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BEN · Franklin Resources, Inc.

Asset Management · mkt cap $16.3B · calls: Q2 FY2026 vs Q1 FY2026
16.0 conviction · conf-adj 16

conf 3/10

enthusiasm:18.0 · trend:-5 · quantifies:0 · impact:0 · under_radar:5 · credibility:0 · business_impact:4 · disruption:-6 · confirmation:0

Enthusiasm latest 6 / prev 7 (falling)

Franklin's AI thesis rests on one concrete commercial deployment—the Intelligence Hub multi-agent distribution platform (Microsoft Azure)—which has produced a measurable ~10% lift in wholesaler meeting activity but no quantified revenue impact yet; management explicitly says it is 'too early to quantify the sales uplift.' The enthusiasm level ticked down from Q1 to Q2 as Johnson herself opened her answer with the caveat that 'I do not think many companies can say AI is yet material,' and the ROI tracking framework Nicholls described is still being constructed rather than reported. The thesis is credible in its specificity (named use cases, an actual productivity metric, dedicated centralized headcount, IS&T spend rising on AI) but remains pre-revenue in evidence, with the strategic payoff—margin expansion toward 30%+ and competitive moat via proprietary data scale—framed as a 2027 and beyond story.

GROUNDED NEXT-FY IMPACT vs CONSENSUS

Grounded on actual base — revenue $8.8B · net income $0.5B · net margin 6.0% · diluted EPS 0.91

These are next-fiscal-year annual uplift estimates, not next-quarter numbers.

Aggregate next-FY est. rev uplift: % · next-FY EPS uplift: % · vs analysts: unclear · priced in: medium (model's call-read: high; verdict above is the hard-data one used for ranking) · confidence: 3/10

ClaimFigureArithmeticNext-FY Rev %Next-FY EPS %
Call-list finalization time −90%
productivity · soft		90% drop (3–4 hours → ~15 minutes)Disclosed time only: midpoint 3.5 hr → 0.25 hr ⇒ 3.25 hr saved/event (3.25/3.5 = 92.9% ≈ 90%). No disclosed events/rep/year, rep count, or $/hr ⇒ cannot convert to $ opex or revenue. rev_uplift_pct = 0 (no topline claim). eps_uplift_pct not computable without FTE/$ base.0
Meeting-prep time −67% per rep/week
productivity · soft		~67% drop (6 hours → ~2 hours per week)Disclosed: 6 − 2 = 4 hr saved/rep/week (4/6 = 66.7%). Annualized hours/rep = 4 × 52 = 208 hr. No disclosed wholesaler headcount or loaded cost ⇒ cannot compute $ saving: e.g. even at $100/hr fully loaded, need N reps. rev_uplift_pct = 0 unless capacity reflows to sales (see meeting-lift claim; avoid double-count). eps_uplift_pct not computable.0
Wholesaler client meetings +9–10%
engagement · soft		9%–10% increaseHard on activity: midpoint 9.5% more meetings. Management (Q2 FY2026): revenue impact 'too early to quantify' — no disclosed wholesale-revenue % or meeting→fee elasticity. Scenario math only (soft): if ε=1 and 35% of rev is wholesale-correlated (not disclosed), company rev_uplift_pct = 9.5% × 35% = 3.3%; incr_rev = 0.095×0.35×8,770.7M = $292.1M; @ current net margin 5.9847% ⇒ incr_NI = $17.5M; vs adj consensus NI $1,098.1M (not GAAP $524.9M): eps_uplift_pct = 100×17.5/1,098.1 = 1.6%. Upper bound (ε=1 on 100% rev, not credible): rev_uplift_pct = 9.5; incr_rev = $833.2M; incr_NI = $49.9M; eps_uplift_pct = 4.5%. GAAP-denom EPS would be 100×49.9/524.9 = 951% (thin-base artifact — excluded).
IS&T Q3 FY2026 guide $155M (AI-driven)
cost · soft		$155M Q3 FY2026 IS&T$155M is total quarterly IS&T expense (disclosed), not incremental AI spend. 'Slightly higher than last quarter' — prior Q $ not in claims. Illustrative incremental AI opex only: assume +$5M/qtr vs prior Q ⇒ $20M/yr run-rate; rev_uplift_pct = 0; after-tax drag = 20×(1−0.21) = $15.8M; eps_uplift_pct = −100×15.8/1,098.1 = −1.4% (negative = cost). Annualized total IS&T run-rate 4×155 = $620M = 7.07% of $8,770.7M rev (context, not incremental AI).0
Cumulative data spend for AI training
cost · soft		Hundreds of millions of dollars (no precise figure)No precise $ — per method, no invented midpoint. rev_uplift_pct = 0; eps_uplift_pct not computable.0

Assumptions: Revenue base: FY2025 actual $8,770.7M (2025-09-30). EPS/NI denominator: consensus adjusted FY2025 NI $1,098.1M and EPS $2.13 (10 analysts) — not GAAP NI $524.9M / EPS $0.91 (would inflate EPS% ~2.1×). Incremental revenue flow-through: current net margin 5.9847%. Cost savings tax rate: 21%. Next-FY phasing: Intelligence Hub benefits observed Q1–Q2 FY2026; management says revenue 'too early to quantify' — no hard next-FY revenue/EPS credited in aggregate. Engagement→revenue scenarios use unstated wholesale mix (35% example only in basis, not aggregated). No double-count of prep-time savings and meeting lift (likely same causal chain).

Top line: No hard, company-level revenue uplift: the only quantified growth lever is +9.5% wholesaler meetings, but management explicitly will not size revenue yet. Illustrative segment map (9.5% × 35% wholesale mix) ⇒ ~3.3% of total revenue (~$292M) — assumption-heavy, not aggregated. Productivity time savings (90% call-list, 4 hr/wk prep) have rev_uplift_pct = 0 on a standalone basis.

Bottom line: No hard aggregate EPS uplift. Disclosed IS&T $155M/qtr is total IT spend, not isolatable AI increment; illustrative +$5M/qtr AI lift ⇒ ~−1.4% EPS drag after tax vs adj NI. Cumulative data spend unquantified. Productivity claims lack rep count/$/hr to size opex savings. Margin-expansion to 30%+ (FY2027 target) is forward narrative, not in quantified claims.

[impact n/m (all claims soft/unanchored)] Actual FY2025 revenue $8,770.7M is already +32.9% vs consensus $6,602.1M (same fiscal date) — bases appear non-comparable (GAAP/reported vs adjusted/consensus). Consensus FY2025→FY2026: revenue +6.0% ($6,602M→$7,001M), EPS +28.7% ($2.13→$2.74), NI +28.1% ($1,098M→$1,408M). Quantified AI claims do not support a hard incremental layer above that trajectory: largest lever (+9–10% meetings) is unmapped to $ revenue; illustrative 3.3% rev / 1.6% EPS (segment scenario) is below FY2026 EPS growth already embedded (+28.7%). AI investment costs (IS&T, data) are real but not precisely sized vs consensus cost forecasts.

QUANTIFICATIONS
Call-list finalization time reduction: 90% drop (3–4 hours → ~15 minutes) (Observed at Intelligence Hub rollout, reported Q1 FY2026, bottomline)
“What we saw is the time to finalize call lists dropped 90% when we rolled this out. Now what is that? It's you know, it went from three to four hours to fifteen minutes.”
Meeting-preparation time reduction per distribution rep per week: ~67% drop (6 hours → ~2 hours per week) (Observed at Intelligence Hub rollout, reported Q1 FY2026, bottomline)
“And the prepping for meetings dropped you know, from six hours to two hours or something per week.”
Lift in number of client meetings for wholesalers: 9%–10% increase (Observed upon Intelligence Hub rollout; confirmed again as 'about 10% more clients' in Q2 FY2026, both)
“Q1: 'what it's done is actually added 9% to 10% increase in the number of meetings that our distribution team has.' Q2: 'Early on, we see our wholesalers seeing about 10% more clients.'”
IS&T expense guidance uplift attributable to AI investment: $155M for Q3 FY2026, described as 'in line to slightly higher than last quarter based on AI investment specifically' (Q3 FY2026 forward guidance, stated on Q2 FY2026 call, bottomline)
“IS&T is $155 million, which is in line to slightly higher than last quarter based on AI investment specifically.”
Cumulative data spend to support AI model training: Hundreds of millions of dollars (no precise figure given) (Cumulative/historical, stated on Q1 FY2026 call, bottomline)
“We spent hundreds of millions of dollars on data. And so to be able to scale that data, plus the data you generate internally across all of your different capabilities is really important in training models.”
PAST (realized)
CURRENT (now)
FORWARD (guidance)
PRICED-IN (REFINED)
MEDIUM

Est. revisions rising  ·  Fwd P/E 13.1  ·  EV/Sales 3.1x

AI claim maps to Investment Advisory, Management and Administrative Service, Sales And Distribution Fees, Service, Other

Estimate-revision momentum is rising on hard data: price targets step up (lastMonthAvg 31 > lastQuarterAvg 29.5 > lastYearAvg 27.33) and FY2026 consensus EPS rebounds above prior years, even though monthly rating counts are mostly flat (buys stable, holds up slightly). Forward valuation is not stretched for a mature asset manager (fwd P/E ~13.1, EV/Sales ~3.1, below TTM P/E ~20), so the market has not fully paid up despite higher targets. AI-driven efficiency or fee leverage would most plausibly flow into the core advisory/management fee line, with secondary read-through to distribution and other service fees—not total revenue evenly.
BUSINESS IMPACT - QUALITATIVE MATERIALITY

5/10 qualitative impact   moderate  medium-term · mixed evidence

Where AI matters: distribution sales productivity and operating efficiency

Intelligence Hub is a real deployed multi-agent platform with ~10% more wholesaler client meetings and large workflow time cuts, but management will not quantify revenue or EPS uplift and explicitly says AI is not yet material firm-wide.

Caveats: ~10% meeting lift is unmapped to revenue, AUM, or EPS; IS&T and cumulative data spend may offset near-term efficiency gains; Fee compression and low-cost passive or AI-assisted DIY investing remain structural threats to active management pricing; Consolidation and data-scale moat benefits are strategic narrative, not yet in reported P&L

AI DISRUPTION / CANNIBALIZATION RISK  two-sided · 5/10

AI can compress active-management fees and commoditize research differentiation, yet Franklin's scale, proprietary data spend, and distribution tooling partly offset that by improving wholesaler throughput and favoring consolidation over subscale peers.

OPTIONS / MARKET STRUCTURE

option liquidity: fair

ATM IV
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TYPICAL BID-ASK
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OPEN INTEREST
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proxy inputs — dollar-ADV $158M · beta 1.591 · px $31.52

source: proxy (no options chain on FMP)
FMP /stable/ exposes no options-chain endpoint on this key, so ATM IV, bid-ask spread and open interest are unavailable. Liquidity below is a PROXY from dollar-ADV, beta and price level (a stand-in for option depth), not measured option-market data.

VERBATIM AI QUOTES
“IS&T is $155 million, which is in line to slightly higher than last quarter based on AI investment specifically.”
— Matthew Nicholls, Q2 FY2026
“We look at AI across growth and efficiency. Having run technology, I do not think many companies can say AI is yet material; everyone is doing a lot. I am proud of our work because we were early adopters in multi-agent orchestration of AI—our Intelligence Hub—which is our platform used for distribution in partnership with Microsoft.”
— Jennifer M. Johnson, Q2 FY2026
“If I bucket our AI efforts, in distribution and investments it is about growth opportunities; in operations and technology it is about efficiencies and throughput. The simple problem is ensuring salespeople are seeing the right clients and having the best conversation. The Hub pulls data from CRM, product systems, external product systems, and maybe social media. LLMs are not great with analytics, so you marry them with other agents.”
— Jennifer M. Johnson, Q2 FY2026
“Early on, we see our wholesalers seeing about 10% more clients. It is too early to quantify the sales uplift, but we see administrative efficiency gains and signs of sales uplift, and we are rolling it out more broadly.”
— Jennifer M. Johnson, Q2 FY2026
“We created a virtual research analyst for one team, where we fed views and philosophy, and it will generate ideas and challenge proposals—'have you thought about these things?'—and it has reviewed historical trades. The important thing is a centralized group to share AI expertise and learnings.”
— Jennifer M. Johnson, Q2 FY2026
“Where we outsource, we are reviewing contract lengths so AI efficiencies do not accrue only to vendors. In-house, in reconciliation, RFPs, and other functions, we are seeing some efficiencies. It is early, and in technology we measure how much code is being written by AI to gauge adoption.”
— Jennifer M. Johnson, Q2 FY2026
“In terms of spend, we have a fully staffed dedicated centralized team. Within that team, we have individuals focused on investment and sales functions and on effectiveness and efficiency. There is a revenue part and a cost part. We are tracking dollars spent versus dollars saved or gained from using and adopting AI. It is early days, but we are building that discipline.”
— Matthew Nicholls, Q2 FY2026
“Turning to artificial intelligence, we've made significant progress in advancing our AI efforts. Yesterday, we announced the launch of Intelligence Hub, a modular AI-driven distribution platform powered by Microsoft Azure. Building on the advanced financial AI initiatives announced in April 2024, Intelligence Hub delivers our vision for US distribution by modernizing core activities, improving sales effectiveness, and enhancing the client experience.”
— Jenny Johnson, Q1 FY2026
“What we saw is the time to finalize call lists dropped 90% when we rolled this out. Now what is that? It's you know, it went from three to four hours to fifteen minutes. And the prepping for meetings dropped you know, from six hours to two hours or something per week. Those are small little incremental cost savings or hope more importantly, what it's done is actually added 9% to 10% increase in the number of meetings that our distribution team has.”
— Jenny Johnson, Q1 FY2026
“AI the amount of data required to truly train a model is really significant. And if you're a smaller manager, one is you won't be able to buy the kind of data. We spent hundreds of millions of dollars on data. And so to be able to scale that data, plus the data you generate internally across all of your different capabilities is really important in training models. And it's just going to be hard to compete on training those models if you don't have a scale.”
— Jenny Johnson, Q1 FY2026
“As you start to train your workforce on how to leverage Agenic AI, which we were very early adopters of broadly rolling out ChatGPT, and we do training on how to create a genetic AI. We do hackathons with our investment teams. And it's a cross-functional hackathons. We put people together that are across various sins to say, go build a Genic AI. And they're doing things that are built one on top of the other, and then we take them and we test them across others.”
— Jenny Johnson, Q1 FY2026
“I mean, it's possible that we're deep in on AI. We're deep in on how to maximize our presence that we have. In India or in Poland, for example, where we've got very large operational capabilities and great talent in these places.”
— Matt Nicholls, Q1 FY2026
“Anytime you have technology breakthroughs, first thing people do is just make more efficient what they do today. That's why we give you quotes like, hey. We're more efficient on the call. Because it's hard to measure the actual value-added output because that doesn't happen right away. It doesn't happen until you start to put in the hands of your people so that they can build those ideas.”
— Jenny Johnson, Q1 FY2026
ANALYST QUESTIONS ON AI
Q (Q2 FY2026, Michael J. Cyprys (Morgan Stanley)): I wanted to ask about AI. Could you update us on how you are using AI across the organization today, the use cases that have been most impactful so far and key learnings, and if you are able to help quantify any of the benefits that you are seeing? As you look out over the next couple of years, what steps are you taking to further embed AI throughout the organization? I know, Matt, you mentioned some uplift on expenses in part from AI investments. Maybe you could elaborate on some of those investments and how you are thinking about the longer-term benefits.
A: Johnson: Frames AI under two buckets—growth (distribution and investments) and efficiency (operations and technology). Intelligence Hub (multi-agent, Microsoft Azure) is live; wholesalers are seeing ~10% more clients early on but revenue uplift is too early to quantify. Investment teams run hackathons, share agents in a central library, and one team has a virtual research analyst. Vendor contracts are being reviewed to capture AI efficiencies internally. AI code-generation share is being tracked as an adoption gauge. Nicholls: A fully staffed centralized AI team is in place with sub-teams covering sales/investment effectiveness and cost efficiency; a discipline of tracking dollars spent vs. dollars saved/gained is being built but is early days.
Q (Q1 FY2026, Ken Worthington (JPMorgan)): I guess pressing AI further, Jenny, you've been in the press talking about the impact that AI has on asset management. Suggesting that it could drive, if not accelerate, more consolidation in the asset management industry. So maybe one, how does AI drive consolidation? And then two, from Franklin's perspective, how would AI sort of alter your ability and willingness to do the M&A transactions and fill in the gaps that you mentioned sort of earlier in the call?
A: Johnson: AI drives consolidation because training competitive models requires massive proprietary data that smaller managers cannot afford—Franklin has spent hundreds of millions on data. Scale of internally generated data across asset classes compounds the advantage. This raises the bar for standalone managers who are narrow in their capital-stack focus. On M&A, Johnson does not say AI changes the calculus directly but implies that scale advantages in AI reinforce the already-stated preference for organic growth and disciplined bar for acquisitions. She also notes Franklin was an early and broad adopter of agentic AI (ChatGPT rollout, cross-functional hackathons, central agent library) as evidence of competitive positioning.