When can AI agents meet customer expectations? What are the right approaches, today and tomorrow?

We observe that Customer Services (insourced and outsourced – BPO) often display studies showing that “customers prefer to talk to humans rather than interact with AI robots”, but these Customer Services are nevertheless offering more AI services.

Customers do prefer to interact with humans, yet they buy products and services from sellers who strive to reduce the human cost by offering AI support solutions.

But is it economical to entrust all customer interactions to AI agents? Does the customer really win, does he really have a choice?

It is easy to understand that it is difficult to convince a customer to pay a premium at the time of purchase to be able to interact with a human in case of possible support needs,

To stay competitive, you need to find ways to make customer service operations less expensive without degrading quality by using AI-powered tools to boost the productivity of human agents.

What are the limits to consider?

In the case of basic, simple, repetitive interactions that do not involve major risks such as:

Financial risks
Reputational degradation
Risk of non-compliance
Legal risks

These interactions can be handled by AI agents.

On the other hand, during interactions that require:

Empathy to reduce customer stress.
Clarify the customer’s description of the problem.
Solve complex multi-source problems.
Consider various combinations of solutions with choices to be made
An understanding of the compliance issues at stake (financial, health-related, etc.) with a critical impact for the customer and the company, in terms of safety, security, etc.

Then humans can interact in a much cheaper and more efficient way.

What are the constraints to make humans efficient?

However, we must be aware that this efficiency at a controlled cost requires that effective tools be provided to agents:

Access to up-to-date processes.
Access to technical references.
Access to complex diagnostic methods (decision trees, etc.).
Climbing protocols.
Clear and smooth clearing protocols.
Contributing to the daily improvement of these tools by providing meaningful feedback.

When AI works… And when it doesn’t

Let’s be fair: there are cases where AI brings real value to customer services.

Instant answers to frequently asked simple and non-English questions,
24/7 availability.
Ability to handle spikes in demand.
applications in several languages.

For well-defined routine tasks, automation can be really helpful.

The problem arises when you try to apply the same logic to the entire spectrum of customer service. Exceptional situations, complex problems, frustrated customers, or sensitive complaints require empathy, flexibility, and human judgment – precisely what AI can’t always deliver today.

A March 2025 report by McKinsey & Co. showed that 71% of companies are now using generative AI in at least one business function, but adoption is significantly lower in highly regulated industries: 63% in healthcare and 65% in financial services, precisely where errors have the most impact.

A proposal for balance

Maybe the question shouldn’t be “AI yes or no?”, but “how much AI and where?”. A smart hybrid approach would be:

Use AI to filter and categorize initial queries, recognizing their limitations
Truly automate simple, repetitive tasks where errors have little impact.
Implement human verification systems for critical responses (keeping humans in the loop, as recommended by Red Hat)
Facilitate quick (instantaneous?) access to a human when the situation requires it or when the AI expresses uncertainty (knowing that one of the problems of AI agents is to admit that they are not able to answer correctly… see our previous publications)
Include clear warnings about when information is AI-generated
Train human agents to work with AI tools that empower them, not replace them
Measuring success from the customer’s perspective in the foreground, not the company’s, success for the customer inevitably leads to the company’s success. The opposite is rarely true…
Be transparent about when customers interact with AI and when with humans.
Trust the critical thinking of your teams by involving them directly in the improvement of solutions provided to customers.

We can summarize this long list by recommending that your teams be provided with tools that use AI and manage your company’s knowledge while improving it with a Knowledge Management System (KMS) platform

The inconvenient question

Ultimately, a tricky thought remains: are companies adopting AI in customer service because it’s the best solution for their users, or because it seems like the solution to protect profitability? Is it a real innovation or an optimization disguised as modernity?

And do they do so with full knowledge of the real technical limitations of these systems? The data suggests not. When GPT-4 and GPT-4 Turbo, the most accurate models available, hallucinate 3% of the time; when advanced reasoning models such as O3 and O4-mini hallucinate 33% and 48% of the time, respectively; when OpenAI’s largest and most expensive model needs to be retired after just 4 months; When courts start holding companies accountable for the false information provided by their chatbots, it all suggests that the industry is trying to run before it learns to walk.

The answer likely varies from company to company, but the deafening silence on customer satisfaction studies, abandonment rates in automated systems, the number of users desperately seeking the “talk to a human” option, and the real, documented technical failures of LLMs, suggests that we may not be asking the right questions.

Technology is a tool, not a goal. And a tool is only useful if it solves the real problems of the real people who use it, and if it works reliably in the real world, not just in lab benchmarks.

As long as decisions about AI in customer service are made in the boardroom by looking at spreadsheets and business presentations from technology vendors, rather than talking to real customers and with an honest understanding of documented technical limitations, we will continue to see implementations that prioritize business efficiency over the human experience.

And perhaps most worryingly, we will continue to see companies surprised when their AI systems fail, when their customers become frustrated, and when they discover that short-term savings can be very costly when they measure up to the loss of customer reputation, trust, and loyalty.

February 2, 2026February 2, 2026

The technical reality: LLMs aren’t as brilliant as they seem !

Beyond the marketing hype about the spectacular advances of AI, the technical reality of large language models (LLMs) has significant limitations that are rarely mentioned in corporate presentations.

Hallucinations: the persistent Achilles’ heel

One of the most serious problems is that LLMs can confidently “hallucinate” information. These systems invent data, quotes, or facts that sound perfectly credible but are completely false. A Vectara study found that the most accurate models, GPT-4 and GPT-4 Turbo, hallucinate about 3% of the time when summarizing texts, while other models achieve error rates as high as 27%.

In customer service, this has real and costly consequences. In February 2024, Air Canada was ordered by a Canadian court to pay compensation to a customer after its chatbot fabricated a bereavement fee policy that didn’t exist. The bot confidently claimed that customers could request retroactive discounts up to 90 days after ticket issuance, which is completely false according to the company’s actual policy. Other notable cases include DPD, a European logistics company, which had to disable part of its chatbot after it started insulting customers and describing the company as “the worst delivery service in the world.” Virgin Money was also forced to apologize after its chatbot reprimanded a user for using the word “virgin.” And Cursor, an American tech startup, had to limit the damage when its chatbot informed customers of a radical change to its usage policy that was entirely fictitious.

The paradox of advanced reasoning models

Paradoxically, more advanced reasoning models, which use “chain of thought” approaches to break down complex problems into smaller pieces, appear to hallucinate more often than ordinary LLMs, according to Vectara’s analysis. OpenAI acknowledged in a performance report for its latest reasoning models that o1 hallucinated 16% of the time when synthesizing public information about people, while its newer models o3 and o4-mini hallucinated 33% and 48% of the time, respectively.

Basic mathematics and logical reasoning

Ironically, while companies market these systems as “superintelligences,” LLMs struggle noticeably with tasks any elementary school student could solve. Basic mathematical reasoning remains a weak point, which is problematic when customers ask questions about discounts, warranty dates, or cost calculations.

How can we manage this risk and have complete confidence in our AI-powered tools?

We have identified precautions to take and methods to follow in order to make the most of AI capabilities (for customer services as in all areas that handle critical information) and we will share these elements in the last of the 5 articles we are publishing on this subject.

Stay tuned!

December 8, 2025December 11, 2025

Checkmate for Customer Service? When Knowing the Rules Is No Longer Enough!

Comparing a game of chess with a customer service interaction may seem unexpected at first. Yet, when you look closely at the structure and progression of both, the analogy becomes surprisingly insightful.

While their objectives differ radically—checkmate vs. customer satisfaction and problem resolution—both follow a similar escalation in complexity as the interaction unfolds.

Conceptual Parallels

Chess Game	Call Center Interaction	Meaning / Analogy
Openness strategy	Call opening / reception.	Set the tone and take control from the start.
Tactical combination	Handling of objections.	Quick thinking to turn things around.
Late-game accuracy	Closing of the call.	Ensure resolution and satisfaction before finishing.
Sacrifice	Offer compensation or a gesture of goodwill.	Short-term loss for long-term gain (loyalty or retention).
Checkmate	Customer satisfaction and resolution	Achieve the desired result in an efficient manner.
Critical error	Communication error / violation of rules	A costly mistake that affects the results.
Pat (blocking)	Deadlock / escalation	Neither side achieved its goal.
Time pressure	High call volume periods	Decisions under pressure; Compromise between efficiency and precision.

Now that the parallel between the 2 activities is clarified, the behavior of the AI on these interactions becomes interesting to observe:

An illustrative example of the limitations of LLMs (large language models) comes from documented experiments with chess.

In March 2024, Chess.com held a showdown between ChatGPT and Google’s Gemini, where both systems could perfectly explain the rules of chess when asked directly, but then violated those same rules repeatedly during the game. Both bots constantly attempted to make illegal moves, and when they were informed of the error, they continued to come up with invalid moves.

Nikola Greb, an NLP data scientist and former ELO 2000+ junior chess champion, played several games against ChatGPT-4 in January 2024 and documented that the model played “like a grandmaster” in the opening first moves, but deteriorated significantly as the game progressed. ChatGPT-4 began to hallucinate, coming up with impossible movements even after being warned. Greb concluded that the overall rating of the system was below 1500, and observed something crucial: “No implicit rule learning has taken place – ChatGPT-4 still hallucinates at chess, and continues to hallucinate after the warning about hallucination. This is something that cannot happen to a human.

This disconnect between what an LLM can “say” and what it can “do” reveals a fundamental limitation: they don’t have real mental models of the world. In the context of customer service, this means that a bot can perfectly recite company policy but apply it incorrectly in specific situations, or it can explain how a product works without being able to diagnose a problem with it.

The Chatbot Chess Tournament 2025

In January 2025, a chatbot chess tournament aired on the GothamChess channel pitted professional chess engine Stockfish against seven generative AI chatbots, including ChatGPT, Google’s Gemini, and X’s Grok. The results were exactly what you would expect when language models try to play chess: decent opening moves followed by increasingly chaotic attempts to circumvent the laws of the game. The Snapchat chatbot decided that the pawns could move sideways like a tower, and when the error was reported, it repeatedly refused to continue saying “I’m sorry. I can’t engage in such a conversation. Let’s keep our conversation respectful.”

The problem of memory and context

LLMs have strict memory limits. While newer models offer wider windows of context, they still treat each conversation as relatively isolated. This means they can “forget” crucial information provided at the beginning of a long conversation, forcing customers to repeat themselves.

In one of the following articles, we will see how to avoid putting the customer in failure while making the best use of the undeniable capabilities of AI…

December 2, 2025

Why are companies adopting AI in customer service? A necessary reflection

Artificial intelligence has burst into customer service departments with breakneck speed. Chatbots, virtual assistants and automated systems are multiplying on websites and applications, promising to revolutionize the user experience. But it is worth asking: are companies making this decision based on solid evidence or simply following a trend?

The seduction of immediate savings

Let’s not fool ourselves: the economic factor is the big elephant in the room. Automating customer service can reduce operational costs dramatically. A chatbot doesn’t need vacations, doesn’t ask for salary increases, and can serve thousands of users simultaneously. For CFOs, the equation seems simple.

However, this short-term view ignores hidden costs: the development and implementation of robust AI systems, ongoing maintenance, the formation of hybrid human-machine teams, and most importantly, the reputational cost when technology fails or frustrates customers.

The Corporate FOMO Effect

There is a clear “fear of being left behind” in the business world. When competitors announce their advances in AI, boards of directors push to “do something with artificial intelligence.” AI has become a marketing element, a box to tick in the annual presentation of results.

This reactive, rather than strategic, adoption explains why so many implementations seem botched: confusing interfaces, bots that don’t understand basic queries, or systems that frustrate more than they help. Technology is deployed not because it solves real problems, but because “you have to be there”.

Did anyone ask customers?

Here we come to the most delicate point. How many companies have conducted serious studies on what their customers really prefer before automating? Anecdotal evidence suggests that many users still greatly value human touch, especially in complex or emotionally charged situations.

No one wants to navigate endless automated menus when they have an urgent problem. No one enjoys repeating their query three times to a bot that doesn’t understand the context. And yet, these experiences multiply every day.

The paradox is that it is a known fact (see studies) that clients prefers to deal with their pairs (human) when they are available, if they’re not available for any reason, customers seem to cope with the AI alternatives But the core paradox is that customers keep purchasing products and services without clearly selecting the ones with their preferred media of support: human, is it because there is very little offering on the market promoting customer support “made by humans”? Should this “label” be developed and promoted.

The efficiency argument… for whom?

Companies talk about “improving efficiency”, but efficiency for whom? A system can be efficient for the business (it processes more queries with fewer resources) and simultaneously inefficient for the customer (it requires more time, generates more frustration).

The real question is: are we measuring success correctly? If the metrics are purely internal (number of queries processed, average response time, cost reduction), we are optimizing for the business, not for the customer.

August 11, 2025

Why AI Projects Fail – The Problem Nobody Wants to See

The AI Hype Trap

We live in an era where it seems artificial intelligence must be present in every corner of our organizations. From startups to multinational corporations, everyone talks about “digital transformation” and “AI adoption” as if it were the magical solution to all business problems.

However, this pressure to implement AI solutions is generating a worrying phenomenon: technology projects without real value and initiatives that consume resources but don’t provide tangible benefits. This is what we could call “useless” AI projects – implementations that sound innovative in presentations but fail to generate real impact.

The Root Cause of Failure: Fragmented Knowledge

The difference between success and failure in AI doesn’t lie in the technology itself, but in something much more fundamental: most organizations don’t have their critical knowledge consolidated.

Organizational knowledge isn’t a file you can upload to a system. It’s a complex ecosystem where decades of experience, historical decisions, specific behavioral patterns of your customers, and the tacit experience of your teams combine in subtle ways.

The Critical Problem:

In most organizations, this knowledge is fragmented and dispersed. Part in the heads of key professionals, part in outdated documents, part in isolated systems, and a significant portion is simply lost when people change positions or leave the company.

Without a deliberate strategy to capture, structure, and centralize this knowledge, any AI initiative will work with incomplete information and lose its potential to generate differentiated value.

The Two Types of Knowledge You Must Consolidate

Explicit Knowledge (what should be documented and centralized):

Critical processes that actually work, not just the official ones
Historical decisions and their outcomes, including instructive failures
Specific success patterns both from your sector and typical clients
Key relationships with customers and suppliers, and their evolution
Metrics and KPIs that actually predict results
Solved use cases and proven methodologies

Tacit Knowledge (what must be captured before it’s lost):

Intuition developed through years of practice in critical roles
Context that allows correct interpretation of data
Exceptions and special cases not in the manuals
Human relationships that influence decisions
Unwritten criteria for making complex decisions
Early warning signals that only experts detect

The Uncomfortable Reality

If a key employee can leave tomorrow and take critical knowledge for your business with them, your organization isn’t ready for AI. It’s ready for chaos.

The True Cost of Failure Due to Fragmented Knowledge

When you don’t solve knowledge consolidation before implementing AI, costs multiply exponentially:

Direct Costs That Scale Out of Control:

AI services that consume resources without generating proportional value
Infraestructura subutilizada por implantaciones incompletas
Specialized consultants continuously redesigning systems that don’t work
Internal staff dedicated to manually supervising what should be automatic

Hidden Costs That Appear During Implementation:

Preparation of fragmented data (frequently 70% of the total effort)
Complex integration between systems that weren’t designed to communicate
Infinite iterations to optimize results based on incomplete information
Change resistance from teams who don’t trust inconsistent systems

The Highest Cost: Lost Opportunity

Resources invested in worthless projects while competition advances
Organizational distrust toward future technological initiatives
Lost time that could have been invested in first consolidating knowledge
Competitive advantage ceded to organizations that do have their knowledge structured

The cost of failure far exceeds the cost of doing things right from the beginning.

The Real Competitive Advantage:

Unique Knowledge + AI

Most AI projects fail because they’re based on generic information available to anyone. Any competitor can access the same tools, the same public datasets, the same best practices.

The true value lies in connecting AI with the specific knowledge that only your organization possesses: unique behavioral patterns of your customers, internal processes optimized over years, supplier relationships built on mutual trust, and the accumulated “know-how” of your teams.

What differentiates success from failure isn’t what AI can do, but what your organization knows that can be enhanced with AI.

In a world where everyone has access to the same AI tools, the real competitive advantage lies in how you feed them with knowledge that only you possess.

The Prerequisite Everyone Ignores

Most organizations rush to implement AI without having solved a fundamental problem: they don’t really know what they know.

Management teams can rarely articulate with precision what their most critical knowledge is and where it resides. This isn’t management’s fault; it’s the result of decades of organic knowledge accumulation without a deliberate knowledge management strategy.

Implementing AI on a disorganized knowledge base is like building a skyscraper on sand foundations. It’s technically possible, but the result will be unstable and expensive to maintain.

April 17, 2025April 17, 2025

Should businesses that prioritize Trust, Safety & Security rely on AI tools to assist their customers, prospects, and partners?

Yes, they should. However, they should exercise caution and be mindful of utilizing secure, safe, and trusted sources of information to guide their staff, customers, prospects, partners, and providers.

This is an obvious and logical approach as the reputation and core existence of these businesses are built upon the values of Trust, Safety, and Security.

Which types of businesses are we referring to?

Immediate and evident answers include Banks, Financial Institutions, Insurance companies, as well as other compliance and risk-driven activities such as healthcare and accounting. Furthermore, industries like aeronautics, construction, and transportation have their own unique compliance requirements, expanding the list beyond the aforementioned sectors.

When customers or prospects inquire about the recommendations of SHA team Knowledge Experts regarding the implementation of AI Conversational Tools and Bots, our stance as Knowledge Management Operational Experts is unequivocal:

AI Bots and Conversational tools can undoubtedly fulfill CSAT, Cost Reduction, Productivity, and Reputation objectives, but only if there is a solid Knowledge Management foundation supporting these AI tools.

And yes, you’re correct in suspecting us of promoting our own business (solid KM), but it is worth noting that independent voices are echoing the same message: “Do not allow your AI-bot to scour uncontrolled areas for information; maintain control and ownership.”

Additionally, it is imperative to ensure the seamless functioning of your data infrastructure (SHA cloud architecture excels in this regard).

For instance,

Gartner recently proclaimed: “By 2025, 100% of generative AI virtual customer assistant and virtual agent assistant projects lacking integration to modern knowledge management systems will fail to meet their customer experience and operational cost-reduction goals.”

According to IDC’s 2024 white paper (IDC #US52048524 sponsored by NetApp), “Around 20% of AI projects may encounter failure without adequate support in data infrastructure,” as revealed by a recent study conducted by intelligent data infrastructure provider NetApp.

SHA is a Knowledge Management System designed and developed by experienced seasoned Customer Service experts who have brought their vision and daily operational experience to deliver a system that enhances Customer Experience, improves Staff Experience, and reduces the cost of service!

For more information on how SHA can support your Customer Service Operations get in touch with us:

/https://sha-saas.com/contact-us/

February 7, 2025February 7, 2025

Faced with the challenge of sharing company knowledge and finding a high-performance training monitoring tool? SHA has a solution for you!

SHA is a platform that combines Learning Management functionalities with Knowledge
Management tools, or in simpler terms: SHA offers a solution that merges LMS (Learning Management System) functionalities with a KMS (Knowledge Management System).

Introducing Solution Management System (SMS) by SHA, a new concept that simplifies Knowledge and Learning Management:

SMS is the result of combining KMS and LMS.

The SHA platform is for companies that handle a lot of information daily and need to ensure quick and easy access for their teams to share it with customers, prospects, suppliers, etc. These companies understand that knowledge is an essential part of their value and must be managed effectively. Users should also be able to contribute to improving the data by providing their opinions on its relevance.

SHA combines Knowledge Management with advanced Learning Management features by directing users to important content for optimal task performance (Cost, Quality, Velocity).

The sectors that benefit most from these tools are those that interact with customers (Customer Services, Call Center, etc.), but all divisions of a company seeking operational performance optimization are involved.

Through extensive discussions with our customers and prospects, we have identified the different types of content and their volatility over time (an important factor for content management).

The documents are the ones most frequently cited as priority content in KMS and LMS.

Tableau Statique

Category	Types of Documents	Activity Staff	Format	LifeCycle
Policies & Procedures	Employee handbooks, SOPs, guidelines	HR, Admin, Legal, General Affairs	PDF, Word (protected)	Medium (annual updates)
Contracts	Client contracts, vendor agreements	Legal, Sales, Providers, Customers	PDF (protected)	Definitive (rarely updated)
Financial Reports	Balance sheets, income statements, audits	Financial Staff, Admin	XLS, PDF	Medium (quarterly or annual updates)
Marketing Materials	Brochures, campaigns, presentations	Marketing, Sales, Communication, Prospects	PDF, PNG, PPT	Short (frequent updates)
Design Documents	Blueprints, CAD files, design drafts	Design, R&D	CAD, PDF, proprietary	Short (frequent updates)
Production Plans	Schedules, specifications, quality logs	Production, R&D, Customers	XLS, Word	Short (frequent updates)
Customer Records	CRM entries, customer feedback	CS Staff, Sales, Customers	XLS Database export	Medium (periodic updates)
Training Materials	E-learning modules, onboarding guides	HR, Service Staff, General Affairs	PDF, Video, PPT	Medium (review every 1-3 years)
Meeting Records	Agendas, minutes, action items	General Affairs, Admin, All Teams	Word, PDF	Short (specific project cycles)
Legal Filings	Compliance reports, patent filings	Legal, Financial Staff, R&D	PDF, Word	Definitive (rarely updated)
Internal Memos	Announcements, updates, notices	Admin, All Staff	Email, PDF	Short (one-time use)
Service Logs	Maintenance reports, issue tracking	Service Staff, Production	XLS, Word	Medium (regular updates)
Supplier Records	Invoices, delivery logs, contracts	Providers, Financial Staff, Admin	PDF, XLS	Medium (periodic updates)
Employee Records	Performance reviews, payroll, leave forms	HR Staff	XLS, Word	Medium (yearly updates)
R&D Reports	Experiment logs, technical reports	R&D, Design	PDF, XLS	Medium (review post-project or annually)

Key Highlights:

Formats:

The document format often balances usability (e.g., Excel for analysis, PDF for sharing) and security (protected formats for sensitive information).

Life Cycle:

Short: Updated frequently due to dynamic needs, e.g., marketing materials, production plans.

Medium: Updated periodically based on cycles, e.g., training materials, financial reports.

Definitive: Rarely updated, often for records with legal significance, e.g., contracts, legal filings

The most common question our prospects ask is how SHA manages to be efficient by merging two activities that usually require separate platforms.

How does SHA introduce LMS capabilities into a content management KMS platform?

It’s simple, thanks to three main factors:

Consider training content as standard “knowledge” content, even if the format, life cycle, and structure may differ from other content.
Use the Content/User duality, which is one of SHA’s unique features. SHA manages the interactions between content and users, always considering the relevance of the content from the user’s perspective. Did the information help them complete their tasks, or does it need improvement?
Focus on apprenticeship rather than training. Operational managers know that training doesn’t always have an impact, but learning does. Taking a training course doesn’t always lead to progress or improved KPIs, whereas learning with SHA’s quality control and monitoring tools ensures that training brings the expected benefits.

Whether your organization already uses an LMS or not, SHA easily integrates learning content, from structured courses to flash briefs, and helps management teams ensure knowledge acquisition.

If you’re already using Knowledge Management tools without Learning Management features, SHA can easily integrate your existing content and facilitate Knowledge Management while providing the benefits of Learning Management for your teams.

To experience the advantages of unified Business Knowledge and team Learning management, contact our Solution Management System experts. They will share their extensive experience in this field!

For more information on how SHA can support your Customer Service Operations get in touch with us:

/https://sha-saas.com/contact-us/

September 16, 2024September 17, 2024

How to meet the specific needs of Financial Services Customer Support?

Are all Customer Service queries (Pre-Sales, Sales, and Post-Sales) the same across industries and organizations? Let’s first acknowledge that the general requirements for Customer Service are:

Staff with extensive product and process knowledge
Customer-oriented processes and policies
Waiting time (including abandon rate and other operational KPIs)
Empathy (among other soft skills)
Strong leadership from agents to guide customers to the desired solution.

Let’s now try to answer the next question: are those requirements’ expected level of delivery similar across all industries?

The initial answer that comes to mind is that different products/services may require different levels of delivery. However, it’s not necessarily because the products/services are different, but rather the potential consequences of failing to meet the expected level that differentiate businesses.

For example, having an issue with a delivery address can cause a 24-hour delay in delivering a book, which is frustrating for the client. However, a wrong address entry can have severe consequences for an emergency ambulance.

Another factor is the organization’s setup, which means that the ways processes, policies, and product/service knowledge are generated and shared depend on the overall business organization.

For instance, if all business activities are in-house and located in a single location, the workflow for sourcing, authoring, and sharing key information (knowledge) will be completely different compared to an organization with multiple sites and a mix of in-house and outsourced service operations.

The level of compliance and legal constraints can also vary greatly depending on the type of service. Certain markets are subject to strict legal rules and are required to provide information within a specific format.

For example, if you’re selling shoes online or if you’re in the bank loan business, your constraints and corporate liabilities are of a very different nature.

Additionally, some industries have inherently complex processes and must keep track of historical legacy. Keeping track of past policies and long-term procedures requires managing a significant amount of information and knowledge with discipline.

For instance, in the consumer electronics industry, it’s not vital to keep track of the hardware specs of a CD ROM burner a customer purchased 10 years ago, but it is critical for a bank to understand the conditions under which a client was granted a certain mortgage rate 15 years ago.

Now, let’s review what makes Financial Services Customer Support so specific in terms of expectations for Customer Service and what makes Financial Industries (banks, credit providers, insurances, etc.) different in the way they provide information to customers.

At SHA, we worked closely with one of our highly reputed financial institution clients to ensure that our Knowledge Management Solution fits their market-specific quality levels while improving their cost of service and enhancing the staff experience.

So, what is so specific about financial information provision? Based on SHA’s experience, the following points stand out:

Massive volume of information from different data sources and formats. Financial processes often include legacy documentation as well as recently edited documents from legal, financial, commercial, and marketing departments.
Long and complex processes and procedures. This requires special techniques, such as tree-decision flows, to improve communication and interactions with customers. It’s not feasible for Customer Service staff to absorb all the details of each procedure.
Bidding procedures with legal exposure most of the time. Providing inaccurate information can lead to major image and financial risks. If Customer Service is outsourced to a BPO, the content must be validated by in-house experts as critical responsibilities cannot be delegated.
Customer experience affected by being “easy to do business with.” It’s a known fact that Customer Service staff can explain things better and faster when information is presented in a friendly manner.
Difficulty in training and retaining knowledge by users. With volatile product specifications and policy changes, it’s challenging to keep up with knowledge even on basic questions like exchange rates and interest rates.
Addressing 100% of the questions is crucial. Unlike other businesses, even a non-frequently asked question deserves the same attention as the top 10 queries. Public AI knowledge tools cannot be considered safe enough to retrieve critical information.

SHA-SAAS also brings our Customer Expertise to the solution, providing the following areas of opportunities:

Single system to control all processes and procedures. All inputs from legal, marketing, sales, finance, etc. are consolidated into one system with a simple and highly efficient search tool.
Quality control and distribution control. Agent and client experience feedback is used to identify the most difficult-to-understand processes, allowing authors to rework and improve them.
Change management. Knowledge must be dynamic, and ways of providing knowledge require continuous improvement plans for both the knowledge itself and its users.
Creation of formal/approved documentation/standard operation procedures. Thorough approval processes ensure compliance with Financial Institution governance rules, accommodating both simple and complex operation setups.
Integrated training control and planning features. The knowledge is critical, but the usage of knowledge by Customer Service staff is even more important. Advanced training modules, individual customized training programs, and individual knowledge performance monitoring are integrated into the Knowledge Management System.
Gap analysis and suggestions for improvement. Our in-house AI-based Knowledge Management Quality tools enable the detection of low signals of quality drifts in the content and suggest improvements.

SHA clients have enjoyed the following benefits using our Knowledge Management System:

Major reduction of paperwork. Not only eliminating “paper” based knowledge, but also reducing and optimizing process flows in a one-stop-knowledge shop.
Reinforcement and streamlining of approval processes. Alignment of approval processes with Corporate Governance Guidelines, providing a transparent, fast, and thorough workflow for authoring knowledge.
Reduction of “in class” training needs. Reduced training duration for new staff and update training, leading to higher productivity per head and better synchronicity with new products and policy announcements.
Improvement in troubleshooting accuracy and reduction in invested time. Better information provided to Customer Service teams leads to better productivity.
Improvement of Customer Experience and Staff Experience. Accurate, up-to-date information provision and efficient guidance result in an improved experience for both customers and staff.

Generic Requirements	SHA Features & Benefits	Financial Services CS Fit
Staff with top product and process knowledge:	Core Knowledge Management with integrated Training Feature	supports multi-format information and ensures freshness of data used by Customer Service teams. It is a perfect fit for in-house core validation governance with remote BPO operations.
Customer-oriented processes & Customer-friendly policies:	Integrated Customer Service staff feedback on the Knowledge Management System and SHA coaching module enable continuous staff improvements.	Clear explanations are essential for complex financial processes.
Waiting time	Reduced call duration with easy-to-find accurate information improves operational efficiency,	Crucial for cost-efficient financial operations
Empathy (within other soft skills):	Well-trained Customer Service staff provided with clear support information can focus on customer satisfaction and improve retention and acquisition.	Customer satisfaction has become a strong differentiator as financial services customers are becoming more volatile with the growth of online banking and insurance offerings.
Agent strong leadership to guide customers to the expected solution or offering:	Staff training and education focus shifts to call flow management rather than pure hard knowledge. Individual and team training track monitoring reinforces the quality of interactions with clients and prospects, leading to cost reduction and revenue growth through	First-time resolution for support and conversion rate, up and cross sales for commercial interactions.

Table1: quick summary of SHA KMS fit with Financial Services CS needs.

In conclusion, while Financial Services requires the basic Customer Service requirements to be fulfilled, there are additional elements that must be considered and addressed. SHA’s Knowledge Management System delivers these elements in a practical and cost-effective manner.

And if SHA excels in delivering the most demanding expectations, it is also a great fit for all other markets with different levels of expected performance.

For more information on how SHA can support your Customer Service Operations get in touch with us:

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December 1, 2022December 27, 2022

Video Killed the Radio Stars but Have Chatbot Killed Knowledge Management Systems?

As most of tools existing for quite a while, Knowledge Management Systems (KMS) were recently challenged by tools that looked more dynamic and more adapted to current tech trends: CHATBOT for example were expected to take over the support of clients having all kind of questions & queries.

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After a few years of hype, where do we stand?

Facts:

Let’s look at the bright side first:

Yes, CHABOT are up and running 7 days a week
Yes, CHABOT can address some easy to ask and easy to analyse queries. It is important to note that those queries have to be easy ones – means the customer have to ask those queries in a very simple manner, so the bot replying, will not get lost with too many words …

BUT

Facts again:

Studies shown that users don’t like to get support from bots (and very probably it is because they could not get proper answers from them…) Recent studies (ie study from Statista.com) found that the huge majority of customers (rough average 80%) prefer human interaction to AI driven interfaces as chatbot.

More studies forecast that bot assistants lacking integration with Knowledge Systems will NOT bring the expected CS and savings.

Very few businesses require 24/7 replies on queries, and when there is a critical need (health, security, financial transactions for instance) most of the time it is low volume and high quality required so human interfaces have to be there.

So KMS seems to be considered as the fundamental foundation which should not be neglected for both human and virtual Customer Support.

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Knowledge Management: a fundamental element that is here to stay?

No surprise that some other studies shows that KM has a bright future to help and support human beings (but not only) to reply accurately and consistently to queries from other precious human beings: customers and prospects.

Gartner found that 74% of Customer Support Professionals mentioned improving knowledge and content to their customers and employees as a priority.

There is a spot for bots and probably in the forthcoming years, technology will evolve so the current rejection could reduce. But we also need to consider that robots need to be fed, and when they’ll be working better, they’ll need to use some fundamental pieces of knowledge, and they’ll find in your KMS.

The days IA/Bots/ML will create their own knowledge (and mimic the current knowledge life cycle and workflow: identification of need, authoring, validating, monitoring, improving, and managing obsolescence…) are not there yet. And when it will happen, we’ll still have to ensure that 2 different lines of code would not generate 2 different solutions to similar queries just because some synonyms were used…

Some advices from the Customer Support Experts at SHA:

Start today with a KMS solution that will enable you to create, maintain and monitor your enterprise knowledge for your contact center staff as well as for your digital virtual agents, today and tomorrow!
Video might have killed the video stars some years ago (same as streaming has killed some others) but LPs are still there and Chatbot did not killed KMS, just made their existence more critical and more visible…
If you need to set up a KMS from scratch or move your existing KMS to a much more agile and cost-efficient system, have a look at what SHA can provide you!

October 13, 2022October 23, 2022

Can Knowledge Management be isolated from Training? How to close the loop?

Introduction

If Knowledge Management is based on the idea that an organization’s most valuable asset is the knowledge of its people and if we accept the fact that Knowledge freshness can be volatile then it becomes clear that both Knowledge Management and Learning Management are to be considered together.

Operational Context: what are the issues?

When running a Contact Center Operation, the relation between Knowledge and Learning is even more critical:

Once the initial and fundamental trainings are delivered, how to ensure your teams’ knowledge keep up with new information, changes, update?

How to avoid to pile up the necessary updates in one regular training session (ie 1hr per week or half a day per month), stuffing the staff with a lot of new things at the same time, and with little or no chance to digest the info?

As a result it is difficult for customer fronting staff managers to keep their team knowledge up to date.

Operational and Financial impact

The regular training updates, (are they short and weekly or long and monthly), bring constraints and costs such as having staff not productive for hours, training room, trainers, support materials…

It can be hard for contact center agents (for anyone!) to digest in one batch a lot of updates on a lot of different topics. It is also very well known that it is difficult to get trainees full attention and focus if there are a lot of different subjects to be covered in a short time.

Real time can be critical:

Regular consolidated updates can’t be real time by essence. So info freshness is at risk and communication to clients can be obsolete during a few days, meaning some customers might be given wrong info for a while.

In the other hand providing updates in advance, before the changes are implemented can also be misleading and confidentiality has also to be considered: some info can be under embargo (till they are live) like: product new specs, new price list, promotion…

Home working:

Today’s natural trend to favour Home Working can make the situation worse, it can be difficult to ensure that everyone is aware that they have news in the mail box which requires attention!

Knowledge Quality:

Knowledge Quality is extremely dependant on its freshness: Knowledge is alive, Training is its heart and KMS is its blood…

One strong point having a Knowledge Management System (KMS) is to get feedback on the content, so authors, writers can amend and improve their articles and make them easier to understand and to share.

In the contact center daily context, it is easy to understand that an agent looking for a solution in the KMS will not spend a lot of time about what could be done to improve the article, he’ll (rightly) focus on the customer experience not on the knowledge improvement.

When reading a training update during a quiet time during the day, the agent is not under the same immediate delivery stress so not only he can read and absorb better the content he’s submitted but he’s more likely to provide useful and relevant comments to the article.

Conclusion (and Solution)

We at SHA, led by experience, strongly believe Training, Learning and Knowledge Sharing are tightly interconnected.

This means that both Knowledge Sharing and Training have to be considered together.

We’d all agree that fundamental, theoretical, specialised trainings require hours/days and most of the time require classroom setup, but Knowledge update, process adjustments, products specification upgrade and the likes can fit in a totally different and far more efficient format.

That’s why SHA has developed a Training Module to “Close the Loop”

With SHA Training Update Module solution you will:

Avoid double flow management (training delivery and content sharing) for new updated, adjusted knowledge workflow.
Free up your training resources (trainers, equipment, rooms…) from the heavy load of minor training updates.
Keep your agent productive and focused during the peak periods.
Provide your agent guidance and support to absorb new things at the right moment.
Track and monitor training progresses and fix potential stress areas within the team
Provide your team more time and thoughts to give feedback on existing solution, hence enhance the content!
Manage your knowledge freshness and deliver real time up to date solutions to your clients and prospects.

Contact us to see more about SHA!

(s.lissillour@sha-saas.com)

SHA put together your massive Knowledge Encyclopaedia and your Real Time Training News Feed, in one app!

Never let your precious knowledge get obsolete!

Use SHA KMS Training Module and close the loop!