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CHALLENGE

A global ERP provider building a next generation 'Agentic AI' architecture needed to validate their platform strategy. They wanted to understand where their customers were on their AI journey - and where they were heading.

RESPONSE

I led a multi-phase research initiative to inform the client's SaaS platform strategy.

I first conducted in-depth interviews with the client's platform team, applications team, pre-sales team and senior leadership to understand their current AI strategy and assumptions about users. I also worked closely with their CX team to consolidate and synthesize existing customer data.

I next interviewed senior technical leaders across six asset-heavy industries (manufacturing, utilities, telecoms, aerospace, transport and services) to explore how they are deploying AI to improve operations in an industrial context.

I augmented these interviews with desk research on current machine learning, generative AI and agentic AI applications in industrial contexts

In addition to this user research, I worked alongside a futurologist, AI technologist and venture strategist to formulate nine specific agentic AI use cases. Each use case suggested appropriate technologies as well as possible partners.

OUTCOME

Our user insights, strategic recommendations and tangible agentic AI use cases provided the client with data and inspiration to make a number of key decisions about their platform.

In particular, our research pointed out the critical gap in the promise of AI and the realities of implementing it in complex operations. Customers were wary of expensive and unproven technologies and needed more help to experiment with AI in low cost and low risk environments.

CHALLENGE

The goal of the Natural Capital and Ecosystem Assessment programme (NCEA) was to consolidate and extend the range of biodiversity data across England and make this data available to a wide variety of stakeholders. This in turn would enable stakeholders to take a 'natural capital' approach to land management.

The difficulty was steering Defra (Department of Environment, Farming and Rural Affairs), its arms length bodies, NGOs and third party contractors to effectively collaborate on a common data model, SaaS platform and product strategy. A specific risk was the collection of expensive data without a clear understanding of how stakeholders might use it.

RESPONSE

I lead several initiatives across the programme.

I designed and implemented an MVP strategy, working with NCEA portfolio teams, their respective policy customers and data providers to rapidly prototype products e.g. a soil health indicator, and prove that it could be powered by NCEA data. This significantly reduced the risk of collecting data that did not meet customer needs.

I led a cross-functional team to identify requirements for a SaaS platform. This service had to be capable of hosting many data types (earth observation, field surveys, citizen science) and serving dozens of policy teams across government. We investigated digital and data infrastructure across Defra and its many Arms Length Bodies and spoke with a large sample of potential users (data scientists, GIS specialists, ecologists etc). Our recommendations were accepted by the NCEA board and funding was approved to develop the service.

In addition I also developed digital and data strategy for the SaaS service. This included extensive work on data integration. We worked closely with ecologists to develop a conceptual ecological model (based on the DPSIR framework) and then worked with data partners to develop an ontology to manage the heterogeneous NCEA data.

OUTCOME

The net result of my work was a stronger and more resilient programme better suited to meet the challenge of biodiversity loss in England.

Not only had I reduced risk by making sure that data products matched user needs, I also embedded user research techniques and a Lean Startup approach to product development at the NCEA.

CHALLENGE

Shell has a wide range of lubricant customers from steel mills to mining operations who need expert advice on many applications. Shell lubricants engineers both advise customers and identify new sales opportunities. However, they have limited time and little support in the field. Shell wanted to improve the advisory service and increase sales conversions from these site visits.

RESPONSE

I conducted a series of interviews with lubricants engineers in Russia, Europe and North America and mapped out the existing service. I also joined the engineers on site visits in the UK and Russia to better understand the demands of the job. In particular, I focused on their interaction with the customer and how we could facilitate better conversations.

From the research, I developed user journeys and personas. Using this material we were able to zoom in on our MVP and identify the biggest challenge for the engineers. Their main concern was being able to sustain a conversation with the customer while rapidly recording issues they were encountering on site. Our solution had to be very fast and unobtrusive.

I ran ideation sessions with our product team in London, challenging them to really push the designs to the limit and find ways to shave off seconds for each in-app interaction.

I also ran co-creation sessions remotely with engineers in Houston, encouraging them to come up with their own solutions.

OUTCOME

We soon launched our MVP in Russia and Europe and began to test and iterate. I set up regular sessions with the engineers to learn how our app was performing in the field and used this feedback to continuously improve the service.

The app had the desired outcome of raising the quality of the service, reducing time on site and generating more cross sell and upsell opportunities.

CHALLENGE

I worked with a mixed team of consultants from EY-Seren, Sapient Nitro and other agencies to set up this Lloyds innovation lab.

The lab was a concerted effort by Lloyds bank to reduce the number of customers who fall into arrears and help those who are in arrears get back on their feet as soon as possible.

RESPONSE

I worked with SMEs to map out the current collections process and reviewed previous customer interviews and segmentation data. From this, I produced customer journey maps and personas to help the lab understand the customer experience. An important feature of these journey maps and personas was that they showed the customer's relationship with other creditors, not just Lloyds bank.

Next, I facilitated 'five whys' workshops to challenge assumptions around existing policies and processes.

With a better understanding of the customer experience and the existing service design, we moved to our solution design phase.

I helped the feature teams rethink the existing service by sketching out alternative scenarios and visualising interactions in the customer and staff journey.

As we came up with ideas and features, we anchored them to hypotheses. Having experience with Lean Startup, I worked across the lab to help the different feature teams write hypotheses and tests for each of their features.

OUTCOME

With a clear set of hypothesis, I was able to set up light weight prototypes and rapidly test lab ideas with customers. This enabled us to eliminate weaker ideas and create a robust product roadmap for Lloyds bank.