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N. Balakrishnan
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An AI-Enabled Sensemaking Framework for Go-to-Market decision makers
Reducing information overload and improving insight quality for Product Marketing Managers at Webfleet
At Webfleet, a Bridgestone company operating in European commercial-vehicle
telematics, PMMs must turn fragmented inputs from market research, customer
feedback, sales signals, competitor intelligence, and regional sources into coher‐
ent Go-to-Market (GtM) insight. The difficulty is not a lack of information but an
excess of it, and current practice relies heavily on individual judgment that is hard
to scale, audit, or improve.
This thesis investigates how a structured, AI-enabled sensemaking framework can
reduce information overload and improve the quality of GtM insight synthesis for
PMMs at Webfleet. Following a qualitative, design-led approach grounded in de‐
sign science research, the project ran across three phases: a contextual inquiry us‐
ing semi-structured interviews with PMMs and secondary stakeholders, the design
of a framework and AI prompt guide, and a perception-based assessment and trial
use of the guide.
The central design outcome is PRISM(E), a six-element framework: Prioritise,
Reduce, Interpret, Synthesise, Mobilise, and an Equip layer of organisational en‐
ablement that runs alongside the five sequential stages. PRISM(E) is opera‐
tionalised inside Microsoft 365 Copilot within Webfleet's own tenant — a choice
grounded in data governance and infrastructure fit rather than raw capability.
Interview findings refined the framework from its conceptual form into a final ver‐
sion, and the work is delivered through three artefacts: an AI Prompt Guide, a
strategic three-horizon roadmap, and a tactical roadmap for adoption.
The contribution is a design-research-grounded operationalisation of sense‐
making and prompt-engineering theory in the specific context of B2B product
marketing — an area that existing literature leaves largely unaddressed. ...
telematics, PMMs must turn fragmented inputs from market research, customer
feedback, sales signals, competitor intelligence, and regional sources into coher‐
ent Go-to-Market (GtM) insight. The difficulty is not a lack of information but an
excess of it, and current practice relies heavily on individual judgment that is hard
to scale, audit, or improve.
This thesis investigates how a structured, AI-enabled sensemaking framework can
reduce information overload and improve the quality of GtM insight synthesis for
PMMs at Webfleet. Following a qualitative, design-led approach grounded in de‐
sign science research, the project ran across three phases: a contextual inquiry us‐
ing semi-structured interviews with PMMs and secondary stakeholders, the design
of a framework and AI prompt guide, and a perception-based assessment and trial
use of the guide.
The central design outcome is PRISM(E), a six-element framework: Prioritise,
Reduce, Interpret, Synthesise, Mobilise, and an Equip layer of organisational en‐
ablement that runs alongside the five sequential stages. PRISM(E) is opera‐
tionalised inside Microsoft 365 Copilot within Webfleet's own tenant — a choice
grounded in data governance and infrastructure fit rather than raw capability.
Interview findings refined the framework from its conceptual form into a final ver‐
sion, and the work is delivered through three artefacts: an AI Prompt Guide, a
strategic three-horizon roadmap, and a tactical roadmap for adoption.
The contribution is a design-research-grounded operationalisation of sense‐
making and prompt-engineering theory in the specific context of B2B product
marketing — an area that existing literature leaves largely unaddressed. ...
At Webfleet, a Bridgestone company operating in European commercial-vehicle
telematics, PMMs must turn fragmented inputs from market research, customer
feedback, sales signals, competitor intelligence, and regional sources into coher‐
ent Go-to-Market (GtM) insight. The difficulty is not a lack of information but an
excess of it, and current practice relies heavily on individual judgment that is hard
to scale, audit, or improve.
This thesis investigates how a structured, AI-enabled sensemaking framework can
reduce information overload and improve the quality of GtM insight synthesis for
PMMs at Webfleet. Following a qualitative, design-led approach grounded in de‐
sign science research, the project ran across three phases: a contextual inquiry us‐
ing semi-structured interviews with PMMs and secondary stakeholders, the design
of a framework and AI prompt guide, and a perception-based assessment and trial
use of the guide.
The central design outcome is PRISM(E), a six-element framework: Prioritise,
Reduce, Interpret, Synthesise, Mobilise, and an Equip layer of organisational en‐
ablement that runs alongside the five sequential stages. PRISM(E) is opera‐
tionalised inside Microsoft 365 Copilot within Webfleet's own tenant — a choice
grounded in data governance and infrastructure fit rather than raw capability.
Interview findings refined the framework from its conceptual form into a final ver‐
sion, and the work is delivered through three artefacts: an AI Prompt Guide, a
strategic three-horizon roadmap, and a tactical roadmap for adoption.
The contribution is a design-research-grounded operationalisation of sense‐
making and prompt-engineering theory in the specific context of B2B product
marketing — an area that existing literature leaves largely unaddressed.
telematics, PMMs must turn fragmented inputs from market research, customer
feedback, sales signals, competitor intelligence, and regional sources into coher‐
ent Go-to-Market (GtM) insight. The difficulty is not a lack of information but an
excess of it, and current practice relies heavily on individual judgment that is hard
to scale, audit, or improve.
This thesis investigates how a structured, AI-enabled sensemaking framework can
reduce information overload and improve the quality of GtM insight synthesis for
PMMs at Webfleet. Following a qualitative, design-led approach grounded in de‐
sign science research, the project ran across three phases: a contextual inquiry us‐
ing semi-structured interviews with PMMs and secondary stakeholders, the design
of a framework and AI prompt guide, and a perception-based assessment and trial
use of the guide.
The central design outcome is PRISM(E), a six-element framework: Prioritise,
Reduce, Interpret, Synthesise, Mobilise, and an Equip layer of organisational en‐
ablement that runs alongside the five sequential stages. PRISM(E) is opera‐
tionalised inside Microsoft 365 Copilot within Webfleet's own tenant — a choice
grounded in data governance and infrastructure fit rather than raw capability.
Interview findings refined the framework from its conceptual form into a final ver‐
sion, and the work is delivered through three artefacts: an AI Prompt Guide, a
strategic three-horizon roadmap, and a tactical roadmap for adoption.
The contribution is a design-research-grounded operationalisation of sense‐
making and prompt-engineering theory in the specific context of B2B product
marketing — an area that existing literature leaves largely unaddressed.