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How to use AI to enrich Servant Leadership: A…

In today’s rapidly evolving business landscapes, markets and the AI rush, leaders can feel the pressure to coincidentally deliver results, navigate complexity, and support the wellbeing of their people.
While many leadership models or trends come and go, servant leadership has endured because it places people at the center of growth.
It is founded on service, active listening, and on committing to the growth of their people and their teams.

But as organizations embrace digital transformation, one question naturally arises:
Can AI support, or even help enhance servant leadership?

The answer is yes — if used responsibly, intentionally, and in alignment with human and company’s values it can yield impactful results.
When applied correctly, AI can amplify a servant leader’s ability to understand their people and customers, remove barriers, reduce bias, and help employees thrive.

This article explores how AI can strengthen servant leadership, and how organizations can implement it without losing the human touch.

1. Servant Leadership in the Age of AI

Servant leadership is grounded in the belief that leaders exist to serve, not to lead from behind closed executive doors without connecting with the people who create the real value on the ground.
The focus is on enabling others to grow, succeed, and reach their potential.
It demands empathy, active listening, and a genuine understanding of people, without taking away the leader’s ability to be firm, clear, and uphold high expectations.

AI, for many leaders, feels like the opposite: automated, technical, impersonal by removing humans out of the equation.
But this is a misconception.

When designed with intention, AI becomes a powerful supporting tool, not a substitute for leadership. It helps leaders do what they already strive to do: serve their teams and customers better.

In essence:
AI does the heavy lifting and time-consuming tasks, so humans can be dedicated to purposeful leadership and high-quality human service.

2. AI Enhances a Leader’s Ability to Understand Their People

One of the core responsibilities of a servant leader is to “know” their team, their workload, aspirations, struggles, motivations, and strengths.

AI can significantly elevate this ability through data-driven insights:

✓ Real-time workload visibility

AI tools can analyze calendar activity, project timelines, and communication patterns to identify:

overload or burnout risk
underutilization
inequitable distribution of work

A servant leader can intervene early to rebalance work, offer support, or adjust deadlines.

✓ Early signals of disengagement

Sentiment analysis and behavioral trends can highlight when an employee might be:

losing motivation
feeling isolated
overwhelmed by change
under stress

These insights allow for thoughtful, proactive check-ins not reactive crisis management.

✓ Personalized growth recommendations

AI-powered learning platforms identify:

skill gaps
career aspirations
needed certifications
ideal learning paths

Employees receive tailored development opportunities based on their personality traits, strengths and weaknesses to help them improve their skills and progress in alignment with their current role but also with their life or career objectives. This approach aligns with a servant leader’s commitment to growth.

We’ve seen many organizations invest much of their time and resources to survey employees’ sentiments and do performance reviews only to do nothing with them after. This data needs to be processed and analyzed to retrieve patterns and suggest new course of actions to show responsiveness, active listening and engagement between executives and their workforce.

This responsiveness can motivate employees to continue to voice their concerns and be more dedicated in their roles, so they can therefore serve customers better.

Often organizations don’t have the resources to analyze this data, it is only shared with managers that don’t have the time or the tools to responds to their teams concerns and this data is therefore kept unused to move the organization forward.

AI can analyze employee voice programs and structures to pinpoint trends, and what employees share by degree of importance-impact-urgency correlated with what could make each team more performant.

AI supports — not replaces — empathy.
It simply helps leaders see what is often invisible.

Servant leadership becomes stronger when it is supported by evidence, not only intention.

3. AI Gives Leaders Back the Gift of Time

One of the biggest obstacles to servant leadership is time. Leaders want to support people, listen, coach, and mentor, but administrative tasks consume their calendars.

AI eliminates many of these barriers by automating routine processes:

✓ Automated meeting summaries & reports

Leaders no longer spend hours writing or reviewing notes or write extensive daily, weekly or monthly reports.

✓ Predictive dashboards

Manual reporting is replaced with instant, accurate insights to act upon (spending less time writing all the necessary reports and more time thinking of strategies to act on the insights at hand)

✓ Intelligent scheduling

AI assistants can:

plan meetings
suggest optimal times
prevent conflicts
manage follow-ups

✓ Workflow automation

From approvals to documentation, AI handles repetitive work, freeing leaders to focus on human connection.

This is critical because servant leadership requires presence.
AI creates the time and space needed for leaders to show up fully for their teams.

4. AI Makes Coaching More Purposeful and Personalized

Great servant leaders are exceptional coaches.
AI enhances coaching by providing leaders with:

detailed performance patterns
communication style analysis
learning progress metrics
collaboration network insights

For example, AI can show:

who collaborates naturally
who avoids conflict
who works in silos
who contributes silently without recognition

This gives leaders a factual foundation for personalized guidance, instead of relying solely on observation or intuition.

It also helps employees feel seen, understood, and supported, all essential elements of servant leadership.

5. AI Improves Communication and Strengthens Psychological Safety

Teams thrive when there is psychological safety, which is the confidence to speak openly without fear.
Servant leadership requires deep listening, empathy, and openness.
This aptitude helps leaders to better support their teams and communicate clearly what is needed for the organization to move forward, explain the common goal and how to reach it, based on current sentiments.

AI helps create this environment by:

analyzing sentiment in team chats or surveys
identifying tension early
detecting shifts in morale
offering anonymous feedback channels

Leaders can act on issues before they escalate, demonstrating care and responsiveness.

Additionally, AI-enabled communication tools can help leaders:

tailor messages
adjust tone
clarify complex information
avoid misunderstandings

Healthy communication strengthens trust, being at the core of servant leadership.

6. AI Elevates Customer Service in Service-Centered Cultures

Servant leadership extends beyond employees, it is also about serving customers with excellence.
This is usually done by having open canals to understand customers’ satisfactions and frustrations to respond to them in a timely manner instead of being lost in internal systems which leads to customer disengagement at the risk of losing their loyalty.

AI supports this by:

predicting customer needs
enabling more personalized experiences
providing real-time customer insights
reducing wait times
automating routine service inquiries

These elements can strengthen a Voice of the Customer program by processing large volumes of unstructured data such as calls, chats and reviews, then turning them into real-time insights that help companies make smarter, faster decisions

Employees feel empowered with better tools, and customers feel genuinely cared for.

When employees are not overwhelmed by repetitive tasks, they can bring more empathy, attention, and creativity to customer interactions.

When employees feel heard and can escalate issues quickly with timely responsiveness, they are better equipped to deliver excellent customer experiences and feel more motivated to go the extra mile.
This stands in contrast to situations where they must navigate unnecessary complexity, fill out countless forms, or go through multiple layers of approval before getting a response.

7. Where AI Needs Guardrails to Protect Servant Leadership

While AI can strengthen servant leadership, it can also undermine it if misused.

To stay aligned with human-centered leadership, organizations must ensure AI is:

Transparent

Leaders should explain AI decisions and ensure systems are auditable.

Ethical

Bias reviews, governance structures, and responsible deployment are essential.

Supportive, not supervisory

AI should not be used to:

micromanage
monitor employees excessively
track every movement
punish mistakes

This would directly contradict servant leadership principles.

Human-first

AI should augment human roles — not replace meaningful interaction, empathy, or connection.

With these guardrails, AI becomes an ally to servant leadership, not a threat.

8. AI + Servant Leadership: A New Path Forward

AI is often positioned as a technical revolution — but it is equally a human one.
It is reshaping how we work, communicate, learn, and grow.

When paired with a servant leadership philosophy, AI unlocks extraordinary possibilities:

Leaders understand their people more deeply
Teams feel more supported and empowered
Decisions become fairer and more inclusive
Work cultures become more humane
Customers receive better service
Organizations grow with integrity

Ultimately, AI enables leaders to live out the true spirit of servant leadership:
to elevate others, remove obstacles, and create an environment where every person can flourish.

Conclusion: AI Is a Powerful Enabler of Human-Centered Leadership

AI cannot replace leadership, but it can absolutely strengthen it.

By freeing time, reducing bias, improving communication, supporting wellbeing, and enhancing decision-making, AI becomes a valuable partner to the servant leader.

Servant leadership + AI = a future where technology amplifies humanity, not diminishes it.

For leaders committed to serving others, the goal is simple:
Use AI not to control — but to empower.
Not to replace — but to elevate.
Not to dominate — but to serve.

So, you can see the impactful results this approach could have on your organization.

You reduce complexed processes in large organization and their burden on employees; you speed communication and focus on what matters to serve customers and people with high standards and a human first approach.

Innovation

The 4 foundational principles to build successful AI projects

Why Most AI Projects Fail Before They Begin

Despite the introduction and the wonderful advancements of artificial intelligence, only a fraction of AI projects actually deliver measurable business impact.

According to Gartner (2024), nearly 65% of enterprise AI initiatives never progress beyond the pilot phase, and fewer than 20% achieve sustained ROI.

The issue is not in the technology, it’s in the project’s intention and approach.

Too many organizations rush into AI without a laser-focused framework, a clear business problem to solve, or a governance model that aligns technical experimentation with business strategy.

Starting an AI project successfully requires more than a product owner, data scientists and algorithms. It requires a structured method that balances business relevance, data readiness, ethical governance, and iterative learning.

In this article, you’ll find a step-by-step roadmap to help you build AI projects that deliver real, transformative impact.

Step 1: Define the Right Business Problem

The most common mistake in corporate AI initiatives is starting with technology first instead of taking the time to draft a real strategy.
Leaders often ask, “How can we use AI?” instead of “What business problem can AI solve for us?”

AI projects should start with a measurable, high-impact problem statement, it shouldn’t be based on vague innovation ambitions.
In research across 200+ enterprise AI projects (MIT Sloan, 2023), successful teams focused only on problems that had:

Clear business KPIs (e.g., reducing claims processing time, optimizing customer service, improving demand forecasting accuracy);
Accessible, high-quality data;
Cross-functional teams collaborating hand in hand towards a common goal (business and tech leaders aligned).

Example:
Coca-Cola’s AI-driven demand forecasting system began with a simple objective: optimize inventory levels per region to reduce waste. By focusing on a tangible business metric, the project yielded a 12% reduction in logistics costs and scaled globally within 18 months.
How did they do it?
-The AI model analyzed real-time inventory and purchasing patterns from intelligent vending machines. During the pilot phase, the company saw a 15% increase in sales and an 18% reduction in restocking visits.
-By combining historical sales data with temperature trends, the system learned to anticipate spikes in demand during heat waves in specific regions, ensuring products were always available when and where customers needed them.
-The model also monitored upcoming events and evaluated their potential impact on sales. These insights enabled more precise product allocation across regions and time periods—reducing both stockouts and excess inventory.

With so many nuances hidden within a single business objective, it’s crucial to bring together all the right data, parameters, and stakeholders ensuring every angle is covered for optimal functionality.

Laying this business foundation ensures the model is built on solid ground, allowing for reliable testing, refinement, and seamless scaling throughout the organization.

Key Question:
What specific decision or process can we improved by X% in order to create measurable value for our customers?
Anything that delivers meaningful value to customers will ultimately drive top-line growth, while process improvements will, in turn, enhance the bottom line.

Step 2: Assess Data Readiness

Data is the foundation of AI, but not all data is usable, ethical, or valuable.
In fact, data issues are responsible for 80% of AI project delays (McKinsey, 2024).

Before modeling or selecting algorithms, companies must first and foremost evaluate their data maturity across five dimensions:

Accessibility: Is the data centralized and retrievable across business units?
Quality: Are there duplicates, inconsistencies, or missing values?
Relevance: Does the data represent the context of the business problem?
Compliance: Does it respect privacy regulations (GDPR, Law 25, HIPAA)?
Bias: Are there demographic or systemic biases embedded in the data?

Example:
Siemens spent months consolidating and cleaning IoT sensor data, machine logs, and maintenance reports across factories into a structured data lake.
When the predictive models were built using this clean, labeled data it was able to reduce equipment downtime by up to 50%, improve operational efficiency by up to 55%, and extend machine lifecycles.

To organize data storage, availability and accuracy is a project on its own, before even starting the AI project itself.

Neglecting this step will lead to false results, require more manual work and cause more mistakes and delays for your company, which will translate into costly implementations and inefficient use of your workforce.

This is where a hybrid approach can come into hand, starting with a waterfall methodology to ensure the right infrastructure, systems and governance are in place, then using an agile framework to develop and iterate the AI model.

Lesson:
Before initiating any AI project, make sure your systems are strong and invest first in data governance, stewardship and cybersecurity.
It takes time to restructure and organize a company’s data infrastructure, and test for accuracy or biases, but this investment will pay great dividends later.

Many organizations attempt to jump straight into deploying technology to accelerate results, but skipping these foundational steps almost always leads to failed projects.

Like everything in life, when you do the right thing, the right thing will come to you.

Just as a house built on weak foundations will fall; building an AI project without the right infrastructure will fail.

Step 3: Embed Governance and Ethics from Day One

AI governance is about creating the guardrails that let organizations innovate with AI safely, ethically, and effectively ensuring that technology serves people and the business, not the other way around.
AI governance is not a post-project checklist it’s an enabler of success.
Without governance, bias creeps in.

Effective governance frameworks include:

AI Steering Committees with executive oversight;
-Define who owns AI decisions (e.g., governance board, ethics committee).
-Align AI use with business strategy and risk management frameworks.
-Manage data lineage (where it comes from and how it’s processed).
Ethical and bias audits integrated into each development stage;
-Prevent bias and discrimination in models.
-Protect privacy and ensure fairness, transparency, and human oversight.
Model documentation (Model Cards / AI Registers) for transparency;
-Establish protocols for documentation, versioning, and explainability.
-Require validation, testing, and reproducibility before deployment.
Continuous monitoring & Lifecycle Management for model drift and performance degradation.
Compliance & Security
-Adhere to laws such as GDPR, Bill C-27 (Canada), or the EU AI Act.
-Maintain cybersecurity, access control, and audit trails.

Example:
BNP Paribas implemented ethics-based auditing systems and frameworks that reduced compliance risks and accelerated deployment by clarifying responsibilities across teams.

A dedicated AI Governance Team: The bank has an internal governance team and developed a model risk governance framework to manage AI deployment and compliance.
Bias Identification and Prevention: BNP Paribas minimizes social and cultural bias by requiring data scientists to complete technical training on detecting and correcting bias in AI models, and by offering all employees an e-learning course on AI ethics and awareness.
Internal Auditing and Tracing: They have built an in-house Model Management System that allows for the tracing and validation of models and data, enabling auditors to “rewind” a model to the exact code and data used previously for debugging and compliance checks.
Data Charter: A Data Platform User Charter commits all data scientists to comply with the highest data security and ethical standards.

Data Point:
Organizations with formal AI governance frameworks are 3.5x more likely to achieve measurable outcomes (Gartner, 2024).

Step 4: Build the Right Multidisciplinary Team

AI projects fail when they’re siloed with either too technical of teams or too strategic.
The most effective organizations build hybrid teams that combine domain expertise, data science, IT infrastructure, and ethical oversight.

A typical successful AI project team includes the following stakeholders:

Business Lead: Owns the problem, defines KPIs, and ensures alignment.
Data Analyst & Data Quality Specialist: Ensures the data is clean and organized to prepare the ground for building the model
Data Scientist / ML Engineer: Designs and trains the model.
Data Engineer: Ensures data pipelines are reliable and scalable.
Ethics / Compliance Officer: Oversees responsible AI practices.
Change Management / Communication Lead: Drives adoption and user trust.

This team’s success largely stems from the project manager or product owner’s ability to maintain clear communication and foster ongoing collaboration.

Example:
Mayo Clinic developed an AI model for medical diagnosis.
The team was composed of:

Data scientists and machine learning engineers to develop models
Clinicians and radiologists to validate outputs and ensure medical accuracy
UX designers and software engineers to build clinician-friendly interfaces
Ethicists and compliance experts to oversee patient data use (HIPAA, PHIPA)
Results:
Improved accuracy and speed of cancer and cardiac disease detection
Enhanced clinician trust and adoption through human–AI collaboration
Lesson: Clinical expertise was crucial for labeling data correctly and ensuring the AI recommendations were usable in real workflows.

Insight:
Diversity of expertise creates resilience. Governance is not a separate layer it’s embedded in the team’s DNA.

Conclusion: AI Projects Start with Strategy, Not Code

The most successful AI projects share a simple truth: they are strategically framed, ethically guided, and operationally disciplined.
Technology alone doesn’t create value, alignment does.

Before you start an AI project, ask:

Is the business problem clearly defined?
Is the data ready and governed?
Is the team multidisciplinary and accountable?
Is governance embedded from day one?

AI should ultimately be approached as a strategic, evidence-based evolution.
It is a transformation journey rather than an experimental project based on current trends and headlines.

Following this roadmap will successfully lay the foundation and support the start of any of your AI projects!

Omnichannel

10 steps to plan successful business automation initiatives

In a world marked with constant economic uncertainty and changes, many people scare away from automation simply by fear of the unknown, initial capital investment, and the worry of changing their organization’s culture.
Many also believe this could lead the way to turning every position into a robot or computer. The reality is that automation is here to make employees’ lives better and alleviate their workflow so they can utilize this time towards finding new ways to innovate, ultimately leading to business growth.
This means less time entering and looking for information, writing reports, and much more.
The mundane activities that every employee dread because it usually keeps them away from doing what really matters in their role.

The benefit of automation lies in the execution of a task at a much higher speed. In addition to its velocity, its performance does not fluctuate based on a human body’s temperament or glucose levels, that could lead any employee to experience oversight.
Miscalculations or errors, when passed from one person to another, or worst from one department to another, can become very challenging to control in a matrixed environment. They can even become deadly in manual laboured environments.
In large organizations with worldwide operations, it becomes difficult for executives to manage their business unit at a micro level; instead, many functional managers lead different groups. Each team working to the best of their ability, given their department’s constraints.
However, if miscommunication happens or mistakes are made, then carried over to other departments, the result seen often leads to: continue business as usual.
From a bigger picture, the reality is: employees work longer hours to make things work. They constantly put out fires derived from an accumulation of mistakes, often losing sight of the real problem.

What automation brings is peace of mind. Information is entered as accurately as possible and completed at a much faster rate. Data can communicate in real time and be updated in no time.
If needed, this information can automatically be added to a business intelligence platform, helping leaders make even better business decisions.
The accuracy and transparency of information ultimately serves everyone involved in the process.

A significant approach in preparing the workforce to this inevitable shift, particularly for the roles that will be most affected by automation, is to support their development by teaching complimentary skills that will go hand in hand with these new tools.
A great effort in communication and support will be important in that transition to achieve higher results.

Automation can be utilized at many different touch points of a business:
Buying, demand planning & supply chain management.
Inventory, manufacturing, logistics, sales recommendations, CRM, Marketing, Payment processing, management, scheduling, workflow tools and much more.

If automation is being considered in an organization, its implementation needs to be well thought out.

Here are 10 steps to plan and adopt successful automation initiatives:

Conduct root-cause analysis underlying your top business issues (internal or external)
Start with the most repetitive tasks. Look for functions that are the most susceptible to errors with the highest business impact.
Research the best solution based on your business objectives, capacity and budget (SaaS, sensors, IoT, Ai, ML, etc)
If you have a strong tech team, consider doing a Build vs. Buy analysis
Strengthen the business process with accurate order and information. This step improves the quality of your data in order to avoid deploying from a broken workflow.
Set up a program for the roles most impacted by automation with employee training and development.
Launch company-wide or department-wide communication explaining the steps towards automation, its impact on daily activities and business growth.
Initiate Integration and Implementation
Conduct Quality Control, Tests & anticipate potential improvements
Sustain the process and reap the rewards!

It is essential to keep in mind that these steps are iterative and need to be examined regularly to truly benefit from their by-product.

The challenge is in proving that automation tools, machine learning and AI can coexist with human capital. That such a coalition can deliver excellence in products or services, in order to better serve customers and reach ultimate business competitiveness.
In parallel, it is equally important to learn how to balance automation tools and ML/AI objectives with the critical human touch needed when delivering exceptional customer service and staying in tune with employees’ well-being.

Based on market observations, tech companies are hiring at speeding pace in a large array of roles. In that sense, there is no reason why all business categories cannot benefit from the same type of performance and growth whether they have a more traditional business approach or were built on a digital-first mindset.

In the next few years, we will witness how companies will embrace these new technologies in order to adapt to their ever-evolving markets.
There will also be an interesting learning curve when it comes to balancing technology driven initiatives and human capital.

At the end, success will reside at that perfectly calculated intersection between human and machine, while never forgetting the purpose each embody for the development of a better society. Ethics, transparency, productivity, better products and services should be the end goal; enabling a transformation that will naturally lead to profitability.

Customer Experience

3 approaches to employee engagement and business innovation

Employee retention and turnover rates have been a heating topic over the last few years. Countless metrics have proven the high correlation between employee engagement and a company’s growing profits.
Unfortunately, these types of discussions are not consistently put forward in business strategy meetings or upon setting up KPIs.

Every large organization recognizes the importance of human capital by investing heavily into benefits, review processes and internal programs or committees, in order to take care of their employees. However, recent data shows that these approaches no longer seem to be sufficient for employees to stay.

Instead, organizations should address Employee Experience the same way they tackle Customer experience. It should constantly adapt with time and be personalized to stand apart.

The average employee’s tenure in a company is estimated to be at 4.1 years. This number is lower amongst the Millennial and Gen Z generations, averaging at 2 years.
It ordinarily takes 3 to 6 months for an employee to get acclimated to a company’s processes and culture, to fit in with colleagues and become more comfortable with the day-to-day work. Within this time, if the employee has not been taken care of and received proper onboarding experience; they are likely already thinking of changing jobs.

We also need to acknowledge the weeks or months required to review and interview candidates. Valuable time taken away from managers that could focus this energy on advancing the business. Adding to this, the lengthy processes HR go through at the beginning of each hiring process.

With this in mind, we have studied 3 simple approaches that could help your organization capture your employees’ attention and see productivity rise:

Communication

Communication starts at the interview stage.
It is not uncommon that HR and hiring managers tend to upsell a position during the interview process, resulting in altered expectations once a new hire embarks in their new role.
This tends to create confusion when it is time to apply themselves to the actual job requirements.
Providing transparency about the actual realities of the job at the interview stage, can help the candidate evaluate if the position aligns with their personal and professional goals.
If it is, that candidate has more chances of being involved and engaged in his/her daily responsibilities, being fully aware of what they signed up for.

On the opposite side, it is equally essential for the hiring manager to recognize a candidate’s personal goals, through verbal and non-verbal cues. A hire that does not have a passion or objective that aligns with a position or company’s mission can only hurt the team’s productivity, morale and ultimately the business.
On some occasions, candidates have not done the work to properly envision their personal goals before interviewing for a job; therefore, it is intrinsically important for a hiring manager to evaluate what is best for both, through active listening and empathy.

Once hired, many employees are not taken through what is expected of them, what their role encompasses and where they fit into the organization. We often see new hires wondering and figuring out what they will be required to do, until they make a mistake that will teach them about their limitations. These mistakes can easily be avoided if the right support is put in place with an effective onboarding process.

It is important to understand that every individual communicates differently. Detailed communication is the key to avoiding misunderstandings.
A non-judgemental stage for open dialogue is critical for encouraging more transparent communication that will get to the root cause of problems and avoid confusion or misinterpretation.
This is where personalization comes into play. A general way of communicating to all employees does not necessarily lead to better outcomes. Companies should consider altering their communication strategy to adapt to the different personas that englobe their diverse workforce.

Collaboration

At the heart of every start-up, lies collaboration. It often derives from limited resources, where roles are less defined, and employees are involved in multiple areas of the business. It is also cultivated through an agile mindset engrained in their methodology, to deliver instant value and inspire innovation.
As organizations grow, roles become more distinct and siloed in order to fulfill certain tasks. With this evolution, teamwork and collaboration naturally suffers. A line of command is formed, where results are based on the execution of top management strategies, taken without the input and expertise of those involved in the implementation process.
A best practice for maintaining collaboration, as organizations grow, is to trust and empower employees by giving them the tools and information needed to make their own decisions as a team.
Transparency of expectations, availability of data combined with the right tools and support can only nurture great results. It gives employees the confidence needed to develop their skills, encourage collaboration and evaluate winning strategies to reach higher performance in their department.

Leaders and managers also need the appropriate training and support to instill this collaborative ethos in the company’s culture, giving them the ability to provide strong direction to their teams.
This type of organizational transformation often leads to increased opportunities for innovation in any business field.

Coaching

Coaching, mentorship and shadowing are crucial elements in the employee experience. When done consistently and not sporadically, it can bare great fruits and develop the talent and skills of many employees, leading to great personal growth.
Providing guidance and support, pushing an individual’s capabilities, listening without judgment, recognizing effort and good work, and empathy are only part of the skill sets managers should aim to master, to inspire their teams to thrive beyond their department.

As we continue to explore more about Diversity and Inclusion, it is important to understand that inclusion is not limited to recognizing an employee’s background and racial descendance.
It is equally significant to practice inclusion during decision processes. This feeling of inclusion will encourage employees to be increasingly involved in the company, rather than acting as outsiders awaiting fort their list of orders.
When employees feel more included, they feel more valued.
When they feel more valued, they are more confident in their potential.
When they feel confident, their performance increases.
As a result, they naturally commit to the company’s vision and goals with the desire of seeing their company prosper.

Personal development is at the core of every employee’s goal.
When a company recognizes and supports each employee in their development stage, they are tapping into a limitless pool of opportunities for engagement, performance and ultimately profit.

To conclude, it is important to see a workforce as a group of diverse and unique individuals. To treat them all the same would be to devalue their assets, skills and talent. When doing so, companies only tap into a small percentage of their workforce’s actual potential.

At the root of every human being is the necessity to contribute and bring value to the world. If you take that away from them, you leave employees with poor mental health and indifference, which can ultimately hurt a business.

The same way personalization is a roaring trend in customer experience, so should employee experience and engagement be given the same care and approach.

At 8visio, we created a set of tools and techniques to monitor and cultivate successful employee experiences in order to help your company reach higher productivity rates and set the right stage for continuous business innovation.

Customer Experience

Are you ready to adopt new technologies, personalization and…

About 100 years ago, a great second Industrial revolution came along with the rural exodus and the uprising of major city centers.
It set the groundwork for technologies that led us to live faster paced lives, filled with new products, travel experiences and advanced communication.
It made people’s lives better, easier and more comfortable; which enhanced with the digital offering of the latter years.
Since then, we have continued to rely on these same technologies to support our modern needs, despite the span of several generational changes.

We will look at the fundamental approach needed to adapt to the next industrial revolution, or as we prefer to call it, the knowledge era, that will enable any company to stay relevant and competitive for the years to come.

As we enter this new era, we understand that information, data science and robotic systems are some of the key elements that will lead us into the next generation.
With that in mind, we experienced a hint of what’s to come in the recent race towards personalized systems and solutions in e-commerce, marketing, customer service and logistics – through advanced analytics, machine learning and AI.

It is well known that 74% of modern customers will abandon a buying journey and even their brand loyalty if they received poor service or were disappointed by an experience (Business Wire).
Relatively, close to 50% of US customers state that brands don’t meet their expectations (Acquia).

It will be difficult for any company to provide personalized experiences to their customers when operating in legacy systems, silos and decentralized data visibility.
These management systems came as an advantage in the industrial era but will slowly become obsolete in this new technological revolution.
Customers can see through these uncoordinated efforts and can sense a certain uneasiness in their experience.

In order to become truly competitive in personalized approaches and to deepen relationships with customers; companies need to start personalizing their own backend and operations processes first, and become wary of the mass produced, one size fits all solutions available on the market. While they can seem attractive at first, they can represent inevitable threats to a business, in its effort to adapt to new technologies, consumer trends and behaviors.

In a world where we are increasingly celebrating diversity and uniqueness of voice, it is equally important to celebrate these same values in every aspect of a business, down to the operative and clerical processes.
In personifying theses values through customized operating processes and managerial approaches; businesses from global brands, to cultural industries and small businesses could witness substantial returns.

Distinctive values that can transcend in every aspect of the company, at the client facing or branding level but also at the HR, operations, manufacturing, and supply chain levels, that will result in perfectly coordinated and orchestrated products and services.

At 8visio, we believe that every company is different, and therefore needs to operate with different processes and customer approaches in order to stay competitive in their respective industry.
We understand that, at times, it can be difficult to navigate the unknown possibilities of new technologies and we are here to offer you the support, knowledge and strategies needed to help your company rise through the ranks of the unforgettable pioneers of this generation.

Omnichannel

What is your definition of Omnichannel experiences?

Many individual and professionals share different views about Omnichannel.
The word first appeared around 2010 and has gained much traction over the past few years in both business and marketing.

Expert marketers were the first at understanding its fundamental benefits by delivering a unified message with personalized communication that was representative of the brand to their different customers.

What’s next in omnichannel experiences?

There is no question that customers are increasingly expecting seamless experiences. They wish to feel unique, remembered and surprised by the companies they decide to invest their hard earned money into.

In more recent context, they also demand for cleaner and safer environments, with new ways to pay and receive their orders.

Nobody enjoys wasting 15 minutes of their day behind cash registers or ticketing lines, or search for help from a representative to not get the right advice, or even call customer care about a mischarge only to hear that they can’t do anything about it after a 30 minute wait on the phone.

In an increasingly fast paced lifestyle customers also understand that “Time is money”.

With this shift in customers’ lifestyles and expectations, companies are more and more aware that they need to rethink the way they operate, and a few understand that omnichannel is the way.

It carries an end result that is consistent and impactful by merging physical, online, mobile, social, and customer support efforts.

Investing in Omnichannel might mean short-term discomfort in culture change, standard operational processes and staff training but it reaps great rewards on the medium to long-term goals of any business.

Its success depends on the right customer-centric strategies with a focus on behavioral changes. By truly understanding your clients and keeping a continual dialogue, you will be guided towards the right touch points to change, so that they can keep coming back for more:

personalization

seamless checkouts

availability to expert advice

unified multichannel experience

are only some of the important preliminary omnichannel strategies to invest in that can lead to great revenue growth and customer retention.

Companies only need to decide at which level they are willing to implement it:

At the marketing level
Achieve consistent Communication across multi marketing channels (social, email, sms, etc.)

At the commerce level
Centralize online, mobile and offline services to offer a homogeneous support and message to customers shopping in a multichannel way.

At the operational level
Integrate different internal tools and systems capable of unifying the traditionally siloed data into a single customer view.

At the business model level
Reimagine the way the company has been conducting its business and push boundaries by surprising clients with sensorial, entertaining and state of the art personalization and services at every touch point of the shopping journey.

One thing is clear, from a business perspective omnichannel is still at its infancy and will keep evolving with time by adapting to customers behaviours and new market trends.