Change is inevitable, and is full of exiting opportunities if managed well. The disruptive pace of change in the global landscape of digital services requires organizations to anticipate and keep pace to assure organizational readiness to handle change, whether evolutionary or revolutionary.

Lintu’s experienced change managers have lived the change cycles in a variety of environments. We’ve learned how to develop an enthusiasm for change that not only increases an organization’s tolerance for change, but actually helps them thrive on change and grow the appetite for improvements. Using the ADKAR model has proven valuable across industry segments.

Lintu solutions goes far beyond helping you manage individual changes. We help organizations develop a healthy culture of embracing change and even desiring the realization of positive outcomes change makes possible. An organization that embraces and fosters change is ready for whatever lays ahead.

Imagine what you could do if change was seen by your workforce as a desirable aspect of your organizational culture.

Inherent in optimization of your service workflow is process transformation. In the context of client experiences, it is effective service delivery built on effective underlying processes that consistently achieves outcomes that exceed client expectations and eliminates re-work.

Organizations develop processes and practices over time as their service models evolve. However as inputs and outputs, requirements, tools, and opportunities for improvement change, so must the functional processes change. Some of these factors are regulatory or other external pressures to change and some are identified opportunities for streamlining and potentially automating, or a need to increase quality and reliability.

Lintu solutions takes a systemic integrated approach to process analysis, looking at the existing processes and evaluating each aspect of the process including the human factors, digital touchpoints, effectiveness metrics, and input/output content and quality. Often process output quality improvements and exception reductions can be achieved with incremental technical controls which include stored procedures and data validation implemented at the human-digital touchpoints.

Looking at larger opportunities such as robotic process automation or new overall digitalization efforts can completely transform a process to leverage transactional effectiveness for reliable repetition. These can greatly reduce manual interactions for reduced effort, increased accuracy, and overall efficiency. Whatever the drivers for transformation

Whether enticing new clients or serving current ones, delivering a world-class experience sets the tone for all interactions. Lintu engineers persistently monitor human-centric trends in financial and government realms to drive product development and modifications for client engagement.

Our methodology commences by defining usability standards based on client business services, performing usability testing sessions, and collecting survey data to funnel in most used interactions. We iteratively fine-tune customer interaction usability and feature enhancements to enable ease of customer experience. Human Centered Design is at the core of the methodology with access to the world’s best human factors research labs where application is warranted.

Artificial Intelligence driven Live Assistance technology harnesses automation in identifying human interaction patterns, user types, most accessed inquiries, demographic driven information access patterns, most used device profiles, and survey trends derived by using AI processes on real users.

Lintu Solutions’ Live Assistance technologies bring a multi-faceted approach where we merge the highly effective user trends to compile live assistance content. Our customer support model transformation approach spans the entire customer relationship driving client value:

Our core focus is not only the highest levels of client satisfaction while securing identities of our customer interactions, but also staying transparent throughout the workflow to enable a sense of trust and reliability in the Live Assistance program.

Machine Learning is the process of training a piece of software to make useful predictions using a data set. There are several models of Machine learning that can be utilized based on the data types and level of interaction of humans applied (supervision). Lintu specialists can assist with the development of strategies for different learning models appropriate for the chosen data sample types to ensure the model chosen will achieve the desired machine learning outcomes:

Live Assistance Integration: Lintu Solutions’ Machine Learning methodology for our Live Assistance client experience solutions leverages a version of Supervised Reinforcement Learning with concepts including using a natural language engine for categorizing user inputs to predict types of requests needed, then identify and detect response inputs to formulate automated responses, which are reinforced by supervised reviews.

We recognize and correlate previously unidentified human interactions with various digital customer support technologies to form specific use cases and eventually drive our iterative enhancements of Live Assistance.

In support of our Live Assistance capability, layered upon and supported by Reinforced machine learning, we also incorporate role-based Dynamic Adaptation to the client. Live Assistance responses are tailored by additional intelligence about the user’s profile and service subscriptions.

Our Adaptation methodology uses stored profile data about the client to guide the responses of the Assistant to assure accuracy and relevance to the automated responses. This predictive tailoring assures the highest value responses are provided the client based on what is most relevant to them.

To continually improve our Live Assistance accuracy, relevance, and value delivery, we apply a maturity model approach that derives optimization actions from data intelligence. This approach is based on our recent direct experience guiding an organization to achieving CMMI level 5 for Services.

Leveraging client-focused experience, activity and feedback metrics, our processes are continually improved based on quantitative analysis of the common causes of variation inherent in the Live Assistance supporting processes of Machine Learning and Dynamic Adaptation.

Lintu’s overall process improvement approach focuses on continually improving process performance through both incremental and innovative technological improvements. Our quantitative process-improvement objectives for client organization are collaboratively defined and continually revised to reflect changing business objectives, and used as input criteria in managing process improvement activities.