Why Your AI Investment is Bleeding Cash (And How to Stop It)
Episode 2 • 16th April 2026 • AI into ROI • Rolando Lopez Nieto
00:00:00 00:15:22

Share Episode

Shownotes

In this episode, Rolando (Doctor in AI by 2027) cuts through the noise to explain why some companies see a massive return on their AI investments while others simply bleed cash on failed prototypes. Drawing from 14+ years of enterprise IT experience with major brands like KFC and Pizza Hut, Rolando introduces the three pillars of AI success: Governance, Customization, and the Human Factor.

What You’ll Learn:

  • The Prioritization Matrix: How to score AI projects based on business value vs. feasibility to secure "quick wins."
  • The Fractional CAIO Advantage: Why mid-sized businesses need enterprise-grade leadership without the Fortune 500 salary.
  • Custom vs. Off-the-Shelf: Why purpose-built systems create competitive advantages that are impossible to copy.
  • The Human Factor: Why change management and role redesign are the secret ingredients to project survival.

Key Data Points:

  • How to automate 70-80% of customer inquiries via conversational AI.
  • Why projects with strong change management deliver measurably higher ROI.
  • Navigating the European Union AI Act and upcoming compliance risks.

Connect with Rolando:

  • Personal Hub: [rolando.im]
  • Media & Insights: [AIintoROI.com]
  • Services & Consulting: [DigitalCog.ai]

Helping business leaders turn AI into ROI — without the hype.

Work with Rolando and his Team:

* AI Studies: Doctorate (2027), Master's, and multiple certifications by Univs. from Michigan, Texas, MIT (x2), and IBM.

* 14 years of experience at a Fortune 500 company in B2B IT, on-site and remote for the entire Americas continent.

* Live AI solutions in 300-3k locations: AI for call centers & drive-thru, AI for BOH restaurants. "AI as a Service Department" and customized AI services.

Connect with "AI into ROI":

* Send an email or book a free call at AIintoROI.com

• Newsletter

• Available on major podcast platforms.

Transcripts

Speaker:

right now there are two types of companies deploying AI

2

:

the first group is reporting multiple dollars

3

:

return per every dollar invested

4

:

the second group is bleeding multiple figures a month

5

:

on prototypes and failed implementations

6

:

the difference isn't technology,

7

:

is the approach and the framework

8

:

so let's talk exactly

9

:

how to join the group that's getting ROI

10

:

before you find yourselves

11

:

in the group that's losing money

12

:

as a preview on today's episode

13

:

we'll talk about the strategic framework needed to win

14

:

on AI projects

15

:

focusing on 3 pillars of success

16

:

governance

17

:

customized execution

18

:

and human factor

19

:

first

20

:

governance and cost control

21

:

why you need executive leadership to avoid burning cash

22

:

how to prioritize AI projects

23

:

so you can invest in quick-wins first

24

:

and why a fractional chief AI officer

25

:

cuts your costs

26

:

while keeping you compliant

27

:

with new regulations

28

:

second we dive into the custom applications

29

:

that deliver superior ROI

30

:

move beyond generic software to targeted custom systems

31

:

third we prove why managing the technology

32

:

change and the human factor

33

:

is a core investment in ensuring success with AI

34

:

projects

35

:

for our first section let's talk about the strategy

36

:

governance and cost control

37

:

here's what most operators get wrong

38

:

they think technology alone will guarantee success

39

:

but they forget about strategy and governance

40

:

first

41

:

organizations must carefully assess their capabilities

42

:

through an AI Readiness assessment service

43

:

this analysis evaluates your current infrastructure

44

:

data quality and organizational capability

45

:

to identify weaknesses

46

:

and form the foundation for a customized strategy

47

:

next we need to prioritize

48

:

most teams have dozens of ideas for AI

49

:

but your capacity your budget

50

:

and your team data

51

:

they are limited and you cannot do everything

52

:

this is where AI use case prioritization comes in

53

:

every potential project gets scored on two things

54

:

first business value

55

:

that's the revenue increase or cost reduction

56

:

second feasibility

57

:

that's how mature your data is

58

:

and how complex the integration will be

59

:

you plot everything on a matrix

60

:

high value and high visibility

61

:

and those are your quick wins

62

:

you do those first they fund your next phase

63

:

and prove the model works to your CFO

64

:

then you can focus on bigger bets

65

:

now who's accountable for your AI strategy

66

:

who manages the risk and controls the spending

67

:

this is why a chief AI officer

68

:

role exists

69

:

this is a senior executive

70

:

who takes the end-to-end

71

:

accountability for your AI

72

:

strategy your governance and the results

73

:

if a full time AI

74

:

officer is not entirely needed yet for your company

75

:

then you might

76

:

you might want to think about hiring a fractional

77

:

chief AI officer

78

:

it's essentially the same senior executive

79

:

but they work on a part-time basis based on contracts

80

:

why does this model work because a full time chief AI

81

:

officer can cost so much more in base salary alone

82

:

while a fractional engagement

83

:

delivers the same enterprise grade leadership

84

:

just for a few hours per week

85

:

and only while the engagement is needed

86

:

here's what a good chief AI officer does for you

87

:

first

88

:

they decide whether to

89

:

buy versus to build in-house

90

:

there's an ocean of vendors in technology and in AI

91

:

a chief AI officer

92

:

helps with their responsibilities of procuring

93

:

evaluating shortlisting and choosing these vendors

94

:

as well as to track every vendor on cost speed

95

:

features and contract terms

96

:

making sure that any implementation from any vendor

97

:

your company's best interests

98

:

are taken into consideration

99

:

second

100

:

they approve which AI models and platforms you use

101

:

and they control the spending on AI prototypes

102

:

not optimized can burn multiple figures a month

103

:

on API calls

104

:

this officer sets spending limits

105

:

searches for best practices to save costs

106

:

and negotiates volume discounts

107

:

third they manage compliance risk

108

:

The European Union AI Act is already a law

109

:

and more regulations are coming

110

:

this officer builds your governance framework

111

:

they define fairness policies

112

:

perform audit trails and test for biases

113

:

for example if a model has high risk of being biased

114

:

to a minority subset of the population

115

:

then the chief AI

116

:

officer should analyze and approve if it can go live

117

:

now our second section is titled

118

:

The Applications and capabilities of Custom AI

119

:

Development

120

:

but let me be clear when I say custom

121

:

it doesn't mean that we must always build in house

122

:

we can for sure

123

:

buy from vendors that have a particular solution

124

:

in some scenarios

125

:

it actually makes sense to buy from a vendor

126

:

if a base solution already exists

127

:

we just need to make sure

128

:

that the solution has a good degree of customization

129

:

for a company's problems and data

130

:

custom AI

131

:

solutions are purpose built systems that combine

132

:

advanced capabilities with your specific business logic

133

:

and business needs

134

:

this creates

135

:

competitive advantages that your competitors

136

:

cannot easily copy

137

:

No. 1 let's talk about how we can

138

:

transform the experience and service for our customers

139

:

one application is conversational AI

140

:

which can be either for call centers

141

:

drive thrus or text and social media

142

:

these chatbots basically handle inquiries 24/7

143

:

automating responses routing queries

144

:

and integrating with the back end of your systems

145

:

like your POS your CRM

146

:

your ticket system for real time data access

147

:

you can of course

148

:

keep a human agent in the loop to handle the exceptions

149

:

but the AI should be able to help and solve

150

:

70 to 80% or more of the calls

151

:

now how about AI to analyze calls

152

:

we could use natural language processing

153

:

to classify calls based on topic

154

:

or urgency and to discover the caller's intent

155

:

but AI doesn't always have to be for the customers only

156

:

how about AI help helping our own human agents

157

:

we certainly can have an internal chatbot

158

:

to help our human support team

159

:

provide a better service to our customers

160

:

for example whenever there are doubts

161

:

this chatbot can have the knowledge

162

:

and help with our company

163

:

policies

164

:

standard operating procedures and help with upsales

165

:

marketing and much more

166

:

No.2 AI

167

:

can also help with our financial

168

:

and administrative operations

169

:

for example intelligent document processing

170

:

automating the extraction

171

:

the validation and processing of documents

172

:

such as invoice processing

173

:

which is highly repetitive uh

174

:

we humans don't like

175

:

don't like it and it opens up for human errors

176

:

it certainly reduces the manual tasks

177

:

another application is fraud detection

178

:

where custom systems analyze suspicious transactions

179

:

for behaviors to prevent financial loss and cybercrime

180

:

how about automating the collection of debt

181

:

we can have AI

182

:

reach out to our customers via text or phone calls

183

:

to collect debt

184

:

now what about AI in legal aspects

185

:

sure legal counsels can now leverage generative AI

186

:

tools to task like contract drafting

187

:

contract review and legal research of course

188

:

human involvement

189

:

and final validation is still required

190

:

but the workload is certainly reduced

191

:

No. 3 what about AI in operations and supply chain

192

:

yes AI can also help

193

:

for example predictive maintenance

194

:

many manufacturing companies are doing this already

195

:

they gather data from the machine

196

:

either using the machine's built in sensors

197

:

or using external sensors

198

:

with these sensors they gather data like temperature,

199

:

RPM, voltage and much more

200

:

with historical data and current data

201

:

custom machine learning models can be built

202

:

that can forecast when a machine may fail

203

:

with these predictions

204

:

they can schedule preventive maintenance

205

:

and prevent an unexpected failure

206

:

that can be much more costly

207

:

now supply chain optimization

208

:

uses machine learning to manage logistics

209

:

procurement and distribution

210

:

the system monitors every SKU

211

:

predicts where demand will surge

212

:

and adjusts the inventory allocation automatically

213

:

the system reasons learns from context

214

:

and makes dynamic decisions without human input

215

:

AI can also be used to manage our workforce

216

:

to optimize staff allocation and scheduling

217

:

for example there are tools that help

218

:

restaurants create the schedule for their staff

219

:

based on AI powered sales forecast

220

:

and the amount of labor hours

221

:

needed to support those sales

222

:

and then you have solutions for specialized industries

223

:

for example healthcare uses AI for drug discovery

224

:

personalized medications

225

:

and assist in diagnosis

226

:

retailers deploy advanced recommendation engines

227

:

and dynamic pricing optimization

228

:

analytics teams use

229

:

automated machine learning to simplify model creation

230

:

and real time data analysis

231

:

the pattern is clear custom AI

232

:

works because it's built for your specific process

233

:

your specific data and specific business model

234

:

organizations have a higher chance of success

235

:

if they choose a solution tailored for them

236

:

our section No. 3 is titled the human Factor

237

:

and that is what may kill most AI projects

238

:

the human factor determines whether you capture that

239

:

ROI or waste that investment

240

:

AI is a little different from past it projects

241

:

because it changes how work is done

242

:

who does it and what decisions people make

243

:

this disruption is why managing the change

244

:

is more critical than ever

245

:

let me give you a concrete example

246

:

AI doesn't eliminate your supply chain planners

247

:

it redefines their value

248

:

they shift from manual crunching to a scenario planning

249

:

and risk management

250

:

that's fundamentally a different job

251

:

if you don't help them make that transition

252

:

they'll resist the system or use it wrong

253

:

and your ROI disappears

254

:

there's also the trust problems

255

:

AI outputs can feel like a black box

256

:

people don't trust what they don't understand

257

:

change management makes the black box transparent

258

:

you provide evidence of accuracy

259

:

you explain how the algorithm works in plain language

260

:

you show side by side comparisons

261

:

of their recommendations versus human decisions

262

:

here's the data that should concern every CFO

263

:

projects with strong change management

264

:

deliver higher ROI than projects without it

265

:

without adoption your algorithms go unused

266

:

you spend the money you build the system

267

:

and nobody uses it

268

:

so what does good change management look like

269

:

first role redesign

270

:

you map current roles to future roles

271

:

you identify what work goes away

272

:

what new work appears

273

:

and what skills people need to develop

274

:

you do this before deployment and not after

275

:

second training that's specific to the actual use case

276

:

not generic AI training

277

:

for example training that shows your sales team

278

:

how to exactly use the lead scoring system

279

:

including what to do when the AI

280

:

recommendation doesn't match their intuition

281

:

third success metrics that include adoption

282

:

not just technical performance

283

:

you measure how many users login daily

284

:

and how often they accept AI recommendations

285

:

and how satisfaction trends over time

286

:

fourth feedback loops

287

:

you create channels for users to report problems

288

:

suggest improvements

289

:

and see their input reflected in updates

290

:

this builds ownership

291

:

the organizations winning with AI treat technology

292

:

deployment

293

:

and people transformation as equally important

294

:

investments

295

:

if you focus only on algorithms and technology

296

:

but you ignore the humans who have to use it

297

:

then you're on your way to lose

298

:

so let's review the three biggest takeaways

299

:

from today's episode takeaway No. 1

300

:

governance and cost control aren't optional

301

:

if you're deploying AI without executive oversight

302

:

you're deploying cash and accumulating risk

303

:

a chief AI officer whether full time or part time

304

:

gives you the enterprise grade leadership

305

:

just for the hours needed

306

:

or based on the engagement needed

307

:

they help with compliance and regulations

308

:

they help decide whether it's best to build in house

309

:

or to buy from vendors

310

:

speaking of vendors they help with all the chaos

311

:

of procuring and evaluating vendors

312

:

and make sure they deliver what's best for your company

313

:

takeaway No. 2

314

:

custom AI delivers superior ROI

315

:

because it's built for your specific business model

316

:

organizations usually report better results when

317

:

solutions are customized for them

318

:

whether built in house or customized by a vendor

319

:

the companies winning right now are deploying systems

320

:

with a good degree of customization

321

:

either for their customer service

322

:

supply chain finance or operations

323

:

these systems

324

:

create competitive advantages that take competitors

325

:

years to replicate

326

:

and take away No. 3

327

:

the human factor is the difference between

328

:

actual ROI and wasted investment

329

:

technology is only half of the equation

330

:

you must invest in change management

331

:

role redesign and training

332

:

projects with strong change management deliver

333

:

measurably higher returns than projects without it

334

:

the window for early mover advantage is getting smaller

335

:

daily

336

:

the operators

337

:

moving now are building leads that compound over time

338

:

the tools exist the benefits are proven

339

:

the question is whether you'll execute

340

:

while there's still an advantage to capture

Chapters

Video

More from YouTube