Where Business Experience Fits in AI Consulting with WSI

A company wants to reduce the time its sales team spends preparing proposals. Reps pull pricing, case studies, product details, and prospect-specific language from several places. AI could help produce a first draft, provided the company sets clear rules around the work.

The team still has to decide which details must be supplied, which pricing fields require human input, which claims are approved, and who reviews legal language. A faster draft has little value if it puts margin, accuracy, or credibility at risk.

Experienced operators recognize these problems. They know what happens when information is incomplete, approvals are unclear, or a proposal reaches a client with the wrong assumptions. That knowledge is directly relevant to AI consulting.

 For someone moving from business leadership into advisory work, the responsibility is familiar. The difference is applying that experience to a client's decisions rather than those of their own organization.  

The First Decision Isn't Which AI Tool to Buy 

Some leaders assume AI consulting belongs to developers, data scientists, or people with technical certifications. A former CMO, operations director, consultant, or agency owner may see a commercial background as insufficient.

Yet early AI decisions depend heavily on business judgment. The client first needs to choose a worthwhile problem and define what a safe, useful result would look like.

An owner asking which AI tool to buy may be dealing with incomplete data, inconsistent follow-up, unclear ownership, or no shared standard for good work. The advisor identifies the bottleneck, decides whether AI belongs in that process, and defines how the team will use and review it.

How a Business AI Advisor Differs from a Technical Specialist

A company may need both roles during an AI project, but they handle different parts of the work.

The roles aren't mutually exclusive. Technical input may be needed during discovery, particularly when data quality, security, or system constraints affect what's feasible. The advisor keeps the business priorities in view while specialists contribute the technical expertise required.

What Business Experience Contributes and What It Doesn't 

Operations leaders recognize weak handoffs and processes employees struggle to follow. Sales and marketing leaders understand buyer expectations, information quality, and client communication. Finance, consulting, technology, and business ownership experience bring familiarity with controls, competing priorities, and difficult decisions.

Nicky McKenna's transition from retail leadership into digital and AI consulting illustrates how commercial experience can inform strategic client conversations.

PwC's Global AI Jobs Barometer research analyzed more than one billion job advertisements and found that newly added tasks in the most AI-exposed roles were 2.5 times more likely to rely on human-intensive abilities, including empathy, judgment, and creativity, than those in the least AI-exposed roles.

The research examines changes in workforce skills, not demand for AI consultants. It does, however, reinforce the relevance of capabilities experienced business leaders have developed throughout their careers.

What You Still Need to Learn

Business experience helps you understand the client. Credible AI advice also requires current knowledge of the tools, their risks, and their limits.

Advisors need to recognize when an answer may be incomplete or inaccurate, which company information should stay out of a public tool, and where human review is required. They also need enough technical awareness to know when a specialist should join the project.

They need to understand data responsibility, privacy, AI governance, and how to assess whether a proposed use case is appropriate. That includes designing small tests, choosing useful measures, and explaining the results in business terms.

A single course can introduce the basics. Continued training, real project experience, peer input, and specialist support matter because tools, regulations, and risks keep changing.

What the Proposal Pilot Needs to Prove 

Before recommending a tool, the advisor maps how the proposal moves from request to client, clarifies the required information and decisions, and then tests AI on a limited number of proposals.

The company can compare drafting time, revisions, and adherence to the agreed quality standard. The team can then determine whether the pilot improved the proposal process without compromising accuracy or review standards.

What an Initial AI Engagement Can Reveal

Suppose the proposal pilot succeeds. Salespeople prepare first drafts faster, and managers spend less time fixing missing details or incorrect assumptions.

An initial AI engagement may reveal a related business issue worth assessing, such as inconsistent follow-up. CRM records may be incomplete, and account information may sit across emails, notes, and separate systems. Leaders may then ask for help with call summaries, account research, or the handoff from sales to delivery.

The advisor already understands how the company sells, who approves key decisions, and where errors create risk. Each new idea still requires its own assessment, but the advisor does not have to relearn the business from the beginning.

What WSI Provides and What Remains Your Responsibility 

Business experience can help shape the first client conversation. Across different clients, however, systems, data quality, and readiness to change will vary. Without a consistent method, every engagement starts from the beginning.

A clear approach helps the advisor assess current work, identify where AI is already being used, and set practical priorities. It also gives the client a defined problem, an owner, a review process, and a way to judge the first project.

Some projects will require technical help with integrations, data preparation, or specialist configuration. The advisor can continue leading the business decision while the appropriate expert handles that work.

WSI’s Business Strategy Framework and AI Adoption Roadmap help consultants structure early discussions, assess current AI use, and identify practical priorities. Consultants can also draw on more than 30 years of digital experience and knowledge shared across WSI’s global network.

In the proposal example, a consultant could use WSI's AI Readiness Assessment to help organize discovery questions before evaluating a possible pilot. Client-facing presentations and access to specialist expertise provide additional resources as the engagement takes shape. These tools support the consulting process, but they don't replace the advisor's responsibility to evaluate each client's circumstances.

An independent consultant may need to assemble their own discovery methods, client materials, and delivery partnerships. WSI makes these resources available through its established consulting model. Developing client relationships, maintaining expertise, and leading engagements remain the consultant's responsibilities.

Is WSI's AI Consulting Model the Right Fit?

Understanding how a business operates provides a useful foundation for AI advisory work. Applying that knowledge across clients requires current technical awareness, a disciplined consulting approach, and the ability to earn trust through the quality of the advice delivered.

WSI's franchise model combines independent business ownership with established consulting methods, ongoing learning, and access to specialist expertise.

Talk with the WSI team to learn how the consulting model works, what support is available, and whether it's the right fit for your experience and goals.