Nobody owns AI at your franchise brand, and that's already costing you

The 36% problem hiding in plain sight

When IBM surveyed more than 600 Chief AI Officers earlier this year, one number stood out: organizations using centralized or hub-and-spoke AI governance models reported 36% higher ROI on their AI investments than those running decentralized approaches - not 5% higher, not 10%, but thirty-six percent from governance structure alone, before a single tool was chosen or a dollar was spent on implementation.

Run a franchise brand and that number lands differently. Franchise networks are, by definition, decentralized. Franchisors set standards; franchisees run locations; and in most networks, AI decisions (which vendors to use, what to automate, how to handle AI-generated leads) fall somewhere between all three, with no clear owner.

The irony is structural: the franchise model that gives brands scale is the same model that, without deliberate intervention, produces exactly the governance failure IBM has now quantified.

What "nobody owns it" looks like in practice

In most franchise brands operating today, AI responsibility is distributed across function heads. The CTO evaluates platforms and integrations. The CMO tests AI tools for lead generation and digital advertising. The VP of Operations decides what gets pushed to locations. None of them have full visibility across the others' decisions, and none are accountable for AI outcomes across the network as a whole.

This is not a failure of leadership. It reflects how franchise organizations grew before AI mattered. Technology decisions were siloed because technology used to be siloed. An HVAC franchise's call-center software and its digital ad platform never needed to talk to each other until AI started routing leads, qualifying callers, and generating follow-up sequences that touched all three at once.

Now they do. And the ownership gap has consequences.

When a franchisee in one market deploys a third-party AI chatbot that doesn't follow brand voice guidelines, who catches it? When an AI-driven lead routing rule underperforms in a particular region, who owns the fix? When a state introduces AI transparency disclosure requirements, who ensures consistent compliance across 150 locations? In decentralized governance, the answer to all of these is effectively: nobody, until something breaks.

Why franchise brands are uniquely exposed

The accountability gap in corporate AI governance is well-documented. In franchise networks, it is structurally amplified.

Franchise networks are built on a specific tension: franchisors maintain brand standards and operational control, while franchisees retain the autonomy to adapt to local conditions. That tension is intentional. Academic research on franchise ownership structure confirms that franchisors depend on standardization (trademark protection, business concept control, operational oversight) while franchisees depend on local flexibility. The franchise compact balances both.

AI disrupts that balance in a specific way: AI decisions are cross-functional by nature. A decision about AI call handling touches operations (staffing, scripts, escalation protocols), marketing (lead source attribution, follow-up sequences), and compliance (call recording law, consumer data handling) simultaneously. In a corporate organization, a Chief AI Officer can own that decision across functions. In a franchise network with no equivalent role, the decision fragments; each function handles its piece, and the whole never gets owned.

The broader market has already responded. IBM research confirms that 76% of organizations now have a Chief AI Officer, up from 26% just one year ago. Among those CAIOs, 57% report directly to the CEO or board, and 76% say other executives consult with them before making AI decisions. That's a governance structure with real authority, not a dotted-line advisory role.

Franchise brands are not tracking at that pace. And because franchise networks operate at scale, the cost of the gap compounds with every new location, every new AI vendor, and every new pilot that never becomes a system-wide deployment.

The pilot trap

Franchise brands tend to be good at AI pilots. A test with one AI-powered phone answering system across five locations. A trial of automated lead follow-up sequences in one region. An experiment with AI-generated local ad copy for a handful of franchisees.

Pilots succeed where rollouts stall.

The reason is almost always governance. A pilot has a sponsor (often whoever championed the vendor relationship). It has a timeline, a contact at the vendor, and a small enough footprint that issues get handled informally. A system-wide rollout requires something different: a decision-making structure that can handle franchisee variation, override conflicts between functional stakeholders, negotiate vendor contracts at network scale, and sustain adoption after the launch momentum fades.

Without a CAIO or equivalent, that structure does not exist. The pilot sits in limbo. Other departments run their own pilots. The network accumulates a patchwork of AI tools with no cross-functional owner, no consolidated view of what is working, and no one accountable when something goes wrong.

IBM's research frames this precisely: organizations that redesign their operating models before automating achieve two to three times higher ROI and twelve to eighteen months faster payback than those that automate first and govern later.

What franchise AI governance actually requires

The 36% ROI gap from IBM's research comes from comparing three operating models: centralized (one team owns all AI decisions), hub-and-spoke (a central function sets standards and governs across business units that execute locally), and decentralized (each unit does its own thing).

For franchise networks, hub-and-spoke is the natural fit and the most underused. It maps directly to how franchise organizations are already structured: the franchisor sets brand standards and operational policies; franchisees execute locally. A hub-and-spoke AI governance model simply extends that existing logic to AI: the franchisor (hub) owns strategy, vendor selection, data governance, and performance standards; franchisees and regional teams (spokes) adapt deployment to local conditions within those guardrails.

Making that work requires an executive with cross-functional authority - not a committee, not a steering group that meets quarterly. A single person, whether a Chief AI Officer, a VP of AI, or a senior operations leader with an explicit AI mandate, who can make binding decisions across the CTO, CMO, and operations functions, and who owns AI ROI as a defined responsibility.

What that role looks like in practice:

Centralize AI vendor governance

One contract owner for AI vendors at the network level. Franchisees do not independently procure AI tools that touch customer-facing or brand-critical workflows. The hub negotiates, evaluates, and approves; spokes implement.

Own the cross-functional decision layer

AI decisions that touch more than one function (lead routing logic, call handling scripts, compliance configurations) get resolved by the AI owner, not by whoever has the most organizational leverage in a given week.

Build visibility before problems surface

Organizations with orchestration-led AI governance are twice as likely to have full visibility into their AI assets, according to IBM research. For a franchise network, that means a consolidated view of which tools are running, where, what they're producing, and what the escalation path is when something fails.

Set franchisee adoption standards without mandating uniformity

Franchisees will have different technical readiness, different local market conditions, and different levels of enthusiasm for new tools. Hub-and-spoke governance sets floors (minimum standards, approved vendor lists, required configurations) without requiring identical implementation at every location.

What this means for PE portfolio operators

For private equity firms managing multiple franchise brands, the governance problem multiplies. Each brand has its own functional leadership. Each is running its own AI experiments. There is no mechanism for cross-brand learning, no shared governance structure, and no way to deploy AI insights developed at one brand across the portfolio without starting from scratch at each company.

IBM research confirms that organizations with orchestration-led governance achieve 29% lower losses from AI irregularities and 20% higher ROI overall. For a PE portfolio managing five to thirty brands, the ROI impact of centralizing even basic AI governance at the portfolio level is not incremental; it is structural.

The practical question for portfolio operators is not whether to centralize AI governance, but at what level. A portfolio-level CAIO or AI council can set cross-brand standards without eliminating brand-level flexibility. What it prevents is the alternative: each brand independently negotiating with the same vendors, running duplicate pilots, and discovering the same failures at different times with no shared memory.

Questions franchise leaders should be asking now

Before the next AI pilot launches, before the next vendor contract is signed, these are the accountability questions worth asking:

Who in our organization can tell me, today, every AI system currently running across our network? Who decides when an AI tool gets paused or decommissioned at the location level? When an AI decision touches marketing, operations, and compliance simultaneously, which executive has final authority? If a franchisee sues over an AI-driven outcome at their location, who is our designated accountable party? What is our policy for AI tools franchisees procure independently?

If the answers to more than one of those questions are unclear, the governance gap is real, and the 36% ROI penalty is already accumulating.

The franchise networks that close this gap first will not just spend less and earn more from AI investments. They will be the ones that can scale AI deployments across hundreds of locations without the fragmentation, the compliance exposure, and the stalled pilots that will define the networks that got to AI governance too late.

If your franchise network is working through how to structure AI ownership before the next implementation, BeForm's Discovery Workshop is designed exactly for this: mapping where AI has the highest impact in your network, and building the accountability structure that makes deployment across locations possible.