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AI for Consultants
AI for consultants means using language models to synthesize research, draft slide storylines and support analysis, inside tools and rules that protect client confidentiality.
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Résumé
It matters because these tasks make up much of a consulting engagement, especially junior work, so AI is changing both how quickly firms deliver and how new consultants learn the job.
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Consulting runs on three kinds of work AI can speed up: gathering and synthesizing information, building a persuasive storyline, and analyzing data. Large firms have built internal tools for this. McKinsey, for example, introduced an internal generative AI assistant called Lilli in 2023 to search its own knowledge. Many smaller firms use enterprise versions of general assistants. Research synthesis means summarizing reports, interview notes and market material into themes. AI does this fast, but synthesis is not neutral. Models tend to repeat the most common view, smooth over contradictions and drop unusual outliers, and the outlier is sometimes the insight. Good practice is to require citations to specific sources and to read a sample of the originals. Storyline work fits AI well because consulting slides follow known conventions: a governing thought, supporting arguments and action titles that state conclusions. AI can propose structures and tighten headlines. It cannot know what the client's CEO is worried about. Analysis is safest when the AI writes code or formulas you can inspect and rerun, rather than stating numbers in prose. Confidentiality is the hard constraint. Client data is usually covered by contracts and NDAs, so consultants should use firm-approved tools whose terms exclude training on customer data. They should also follow the client's own rules, which may forbid AI use entirely. Evidence on quality is mixed. A 2023 field experiment led by Harvard Business School researchers with Boston Consulting Group consultants described a jagged frontier. On tasks within the model's capabilities, consultants using AI did better and faster. On a task outside it, they were more likely to reach a wrong answer. The lesson is to know which side of the frontier a task is on. For juniors, the classic first-draft work (desk research, formatting, data cleaning) is starting to shrink. This raises a real apprenticeship question: how do new consultants build judgment if they no longer do the groundwork?
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Tabax tànneef
Ni ñuy jëmmale aplikaasioŋ bi mooy wane ndax IA dafay gëna baaxal njariñ yi.
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Integraasioŋ bu baax ci def liggéey dafay jur njariñu liggéey bu jëfandikukat yi mëna wóolu.
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The Future of AI for Consultants
Firms are likely to keep building proprietary assistants over their own knowledge and to price some work by outcome rather than hours, though how pricing will change is uncertain. Junior roles will probably shift toward verification, client interaction and problem framing, and firms will need deliberate training to replace the learning that used to come from grunt work. Clients are also adopting the same tools, which may reduce demand for routine research and increase demand for judgment, implementation support and change management. Research like the jagged-frontier study suggests the advantage will go to consultants who can tell which tasks AI handles well.
Doxal ci àdduna dëgg
A consultant uploads twenty anonymized interview transcripts to an approved enterprise tool and asks for recurring themes, each backed by quotes and the interview it came from, then spot-checks the quotes against the originals.
A team gives an assistant its key findings and asks for a storyline using action titles, where each slide headline states the conclusion, then rewrites the sequence to match what the client's steering committee cares about.
An analyst asks an AI data tool to write Python code that cleans a sales export and computes margin by region, then reruns the code and checks the totals against the source system.
A firm deploys an internal assistant over its past proposals and knowledge base so consultants can find relevant prior work without pasting client material into public chatbots.
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Ekip yi mën nañu otomatise lu ëpp ba noppi dindi àtteb nit ñi.
Kalite mën na wàññeeku sudee duñu wéy di jàngat li ñuy génne.
Roadmap ngir samp gi
Defal kàrt ni liggéey bi di doxee leegi nga ràññee jéego bi gëna am jafe-jafe.
Mandargal barabu saytu nit balaa otomatisasioŋ bu mat sëkk.
Taggat jëfandikukat yi ci ay laaj, yooni eskalaasioŋ ak seeni sàrti kalite.
Toppal njariñu niveau liggéey bi ngir firndeel valeur buy wéy.
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What is AI for Consultants?
AI for consultants means using language models to synthesize research, draft slide storylines and support analysis, inside tools and rules that protect client confidentiality. It matters because these tasks make up much of a consulting engagement, especially junior work, so AI is changing both how quickly firms deliver and how new consultants learn the job.
What did the 2023 Harvard Business School and BCG field experiment describe?
Consultants did better on tasks inside the model's capability but were more likely to be wrong on a task outside it.
Why does the guide warn that AI research synthesis is not neutral?
Synthesis can smooth over contradictions and lose unusual data points, so citations and spot-checks matter.
What is the safest way to use AI for quantitative analysis, per the guide?
Code or formulas you can run make results reproducible and auditable.
Which practice fits the guide's confidentiality advice?
Contracts and NDAs usually govern client data, and some clients forbid AI use outright.
In retrieval-augmented generation, what happens before the model answers?
Documents are chunked, embedded and searched, and the top passages go into the context window.
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