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AI hysteria

To AI or not to AI, is there really a choice? Your organsiation has most likely already adopted AI in various forms from off the shelf 3rd party tools to inhouse applications. Yet, in the past 12 months particularly with the mass rollout of generative AI, artificial intelligence (AI) stands out as a transformative force and organisations are being forced to address AI face on, to determine how it fits into its business landscape.


From automating routine tasks to solving complex problems, AI's potential is immense. However, harnessing this potential requires more than just the technological know-how; it demands a strategic approach. We summarise how businesses and organisations can develop an effective AI strategy to unlock new opportunities and drive innovation. This short guide will also help to break through the hysteria and overwhelm.


Understanding AI and Its Potential


AI Basics

At its core, AI involves creating systems capable of performing tasks that typically require human intelligence. In a nutshell. This includes machine learning (ML), where algorithms learn from data, and deep learning, a subset of ML based on artificial neural networks. Natural Language Processing (NLP) allows computers to understand and respond to human language, further expanding AI's capabilities.


Industry Applications

The applications of AI are as diverse as the industries it permeates. In healthcare, AI assists in diagnostic processes and personalised medicine. In finance, it's used for fraud detection and risk management. Retail businesses leverage AI for personalised customer experiences and inventory management. These examples barely scratch the surface of AI's expansive utility.


Setting Clear Objectives in Crafting an AI Strategy


Identifying Goals

An AI strategy must align with your organisation's overall objectives. Let's reflect on that. AI implementation projects or programs that are not aligned with core and prioritised business objectives will have less chance of success. Whether it's enhancing customer experience, streamlining operations, or driving innovation, your AI goals should directly support your broader business targets with robust business cases to focus on the purpose and benefits. While it might be tempting to view AI as the proverbial tail wagging the dog, business leaders need to be reminded that they are in the driving seat.


Problem-Solving with AI

AI is a tool for solving problems, not an end in itself. Identify specific challenges within your organisation where AI can provide a solution. This focused approach ensures your AI initiatives deliver tangible value.


Assessing Organisational Readiness


Resource Evaluation

Do you have the necessary data, talent, and infrastructure to support AI initiatives? Assessing your current resources is a critical first step. Without quality data, even the most advanced AI algorithms cannot function effectively.


Skill Gap Analysis

Having the right team is crucial. Determine if your current staff has the necessary skills or if you need to recruit AI experts. Sometimes, partnering with external consultants, partners and vendors is the best way to bridge this gap.


Building a Data Strategy


Data Acquisition and Management

AI feeds on data. Collecting high-quality, relevant data and managing it effectively is fundamental. Establish robust data acquisition and management practices to ensure your AI systems have the fuel they need to succeed.


Data Governance and Ethics

With great data comes great responsibility. Establish clear policies for data governance, focusing on privacy, security, and ethical use. Compliance with regulations like GDPR is not just mandatory but also builds trust with your stakeholders.


Choosing the Right Technologies and Partners


Technology Selection

Selecting the right AI tools and platforms is a balancing act between current capabilities and future scalability. Whether it’s cloud-based AI services or in-house development, your technology choices should align with your long-term strategy.


Partnering Wisely

Sometimes, the best resources lie outside your organisation. Forming strategic partnerships with AI vendors and service providers can accelerate your AI journey. Choose partners whose capabilities complement your own and whose values align with your business ethos.


Implementation and Integration


Pilot Projects

Start small. Pilot projects allow you to test the waters with minimal risk. They provide valuable insights and learnings that can guide larger-scale implementations.


Integration Challenges

Integrating AI into existing systems can be challenging. Ensure your IT infrastructure is adaptable and that there is a clear plan for how AI will fit into existing workflows and processes.


Monitoring and Scaling


Performance Metrics

Define clear metrics to measure the success of your AI initiatives. These could range from cost savings and revenue growth to customer satisfaction and employee engagement.


Scaling AI

Once your pilot projects prove successful, it's time to scale. Expanding AI initiatives requires not just technological readiness but also an organisational culture that embraces change and innovation.


Fostering an AI Culture


Continuous Learning and Adaptation

AI is a rapidly evolving field. Encourage a culture of continuous learning and experimentation within your organisation to keep up with the latest developments in AI.


Change Management

Adopting AI often requires significant changes in processes and workflows. Effective change management is essential to ensure a smooth transition and to get your entire team on board with the new AI-driven approach.


Implementing an AI strategy is a journey, not a destination. It requires careful planning, the right resources, and a culture that embraces change and innovation. By following these guidelines, organisations can unlock the full potential of AI, driving innovation and gaining a competitive edge in the digital era.


Stay tuned for more insights on AI strategy in our upcoming posts. Ready to transform your business with AI? Don’t navigate this journey alone. Book a free consultation call  with our experts today and take the first step towards a smarter, AI-driven future for your business.




Envisago Consulting - Be Future Ready

CX. Operations. Technology. Change Management.


First Contact Resolution

In the customer support landscape, First Contact Resolution (FCR) stands as a crucial metric, offering a window into the effectiveness and efficiency of a service or support team. However, its measurement is anything but straightforward. We aim to unpack the intricate nature of FCR, shedding light on why it's challenging to measure and proposing robust strategies to navigate these challenges.

Understanding the Elusive Nature of FCR Measurement


First Contact Resolution, at its core, seeks to evaluate whether a customer's query or issue is resolved in the initial interaction. But this seemingly simple concept is mired in complexities. The primary challenge lies in the subjective nature of what 'resolution' means. For some customers, a quick answer might suffice, while others may seek a more thorough solution or follow-up. This variance makes setting a universal benchmark for resolution a tricky affair.


Adding to this complexity is the modern customer journey, which often spans across multiple channels. A query might commence as an email, morph into a phone conversation, and conclude through a chat service. Keeping track of these interactions, and more importantly, deciphering when and if a resolution occurs, requires sophisticated tracking and analysis.


Another critical aspect often overlooked is the timeframe within which a repeat contact from the customer is considered. Does a lack of follow-up within a week signify a resolved issue, or should the window be extended to a fortnight? This timeframe is a vital cog in the FCR measurement wheel, one that needs careful calibration to ensure accuracy.

Strategies for Effective FCR Measurement


To navigate these challenges, we suggest several strategies:

  1. Defining Resolution with Clarity: It's essential to establish a clear, business appropriate definition of resolution. This could range from a customer's verbal confirmation to specific criteria that the support team must fulfill. Response does not mean resolution so ensure there is clarity in the definition.

  2. Embracing Technology: Utilising advanced CRM systems can significantly streamline the tracking of multi-channel interactions. These systems should be adept at not just recording interactions but also intelligently linking related contacts to paint a complete picture of the customer’s journey. Status of customer interactions i.e. open/closed may directly feed into your metrics so make sure the fields you want to extract in reporting are accurate, and make sense to track.

  3. Setting a Realistic Timeframe: Deciding on an appropriate timeframe for resolution is a delicate balance. For example if the customer calls back in 24 hours, 7 days, or 20 days how is the customer contact treated and what constitutes a fail? Defining the right timeframe involves understanding the norms for your industry and the type of query analysing historical data, and considering the nature of the service or product. This timeframe should be regularly reviewed and adjusted as needed.

  4. Leveraging Direct Customer Feedback: Post-interaction surveys can be an invaluable tool. They provide immediate and direct feedback from customers on whether their issue was resolved, offering a tangible metric to gauge FCR.

  5. Ongoing Training and Feedback for Teams: Ensuring that the customer support team is not just aware of, but also aligned with the FCR goals is crucial. Regular training sessions, coupled with feedback mechanisms, can foster a culture that prioritises effective resolution.

  6. Analysing Trends for Continuous Improvement: Regularly analysing customer interactions for patterns and trends can uncover insights into common issues that are not being resolved in the first interaction. This ongoing analysis is key to continually refining the FCR measurement process.


Measuring First Contact Resolution, while challenging, is far from an insurmountable task. By understanding its multifaceted nature and adopting a strategic approach, organisations can effectively measure and enhance their FCR rates. This not only leads to improved customer satisfaction but also streamlines service operations, making them more efficient and responsive. Ultimately, the journey towards mastering FCR measurement is a continuous one, demanding regular evaluation and adaptation to meet evolving customer needs and expectations.


So are you ready to master the art of First Contact Resolution? Reach out to us for a free consultation. Don't miss this opportunity to elevate your customer support standards. Make every customer interaction count!



Envisago Consulting - Be Future Ready

CX. Operations. Technology. Change Management.





network design

Integrating Artificial Intelligence (AI) into business operations is a tangible strategy that can drive significant improvements in efficiency, decision-making, and customer satisfaction. However, the success of AI implementations heavily depends on the underlying network architecture. (It's worth having a chat with your IT team to confirm the robustness of your current network). Here's how strategic network design can unlock AI opportunities in your business operation.


1. Strategic Network Design and The Backbone of AI: Data Flow and Management


The essence of AI lies in its ability to process and analyse vast amounts of data. A well-structured network ensures efficient data handling, which is crucial for AI applications. An optimised network facilitates seamless data flow, storage, and accessibility, serving as the backbone for robust AI systems.


2. Building for the Future: Scalability and Flexibility


As businesses grow, their AI needs evolve. Networks designed for scalability and flexibility allow for the smooth integration of AI solutions, accommodating growing data volumes and changing business requirements without the need for complete overhauls.


3. The Need for Speed: Latency and Real-Time Processing


For AI applications like predictive analytics, real-time data processing is key. Networks optimised for low latency ensure that AI systems can operate efficiently and make decisions in a timely manner, which is critical in fast-paced business environments.


4. Guarding the Gates: Security and Compliance


AI systems often handle sensitive data, making security a top priority. A secure network protects AI systems from cyber threats and ensures compliance with data protection regulations, which is essential in today's data-driven world.


5. Bringing AI to the Edge: Edge Computing


Edge computing in network design allows for data processing closer to the source, enhancing AI applications that require immediate data insights, such as those in IoT devices. This reduces latency and bandwidth usage, making AI systems more efficient.


6. Connecting the Dots: Connectivity and Integration


Effective AI applications often need data from various sources. A well-designed network ensures seamless connectivity between different systems, allowing AI tools to integrate diverse data sets for comprehensive analysis.


7. Powering AI: Resource Allocation and Optimization


AI applications can be resource-intensive. Networks that allocate and optimise resources efficiently ensure that AI systems have the necessary computational power without impacting other operations.


8. Reliability in Adversity: Disaster Recovery and Data Redundancy


AI systems need to be reliable and operational at all times. Networks with robust disaster recovery and data redundancy plans ensure continuous AI operations, even in challenging situations.


9. Embracing the Cloud: Cloud Computing Integration


Many AI solutions leverage cloud computing for enhanced capabilities. A network that integrates seamlessly with cloud services facilitates more powerful and versatile AI applications.


10. Keeping a Watchful Eye: Monitoring and Maintenance


Networks designed with advanced monitoring capabilities enable ongoing assessment of AI system performance, ensuring any issues are swiftly addressed to maintain optimal functionality.


In conclusion, the role of network design in the implementation of AI in business operations is pivotal. A thoughtfully designed network not only supports current AI applications but also sets the stage for future advancements and integrations. As businesses continue to explore AI opportunities, the focus on strategic network design becomes increasingly crucial, acting as a key enabler in the journey towards innovation and growth.


Are you ready to harness the power of AI in your business? Ensuring your network is up to the task is your first step towards a smarter, more efficient future.



Envisago Consulting - Be Future Ready

CX. Operations. Technology. Change Management.

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