must still be able to choose, correct, reject, and get human help.
The latter is important.
An intelligent customer experience should not make the customer powerless.
It should enable the customer to make better decisions .
Technical terms for customer experience, AI, personalization, and the digital customer journey
The concepts that become important when the customer themselves gains access to artificial intelligence
Customer Expectations - The customer's ideas about what quality, service, availability and value the business should deliver.
Customer Experience (CX) The customer's overall experience of the business through touchpoints and the relationship over time.
Customer Journey - The sequence of activities, needs and touchpoints the customer goes through before, during and after an action or decision.
Customer Insight - Systematic understanding of customers' needs, motivations, behaviors, problems and experiences.
Search Cost - The time, effort or other resources the customer must spend to find and evaluate relevant information.
Information Asymmetry – Information Asymmetry A situation where the parties to a transaction have unequal access to relevant information.
Personalization - Customization of content, services, or recommendations based on relevant information about the user's needs or context.
Hyper-personalization - More detailed and dynamic personalization based on multiple data sources, context and possibly AI.
Intent – The underlying goal or need behind the customer's search, question, or action.
Decision Support - Information or tools that help the customer understand alternatives and make a choice.
Omnichannel – Omnichannel - An approach where various touchpoints and channels are designed as a coherent customer experience.
Conversational Commerce – Use of dialogue-based interfaces for search, advice, service and commerce.
Human Handoff - Transfer from an automated or AI-based service to a human when the situation requires it.
Customer Loyalty - The customer's inclination to continue the relationship with a business, based on, among other things, value, experience and trust.
When the customer gets artificial intelligence, the business must become better at deserving the customer's time and trust
The customer of the future doesn't necessarily expect more technology – she expects technology to make the experience better
It's easy to believe that the AI customer will expect AI everywhere.
That's probably not the most important thing.
The customer doesn't necessarily wake up in the morning and think:
"Today, I really hope that the bank, online store, and insurance company have implemented generative AI."
She will get her task done.
Fast when it should be fast.
Thorough when it requires thoroughness.
Simple when it should be simple.
With human help when the situation requires a human.
This is what makes AI interesting from a customer perspective.
Not the technology itself.
But the opportunity to reduce friction, make knowledge accessible,
improve decisions and create better services.
From a stronger customer to a bigger question about the role of humans
When both the customer and the business get AI, human qualities become more important in new ways
We began the previous article with the business.
What happens when all businesses have access to artificial intelligence?
In this one we have reversed the perspective.
The customer gets it too.
She can come better prepared.
She can investigate further.
Compare faster.
Ask more difficult questions.
Expect greater relevance.
And more easily detect when your business information is lagging
together.
It can make the customer stronger.
But at the same time, an interesting paradox arises.
The more technology can do...
the more important the question becomes of what people should do .
Who is going to understand the complicated situation?
Who should take responsibility?
Who should build the relationship?
Who will ask the questions no model thought to ask?
Who should use experience and judgment when the data does not provide a clear answer?
answer?
Who will create new ideas?
Who should decide what the business should do – not just what
it can do?
Thus we have come to the third question in our final stage.
What will be the most important role of humans?
Because maybe it's precisely when machines become better at doing more...
that we need to understand what people are most important for.
Academic background and further reading
This series also builds on my own professional journey through the Web Design study , the eMarketing study , Innovation and Commercialization and professional seminars in San Francisco and Oxford . Here you will find the background, professional environments and experiences that have followed the development from the early years of the web to today's digitalization.
Recommended literature
Developments in artificial intelligence are moving faster than perhaps any other field of study in our time. No single book can provide all the answers, but good books can provide a solid foundation for understanding the technology, the opportunities, and the challenges.
In the KI-Kompis series, we therefore recommend a selection of books that illuminate artificial intelligence from different perspectives – technology, strategy, management, innovation, ethics, digitalization and practical application. Together, they provide a broader understanding of how artificial intelligence affects people, businesses and society.
Click on the book icon to see the full literature overview with recommended books on artificial intelligence.
Recommended books from our library
Build a Large Language Model (From Scratch)
Author: Sebastian Raschka
Short review
This book takes the reader behind the scenes and shows how a modern language model is actually built – step by step. Sebastian Raschka explains advanced concepts in an educational way and provides a unique understanding of how large language models like ChatGPT work. Although the book contains code examples, it is also very valuable for anyone who wants a deeper understanding of the technology behind artificial intelligence.
Why we recommend the book
One of the most talked about books on large language models. Perfect for those who want to understand how artificial intelligence works beneath the surface and why language models have become a revolution in digitalization and knowledge sharing.
Quick Start Guide to Large Language Models
Author: Janelle Shane
Short review
This book provides a practical and easy-to-understand introduction to large language models (LLMs). Sinan Ozdemir explains how language models are used in modern businesses, how they can be integrated into work processes, and why they have become one of the most important technologies in artificial intelligence.
Why we recommend the book
A very good book for anyone who wants a quick and practical introduction to language models. It is suitable for both beginners and professionals who want to understand how LLMs are used in practice.
Artificial Intelligence: A Modern Approach: The Future Is Coming! Discover How Artificial Intelligence Will Change Your Life!
Authors: Stuart Russell & Peter Norvig
Short review
This is the world's most famous textbook on artificial intelligence and is used in universities worldwide. The book covers the entire subject area – from problem solving and machine learning to language understanding, robotics and ethics – and is considered a classic in the AI field.
Why we recommend the book
If you are only going to own one academic book on artificial intelligence, this is one of the very best choices. A timeless classic that provides a solid academic understanding of artificial intelligence and is still used as a syllabus at leading universities around the world.
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