What we do

In the Launchpad we undertake the hardest part on every transformation: bring a solution to life. Make it successful by being usable, flexible, and scalable. Help it evolve by accelerating adoption, capabilities, and results. And make it aligned with market needs and business plans.

From proof of concept to working realities; from algorithms to teams exchanging knowledge with a system; from intelligence engines to teams acting upon the system’s recommendations or instructions. We provide the full operating model to make this possible; to incorporate knowledge automation applications and other AI-based technologies into everyday company behaviors: setup, launch, post-launch, and/or operations as needed.

 

Our challenge

For a new solution to work or an innovation to stick, it has to be well understood and owned. This requires new ways of working, interacting, or behaving, both among individuals themselves – teams, units, organizations – and between people and technology – new data, interfaces, systems.

The complexity of the challenge rests therefore more on the behavioral impact than on the technical solution itself: How to make teams “collaborate” with technology instead of just use it. How to ensure teams are benefitting from technology, learning from it, growing their skills.

Or more precisely – to pick one example we work on: how to make the consistent and disciplined feedback on what salespeople do with customers – the results of their interactions – a self-encouraged job? To what extent does the constant measurement of customer interactions enable a new way of doing marketing? Or the way top management engages with field activity? What about the promotion of brand values and the development of brand equity? All of them making up for a wide range of challenges that we like to address holistically.

 

How we work

How we do things here can adopt many forms, but the endgame is always the same: make people work under new paradigms, using new processes, enabling new interactions, and working with technology in different ways. It’s not just the execution of the solution what we pursue but the assurance of its endurance, evolution, and growth. This is because we are not product or service providers; we create custom-made solutions by combining different products and services, adapted to each challenge, and embark on the road towards their maturity.

We do this by running different implementation cycles to ensure knowledge transfer, handover, and adoption, and/or by partnering on a more long-term basis to demonstrate real “skin in the game”, sharing both risks and results accordingly. Whatever the case is, we tie our reward to specific results being achieved.

We act on three levels, depending on how far we go on this type of partnerships: capacity building, Expert as a Service, and AI-driven teams.

 

Capacity building

As these solutions are here to grow capabilities, we need to get to the point where that occurs naturally, so the transition is very important. We are talking about new behaviours and attitudes that don’t need to be there on day 1. We help teams and stakeholders in general – field force (i.e., salespeople, fleet operators, general users), middle managers (i.e., team leaders, commercial managers, etc.), and top managers – travel this path up to the point where the “new normal” is business as usual to them. We empower them to take the responsibility for future improvements and growth.

This requires lots of flexibility, both in skills, as we might start as designers and developers to end up program managing a transformation, and role, as we move from drivers to facilitators within the organization.

 

Expert as a Service

An intrinsic part of any change process we usually get involved in entails a high concentration of specific or expert knowledge.

As with any knowledge-based organization, the aggregation and scale-up of new knowledge is what fuels to a large extent the new type of behaviours and interactions necessary for long-term success. It can significantly benefit the routines of all possible stakeholders across the playing field – countries, business units, teams, individuals – if underpinned by the right processes and ways of working; real-time measurements, targeted action plans, new personalized offers, best-practice sharing, identification of new clients, new products, offers, and delivery of expert tasks in general.

Are we agile enough to build and keep up-to-date knowledge on a given field of relevance, say, last-mile customer service? What about the process to learn and share about new opportunities and discoveries on, say, food & beverages customer needs? How to end with the individual and stand-alone customer knowledge that is usually typical of a sales network without disturbing their sense of ownership?

We build and run ad-hoc teams and organizations that address these challenges by providing on-demand, instant, personally crafted, and scalable knowledge. They can adopt the form of a center of excellence, and expert share service facility, or any type of platform tasked to provide the right engagement through the combined use of data, AI, and technology, in response to a particular business problem.

 

AI-driven teams

Sales agents having at their disposal an automatically generated customer visit plan for the full year, for every customer, updated monthly. Being told what actions to trigger in front of the customer, what case studies to discuss, what products to negotiate. All ready on their tablets so that they can know where to go, when, and what to do, and thus concentrate on the personal engagement. An AI-based engine that identifies undetected patterns on customers and learns from experience what works best for each of them at an individual level, measuring and organizing the daily activity of sales teams accordingly.

An AI of this sort requires of the collaboration from the teams providing feedback from their actions; detailed and accurate accounts of the outcome of a customer interaction, easily captured through interactive forms on their tablets. This is how reinforcement learning is enabled and applied in a real-life and proven interactive environment; an agent learns from the experience based on the feedback of its own actions.

Similar applications might involve different industries, activities, or types of interacting customers, but the common characteristic is that of a distributed human network acting or impacting on different points of interaction: pre-sales or sales teams from a food & beverages brand delivering and placing product in a supermarket or grocery store; virtual procurement agents helping category managers adapt requirements to different products and sellers, and realize cross-category synergies; food security auditors being guided on what and where to inspect and provided with real-time insights on instant remediation actions.

We work to bring to life a human-machine duality based on the collaboration between the two, applied to different environments where each of them concentrates on what they can do best. The human benefits from the machine’s power to grow data and knowledge exponentially; the machine benefits from the human trial and error, field gathering of unstructured data, and its modelling and scale-up to make it more widespread and accepted.

Our brand company BeJarvis provides integrated human-machine solutions as a service, deploying all the intervening components in a turnkey fashion: people, processes, technology, and tools. We do it “as a service” to offer the modularity, flexibility, maturity curve, and scalability that businesses and organizations require.

Visit our website www.bejarvis.com to learn more about what we do in this field and how we do it, along with a number of enticing case stories and examples.