Questions we hear about Wheelhouse.

LARRY: Enterprise AI is incredibly useful. The question is not whether it can write a good paragraph or summarize a document. The question is whether the work reflects what your business is actually trying to do. In a big financial services company, that means your positioning, products, audiences, priorities, compliance requirements and the decisions leadership has made. Wheelhouse gives AI that business context and applies it consistently.

AL: I would add one thing. The model is only one component. Companies are going to use multiple models and those models will keep changing. Wheelhouse is the layer that connects the models to approved business context, data, workflows, people and systems. You should not have to rebuild the way your company works every time the best model changes.

AL: No. The foundation models are getting better at an extraordinary rate. Our job is to make those capabilities useful inside a real enterprise. Wheelhouse can work with the models a client chooses and add the business context, controls and workflow around them. By now, most businesses have an enterprise LLM — maybe more than one.

LARRY: Time between knowing and doing. Most firms do not suffer from a shortage of information. They suffer from the amount of time it takes to decide what the information means and turn that decision into coordinated action.

AL: And to do it without removing accountability. Faster is useful only if the organization still knows who decided what, what information they used and what was approved.

AL: Emphatically, no. That distinction matters. Wheelhouse can identify a signal, put it in context, recommend a response and show the reasoning. People make the decision. We say it very plainly: Wheelhouse recommends. Leadership decides.

LARRY: I actually think that makes the recommendation more useful. The goal is not to hand judgment to a machine. It is to give smart people a much better starting point so they spend less time assembling the facts and more time making the call.

AL: CRM is a system of record for customers, prospects, opportunities and activity. It's very good at that job. Wheelhouse is interested in what you do with the information. A client signal appears in Salesforce. Does it matter? What else do we know about the account? Which business priority does it connect to? What should happen next? Then the approved action can flow back into the systems your teams already use.

LARRY: No. Marketing is one of the teams that can execute decisions made through Wheelhouse. Campaigns, client communications, product messaging, thought leadership and sales support are visible outputs, but the important work often happens before marketing gets the assignment: What happened? What does it mean? What are we going to do?

AL: That's why we think of Wheelhouse as a layer that touches many parts of the organization. Not just a content tool.

LARRY: The things you want it to know. Your strategy, positioning, products, audiences, brand, approved messages and the distinctions that matter in your business. The goal is not to dump every document the company has ever created into a system and hope intelligence emerges.

AL: It also needs clear ownership. What is authoritative? Who can change it? Which business units can see which information? Good enterprise AI depends as much on those questions as it does on the model.

AL: Governance is often treated as a brake: who can use AI, what data can leave the company, what the model is allowed to access. Those controls are necessary. But there is another kind of governance that matters just as much. Is the work aligned with what the company has approved? Did the right person make the decision? Can we see why? Wheelhouse is designed around both control and useful action.

LARRY: From a CMO perspective, I would put it more simply. I want people to move faster without waking up to discover five teams have put five different versions of our story into the market.

AL: No. That would defeat the point. Most large firms have already made enormous investments in CRM, marketing automation, content systems, data platforms, collaboration tools and now enterprise AI. Wheelhouse is designed to work with that environment. We want those investments to become more useful, not create another rip-and-replace program.

AL: They have to be part of the design from the beginning, especially in financial services. Wheelhouse is designed to work within a client's security, data-access and governance requirements, including clear permissions around who can access information and who can approve important actions. The exact deployment, permissions and data controls depend on the client environment, so those questions are addressed as part of implementation, not bolted on later. Depending on the client environment, deployment can include the client's own cloud infrastructure.

AL: It depends on the use case and how many systems need to be connected. We would rather start with a defined business problem, get the right strategy, data and approvals in place and prove value than spend months designing a giant transformation program before anyone uses it.

LARRY: The fastest starts are usually the ones where everybody already knows the problem. If people are saying, 'This takes too long' or 'Why can't we act on this information?' we have a very concrete place to begin.

AL: There are two tracks. One is business: establishing the approved strategy, positioning, product information, audiences and guardrails Wheelhouse will use. The other is technical: connecting the models, data, systems and workflows required for the use case, with the client's security and permissions built in. The scope depends on where you start.

LARRY: Start with a business problem people already recognize — a product launch, a new audience, a sales opportunity or a response that takes too long today. Define what success looks like, configure Wheelhouse around the strategy, data and workflows that matter to that use case, and measure what changes.

AL: The point is to prove value in a defined part of the business before expanding. The exact scope and timeline depend on the use case and the systems that need to be connected.

AL: You could... The question is whether building and maintaining the connections among strategy, data, AI models, approval workflows and execution tools is where you want your internal teams spending their time. Plus we're continually upgrading the product based on multiple implementations and a wider variety of use cases than any single firm would want to invest in supporting. It's the same reason most companies don't build their own CRM, content management or marketing automation platforms. They use technology built for the job and configure it for their business.

LARRY: Pick a problem people already complain about. That's a much better starting point than saying, 'We need an AI strategy.'

AL: I agree. Start narrow enough to measure what changed. Did we reduce time? Rework? Manual effort? Did we make better use of systems we already own? Then expand from evidence rather than enthusiasm.

LARRY: Because we know it. We know the products can be complicated, the sales cycles are long, the audiences are sophisticated and every word can matter. Generic content is especially useless here.

AL: And because the operating environment is demanding. Security, permissions, auditability, compliance, legacy systems, multiple business lines... If an AI solution cannot live with that complexity, it is not an enterprise solution for financial services.

AL: AI will become less of a destination and more of a layer inside the way companies operate. People will stop being impressed that software has AI in it. They will care whether it makes the business work better. That is the direction Wheelhouse is built for.

LARRY: And I think the winners will be the firms that use AI to become more responsive without becoming more generic. Faster, yes. But also more specific to the client, more consistent with the brand and more connected to what the business is trying to accomplish.