WEBVTT
1
00:00:00.220 --> 00:00:03.980
Welcome to Mindful Leadership, Transforming Lives, with your host, Singh.
2
00:00:04.419 --> 00:00:07.540
This is where leadership meets purpose, presence, and passion. In
3
00:00:07.559 --> 00:00:11.720
today's fast-paced, high-pressure world, we face a leadership crisis. Too often,
4
00:00:12.099 --> 00:00:15.519
teams are led by managers who lack empathy, clarity, and vision,
5
00:00:16.039 --> 00:00:20.980
leaving individuals uninspired, disconnected, and undervalued. But it doesn't have
6
00:00:21.000 --> 00:00:23.390
to be this way. Great leadership isn't born, it's built.
7
00:00:23.750 --> 00:00:27.730
The endgame? Empower your leadership, inspire the world. Every week,
8
00:00:27.769 --> 00:00:30.510
join me, your host Singh, as we tackle the gaps
9
00:00:30.690 --> 00:00:35.329
in leadership and life coaching head-on, learning how mindfulness, emotional intelligence,
10
00:00:35.429 --> 00:00:40.130
and compassion can redefine success. This isn't just another podcast.
11
00:00:40.729 --> 00:00:43.229
It's a mission to fill the void led by an
12
00:00:43.270 --> 00:00:46.450
effective leadership. And it's not just about leadership in business.
13
00:00:46.929 --> 00:00:50.310
Life coaching for leaders and new executives is at heart
14
00:00:50.350 --> 00:00:53.490
of this journey. Ready to rise above the noise and
15
00:00:53.530 --> 00:00:56.850
lead with clarity, purpose, and impact? Join a global movement
16
00:00:57.259 --> 00:01:01.159
dedicated to reshaping the future of leadership one mindful moment.
17
00:01:01.020 --> 00:01:07.590
At a time. Let's begin. Welcome back to the Mindful
18
00:01:07.609 --> 00:01:11.969
Leadership Podcast on the Transformation Network. This is the show
19
00:01:12.030 --> 00:01:16.079
where we turn stress into plans, too much thinking into
20
00:01:16.109 --> 00:01:20.859
clear thinking, and big problems into next steps. I'm your host, Singh.
21
00:01:21.500 --> 00:01:27.700
I've seen a lot of fancy dashboards, give answers that
22
00:01:27.959 --> 00:01:31.450
look just right, but we're actually wrong in real life.
23
00:01:32.109 --> 00:01:35.209
So grab something warm to drink, put your phone face down,
24
00:01:35.569 --> 00:01:39.450
and let's spend the next few minutes talking about clean data,
25
00:01:39.909 --> 00:01:44.939
clear thinking, and AI. I want to open with a story,
26
00:01:45.099 --> 00:01:47.780
and I want you to listen for the moment you
27
00:01:47.799 --> 00:01:52.599
recognize yourself in it. Our Chief Operating Officer, Anika, called
28
00:01:52.620 --> 00:01:55.560
me on Thursday afternoon last week. She was excited. Her
29
00:01:55.599 --> 00:01:59.719
company had just turned on an AI tool that looked
30
00:02:00.439 --> 00:02:03.719
at four years of customer information. The email gave a
31
00:02:04.019 --> 00:02:07.959
clear answer. Stop spending money on one group of customers.
32
00:02:08.280 --> 00:02:11.719
Spend more on another group of customers. The chart looked good.
33
00:02:11.759 --> 00:02:15.340
The answer sounded exact. It even had a decimal point,
34
00:02:15.870 --> 00:02:20.889
which made it feel extra smart. Anika had already shown
35
00:02:20.930 --> 00:02:23.460
it to her co-workers. And they loved it because it
36
00:02:23.599 --> 00:02:27.879
looked like proof. And I was glad for her, genuinely.
37
00:02:28.400 --> 00:02:32.490
Because for years, her team had been arguing about this
38
00:02:32.669 --> 00:02:37.610
exact question with nothing but instincts and slight dicks. So
39
00:02:37.729 --> 00:02:41.789
I said, that's a real step forward. And then I
40
00:02:41.939 --> 00:02:46.879
asked her one question. Anika, where does the segment label
41
00:02:47.080 --> 00:02:52.219
actually come from? So over the next 10 days, she
42
00:02:52.280 --> 00:02:55.919
found the problem. Three different teams had used the same
43
00:02:55.979 --> 00:03:01.900
label in three different ways. Sales used it for deal size,
44
00:03:02.620 --> 00:03:08.069
support used it for customer risk, marketing used it for industry. Later,
45
00:03:08.509 --> 00:03:12.430
during a system move, some missing information had been filled
46
00:03:12.469 --> 00:03:16.129
in with the most common answer just to keep things clean.
47
00:03:17.330 --> 00:03:20.129
So the level was not really one clear customer group.
48
00:03:21.409 --> 00:03:26.240
It was several different ideas wearing the same name. The
49
00:03:26.340 --> 00:03:29.840
AI had not lied. It had done exactly what it
50
00:03:29.900 --> 00:03:34.219
was asked to do. But it was using bad information.
51
00:03:34.550 --> 00:03:38.810
So it gave back a bad answer in a beautiful
52
00:03:38.849 --> 00:03:42.389
chart with a lot of confidence. So here is the
53
00:03:42.409 --> 00:03:47.460
big idea. Artificial intelligence or augmented intelligence, AI, no matter
54
00:03:47.500 --> 00:03:52.400
how you take it, does not magically fix the messy information.
55
00:03:54.000 --> 00:03:57.139
AI just makes whatever you give it bigger, faster, and
56
00:03:57.219 --> 00:04:01.009
more believable. So if you give it good information, it
57
00:04:01.030 --> 00:04:04.289
can help you find good answers. But in this case,
58
00:04:04.430 --> 00:04:07.349
if you give it messy information, it may give you
59
00:04:07.449 --> 00:04:11.409
a messy answer that sounds very smart. This is what
60
00:04:11.509 --> 00:04:16.410
makes this new. and tricky. In the past, bad information
61
00:04:16.850 --> 00:04:20.290
often looked bad. A broken report looked broken. A missing
62
00:04:20.350 --> 00:04:26.750
spreadsheet looked empty. But now, a wrong answer can look polished.
63
00:04:27.649 --> 00:04:30.509
It can look like a clean dashboard even when the
64
00:04:30.600 --> 00:04:36.759
information underneath is not clean at all. So AI is
65
00:04:36.819 --> 00:04:40.959
not a fixer, it is an amplifier. feed it a mess,
66
00:04:41.379 --> 00:04:45.740
and it hands the mess back in a proper suit.
67
00:04:46.060 --> 00:04:49.860
Some people say that very few organizations are truly ready
68
00:04:49.899 --> 00:04:54.060
for AI. Now, whether the exact number is right or not,
69
00:04:54.430 --> 00:04:57.829
the lesson is clear. Being ready for AI is not
70
00:04:58.009 --> 00:05:02.370
only about technology. It's about honesty. It's about humanity. You
71
00:05:02.410 --> 00:05:06.230
have to be honest about what information you have, what
72
00:05:06.290 --> 00:05:09.899
it means, and what might be missing or wrong. And
73
00:05:10.180 --> 00:05:12.980
this is not just true for companies. It is true
74
00:05:13.019 --> 00:05:16.759
for solopreneurs, people who are using this. Your mind has
75
00:05:16.879 --> 00:05:24.009
inputs too. When finished conversation, worries you picked from someone else,
76
00:05:25.009 --> 00:05:29.029
stories you have not checked, but guesses that feel like facts.
77
00:05:30.470 --> 00:05:35.069
If you feel and feed all of that into your thinking,
78
00:05:35.449 --> 00:05:38.970
your choices can become cloudy too. You remember the old saying,
79
00:05:40.240 --> 00:05:43.540
Bad input leads to bad output. Now, the difference now
80
00:05:43.959 --> 00:05:48.500
is that the bad output can sound very confident. And
81
00:05:48.560 --> 00:05:52.519
that's where the problem is. It matters right now because
82
00:05:52.800 --> 00:05:57.360
many leaders are being pushed to use AI quickly. They're
83
00:05:57.379 --> 00:06:00.819
being asked to move faster, save time, make smarter choices.
84
00:06:01.920 --> 00:06:08.689
Speed without clean inputs just creates confident mistakes. A leader
85
00:06:08.699 --> 00:06:12.029
can move very fast and still move in the wrong direction.
86
00:06:12.509 --> 00:06:15.670
That is why this episode is not only about data.
87
00:06:15.990 --> 00:06:19.410
It is about slowing down just long enough to make
88
00:06:19.449 --> 00:06:24.250
sure the answer is built on something true. Folks, for
89
00:06:24.300 --> 00:06:27.120
the last four years, we have been building a way
90
00:06:27.160 --> 00:06:31.420
to lead well in the age of AI. In this
91
00:06:31.459 --> 00:06:37.269
set 14, the conscious operator, we started with humans. Hold, understand,
92
00:06:37.629 --> 00:06:41.170
mark the line, apply the leverage and never outsource the
93
00:06:41.209 --> 00:06:45.920
human side to AI. We talked about stopping the noise,
94
00:06:46.860 --> 00:06:50.899
asking the right question and building on something strong. Under
95
00:06:51.480 --> 00:06:56.420
all of that is one simple idea. AI gives everyone speed.
96
00:06:58.110 --> 00:07:02.250
The human job is to bring judgment clear thinking, and
97
00:07:02.329 --> 00:07:06.120
care about what is true in that conversation, in that context.
98
00:07:07.120 --> 00:07:10.560
So just as we are looking at the layer underneath
99
00:07:10.720 --> 00:07:13.980
all of that, the input. Input means that you put
100
00:07:14.040 --> 00:07:17.000
in before you ask for an answer. You can pause,
101
00:07:17.639 --> 00:07:21.319
ask a good answer, and build carefully. But if the
102
00:07:21.379 --> 00:07:25.079
information you give the AI is messy, you will still
103
00:07:25.379 --> 00:07:31.720
see a result that is confidently wrong. So today we
104
00:07:31.759 --> 00:07:36.079
are using one word for leaders who want answers they
105
00:07:36.100 --> 00:07:39.949
can trust, clean. You do not clean up your data
106
00:07:40.069 --> 00:07:44.009
after the answer, you get clean before you ask. So
107
00:07:44.290 --> 00:07:49.509
clean stands for catalog, label, eliminate, align, and name the gap.
108
00:07:49.769 --> 00:07:53.209
So these are five steps that help turn a confident
109
00:07:53.350 --> 00:07:57.250
answer into an answer you can trust. They work for
110
00:07:57.269 --> 00:08:01.009
your company and they work for your own thinking too. Now,
111
00:08:01.250 --> 00:08:05.449
before we start the first letter, let's name what is
112
00:08:05.509 --> 00:08:10.100
really about. What kinds of leaders in the age of AI?
113
00:08:10.920 --> 00:08:15.040
The first kind is the output chaser. This leader wants
114
00:08:15.120 --> 00:08:18.449
fast answer. They ask AI a question, get something that
115
00:08:18.490 --> 00:08:22.720
sounds smart, and believe it because it sounds good. But
116
00:08:22.920 --> 00:08:27.050
he is very good at sounding fresh. So the output
117
00:08:27.079 --> 00:08:30.550
chaser can have trouble telling the difference between a true
118
00:08:30.629 --> 00:08:34.529
answer and an answer that only sounds true. That was
119
00:08:34.570 --> 00:08:40.090
a nicker. Thursday, walking into the boardroom with a fancy
120
00:08:40.110 --> 00:08:47.340
chart and decimal point. The second kind is the input key. Now,
121
00:08:47.419 --> 00:08:51.059
this leader may use the same tools and the same AI,
122
00:08:51.360 --> 00:08:56.309
but they remember something important. When every answer looks polished,
123
00:08:56.970 --> 00:09:00.009
the best place to use your judgment is before the answer.
124
00:09:00.970 --> 00:09:04.009
They pay attention to what goes in, they know where
125
00:09:04.049 --> 00:09:07.429
the numbers come from, and they know which ideas have
126
00:09:07.509 --> 00:09:12.779
been checked and which ones are only guesses. They know
127
00:09:12.820 --> 00:09:17.110
which ideas. They may not be the fastest to any answer,
128
00:09:17.490 --> 00:09:20.149
but they are much faster to an answer that can
129
00:09:20.190 --> 00:09:23.289
survive in the real world, right? So the difference between
130
00:09:23.330 --> 00:09:26.929
these two mindsets is not who is smarter. It is
131
00:09:27.080 --> 00:09:31.279
who has a better technology budget. You think so?
132
00:09:31.500 --> 00:09:31.679
No.
133
00:09:31.960 --> 00:09:36.000
The difference is that one asks a few important questions
134
00:09:36.080 --> 00:09:39.759
before trusting the answer. And I know what some of
135
00:09:39.799 --> 00:09:42.110
you may be thinking. Our data will never be perfect.
136
00:09:43.029 --> 00:09:45.740
If you wait for the perfect data, we will never start.
137
00:09:46.120 --> 00:09:50.639
And I agree. I've been there. Perfect data does not exist.
138
00:09:51.679 --> 00:09:54.720
Waiting for it is just a way to avoid making decisions.
139
00:09:56.039 --> 00:09:59.179
But I'm not asking for perfect data. I'm asking you
140
00:09:59.210 --> 00:10:03.600
to know what you actually have. You can make decisions
141
00:10:04.320 --> 00:10:08.899
with imperfect information. What you cannot do is trust information
142
00:10:09.220 --> 00:10:12.840
when you have never checked where it is missing. So
143
00:10:13.159 --> 00:10:18.450
the real problem and the danger for leadership is not
144
00:10:18.529 --> 00:10:23.429
the messy data. The real danger is messy data that
145
00:10:23.450 --> 00:10:27.450
you think is clean. So the first letter, catalog, right?
146
00:10:27.769 --> 00:10:30.850
Catalog means that you make a clear list of what
147
00:10:30.889 --> 00:10:37.049
you actually have. It is not exciting work, but this
148
00:10:37.110 --> 00:10:39.529
is which is why many people skip it, right? So
149
00:10:39.580 --> 00:10:43.019
to catalog means to take an honest look at your information.
150
00:10:43.639 --> 00:10:46.100
Do not look at what you wish you have. Do
151
00:10:46.120 --> 00:10:49.730
not look at what a chart says you have. Look
152
00:10:49.759 --> 00:10:53.330
at what is really there. Ask simple questions. Where does
153
00:10:53.350 --> 00:10:56.629
this number come from? Who types it in? How often
154
00:10:56.669 --> 00:11:00.250
is it updated? What happens when someone skips it? Which
155
00:11:00.330 --> 00:11:04.889
boxes are really filled in and which ones are mostly blank? See,
156
00:11:05.210 --> 00:11:09.289
many companies have never checked this closely. They may have
157
00:11:09.580 --> 00:11:13.980
neat charts that show how data should work, but the
158
00:11:14.019 --> 00:11:17.730
real life is usually messy. The same thing is true
159
00:11:17.769 --> 00:11:21.309
for your own mind. Catalogue what is really in your head.
160
00:11:22.149 --> 00:11:25.360
Not just what is on your calendar, but what you
161
00:11:25.460 --> 00:11:29.759
are listening to and reading, watching, worrying about. Who is
162
00:11:29.779 --> 00:11:34.779
giving you that information? Where did your worries come from? See,
163
00:11:35.179 --> 00:11:41.129
many leaders carry worries without knowing where those worries started.
164
00:11:42.129 --> 00:11:46.009
Working with an operations director, Rohan, His team spent a
165
00:11:46.070 --> 00:11:50.409
whole year building AI tools using delivery data, but they
166
00:11:50.450 --> 00:11:53.889
had not checked the data first. When they finally did,
167
00:11:54.350 --> 00:11:58.970
it only took four days, one whiteboard session, no fancy technology.
168
00:11:59.909 --> 00:12:04.620
They found that most information, that's important delivery time box,
169
00:12:05.440 --> 00:12:09.940
was optional. Two of their six regions had never filled
170
00:12:10.159 --> 00:12:14.799
in the delivery time box. The system has been guessing
171
00:12:14.899 --> 00:12:20.889
an average number since 2021. So a full year of
172
00:12:20.950 --> 00:12:24.429
work while resting on a blank space that looked like
173
00:12:24.529 --> 00:12:29.029
real number. If they had made a clear list at
174
00:12:29.070 --> 00:12:31.929
the start, they could have saved a lot of time.
175
00:12:33.230 --> 00:12:36.690
The second letter in clean is L. It stands for label. Now,
176
00:12:36.730 --> 00:12:40.740
this step is about making sure words mean what everybody
177
00:12:40.840 --> 00:12:45.960
thinks they mean. Going back to Anika's problem, right? It
178
00:12:45.980 --> 00:12:50.179
was really a labeling problem. One word segment meant different
179
00:12:50.220 --> 00:12:52.279
things to different teams. Sales used it in one way,
180
00:12:52.299 --> 00:12:54.480
support using another way, marketing used it in another way.
181
00:12:55.159 --> 00:12:58.600
Everyone used the same word, so nobody noticed. They were
182
00:12:58.639 --> 00:13:03.059
talking about different things. And then the AI used that
183
00:13:03.139 --> 00:13:06.730
confusing word and treated it like it meant one clear thing.
184
00:13:07.409 --> 00:13:11.169
So to label honestly means to make sure Each important
185
00:13:11.230 --> 00:13:15.990
word has a clear meaning. So what does qualified lead mean?
186
00:13:16.789 --> 00:13:20.750
What does active customer mean? Active since when? What does
187
00:13:21.230 --> 00:13:24.960
done mean on your team? See, these may sound like
188
00:13:25.000 --> 00:13:29.100
small questions, but they're not small. They are the building
189
00:13:29.220 --> 00:13:33.919
blocks of bigger decisions. AI will really stop and ask,
190
00:13:34.639 --> 00:13:37.440
what do you mean this word in three ways, right?
191
00:13:37.600 --> 00:13:39.789
It will not. It will just use the word you
192
00:13:39.809 --> 00:13:42.950
gave it, the label you gave it. So you need
193
00:13:42.990 --> 00:13:46.669
to label honestly in your own mind too. Ask yourself,
194
00:13:47.289 --> 00:13:50.960
is this a fact or is it a fear? Is
195
00:13:50.980 --> 00:13:55.159
this really a decision or I'm just delaying? Is this
196
00:13:56.340 --> 00:13:59.820
person actually difficult or they are saying something I do
197
00:13:59.860 --> 00:14:03.139
not want to hear? See, a label you never check
198
00:14:04.370 --> 00:14:10.190
can quietly shape your choices for years. I'll share about Clara,
199
00:14:10.750 --> 00:14:13.750
another founder I work with. Her leadership team had argued
200
00:14:13.789 --> 00:14:17.929
for eight months about whether the company's growth has slowing down. Now,
201
00:14:18.129 --> 00:14:23.110
both sides on the table had numbers. Both have slides.
202
00:14:24.169 --> 00:14:28.029
They both have sides taught where they're right. Now, the
203
00:14:28.049 --> 00:14:31.570
problem was that they were using the word customer in
204
00:14:31.590 --> 00:14:34.929
two different ways. One group counted the customer from the
205
00:14:35.000 --> 00:14:38.029
day they signed up. The other group counted a customer
206
00:14:38.509 --> 00:14:41.509
from the day they first used the product. See, eight
207
00:14:41.529 --> 00:14:47.600
months of conflict came from one word that nobody clearly defined. Now,
208
00:14:47.840 --> 00:14:50.879
the third letter is E. It stands for eliminate. Now,
209
00:14:50.919 --> 00:14:55.919
this means choosing on purpose. What should not be included? Now,
210
00:14:56.360 --> 00:14:59.279
every organization, and I've been there 25 years, right? So
211
00:14:59.379 --> 00:15:04.059
every organization has missing information hiding inside the system. It
212
00:15:04.080 --> 00:15:06.679
can be copies of the same record. There may be
213
00:15:06.710 --> 00:15:09.629
test entries from a project years ago that no one deleted.
214
00:15:10.009 --> 00:15:12.250
There may be a pilot program data mixed in with
215
00:15:12.289 --> 00:15:15.970
real customer data. There may be notes written when someone
216
00:15:16.090 --> 00:15:21.100
was upset. There may be numbers from before the routes changed. Now,
217
00:15:21.320 --> 00:15:24.419
most of this was not done on purpose. It did
218
00:15:24.460 --> 00:15:27.230
not get built in one day. It built slowly like
219
00:15:27.330 --> 00:15:30.600
dust for years. For years, it may not have caused
220
00:15:30.620 --> 00:15:33.899
a big problem. But when you ask AI to find
221
00:15:34.019 --> 00:15:37.139
patterns in it, the AI may find patterns in the dust.
222
00:15:38.179 --> 00:15:44.750
So to eliminate the contamination means to choose what belongs where.
223
00:15:45.830 --> 00:15:48.210
Do not just give AI everything and hope it will
224
00:15:48.250 --> 00:15:52.789
figure things out. No. More information is not always better information.
225
00:15:53.830 --> 00:15:57.429
If misinformation goes in, it does not disappear. It gets
226
00:15:57.690 --> 00:16:01.500
mixed into the answers. The same thing happens with our
227
00:16:01.620 --> 00:16:06.700
own mind. When your inputs get messy too, think about
228
00:16:07.000 --> 00:16:11.139
scrolling through bad news before bed. Think about sitting in
229
00:16:11.179 --> 00:16:13.250
a meeting that has nothing to do with your work
230
00:16:13.529 --> 00:16:18.190
but still leaves you tense. Think about a co-worker passing
231
00:16:18.230 --> 00:16:23.090
their worry to you like it is useful information. You
232
00:16:23.289 --> 00:16:28.509
probably would not put broken records into an important computer system.
233
00:16:29.360 --> 00:16:35.139
But many of us put misinformation into our minds before
234
00:16:35.259 --> 00:16:40.960
the day has even started. Working with another executive, Tony,
235
00:16:41.559 --> 00:16:44.519
his team could not figure out why the AI forecast
236
00:16:44.600 --> 00:16:47.970
kept saying they would sell more than they really did.
237
00:16:49.000 --> 00:16:53.909
The answer was almost funny. Their old sales table still
238
00:16:53.990 --> 00:16:59.110
included every order from a free sample campaign in 2022.
239
00:16:59.500 --> 00:17:03.120
Thousands of people had taken something for free, but the
240
00:17:03.200 --> 00:17:07.299
system counted them as regular customers. So the AI learned
241
00:17:07.460 --> 00:17:11.839
that the market was much bigger than it really was. Now,
242
00:17:12.160 --> 00:17:17.700
two hours, literally two hours of removing the wrong data
243
00:17:17.960 --> 00:17:21.150
fixed a problem they had spent nine months trying to
244
00:17:21.190 --> 00:17:25.509
solve with more technology. The fourth letter A stands for align.
245
00:17:26.440 --> 00:17:30.579
Alignment's making sure information is connected to a real decision.
246
00:17:31.380 --> 00:17:36.319
This will save you from doing a lot of perfect-looking work.
247
00:17:36.099 --> 00:17:42.250
That actually helps anyone choose what will be next. Data
248
00:17:42.269 --> 00:17:46.670
is not useful just because it is complete. Data is
249
00:17:46.750 --> 00:17:51.190
useful when it helps people make decisions. If cleaning data
250
00:17:51.250 --> 00:17:54.730
becomes its own project, it can go on forever. There's
251
00:17:54.829 --> 00:17:58.839
always another box to fix. Another year to check. Another
252
00:17:58.859 --> 00:17:59.440
system to clean.
253
00:18:00.539 --> 00:18:04.700
I spent months and months and months and a lot
254
00:18:04.740 --> 00:18:09.140
of money getting AI ready without naming one clear decision.
255
00:18:10.140 --> 00:18:12.619
One clear decision they were trying to get ready for.
256
00:18:13.420 --> 00:18:18.599
And when they finish, nothing really changes. So to align
257
00:18:18.759 --> 00:18:23.369
means to start with the decision and work backwards. Name
258
00:18:23.390 --> 00:18:26.529
the real decision. Make it specific. Give it a due date.
259
00:18:26.890 --> 00:18:30.829
Know why it matters. Then ask, what information do we
260
00:18:30.930 --> 00:18:35.849
need to make this decision well? Clean only the information
261
00:18:36.309 --> 00:18:41.170
that touches that decision. This turns a never-ending project of
262
00:18:41.210 --> 00:18:44.029
data cleaning into a project where you have a clear
263
00:18:45.170 --> 00:18:49.730
finish line. Now, the same idea goes for our head too, right?
264
00:18:50.250 --> 00:18:54.839
Not everybody deserves your time. Ask, what decision does this
265
00:18:54.880 --> 00:18:58.599
help you make? If it does not help you make
266
00:18:58.660 --> 00:19:02.420
a real decision, it might just be mental noise. The
267
00:19:02.500 --> 00:19:05.140
one question can clear a lot of space in your mind.
268
00:19:06.559 --> 00:19:09.960
I was coaching her and her team had spent 11
269
00:19:09.960 --> 00:19:13.640
months trying to clean all their data. The process had
270
00:19:13.700 --> 00:19:17.309
no clear end and the team was one up. And
271
00:19:17.349 --> 00:19:21.589
I asked this one question to her. What decisions are
272
00:19:21.630 --> 00:19:24.759
you trying to make? She was honest. She said no
273
00:19:24.819 --> 00:19:28.640
one had asked that in her ear. So she named
274
00:19:28.900 --> 00:19:32.480
one pricing decision with one deadline. It turned out only
275
00:19:32.539 --> 00:19:35.519
needed four tables out of the 99 that they've tried
276
00:19:35.539 --> 00:19:39.430
to fix. They cleaned those four in three weeks, made
277
00:19:39.470 --> 00:19:43.849
a decision, and made it well. Other tables could wait
278
00:19:43.970 --> 00:19:47.170
until someone had a real question for them. And then
279
00:19:47.190 --> 00:19:50.299
the fifth letter is N. That stands for name the gap.
280
00:19:51.539 --> 00:19:54.680
If you forget the other four steps, remember just this one.
281
00:19:55.200 --> 00:20:00.019
Your information will never be perfect. And I agree that
282
00:20:00.420 --> 00:20:04.960
your information that you have will never be perfect. There
283
00:20:05.009 --> 00:20:09.309
will always be a region that did not report. There's
284
00:20:09.430 --> 00:20:12.890
always be a year before the system change or a
285
00:20:12.930 --> 00:20:16.339
box that only some teams fill in. See, that is normal.
286
00:20:16.400 --> 00:20:19.640
It does not mean you fail. The real problem is
287
00:20:19.720 --> 00:20:23.319
pretending the gap is not there. The real problem is
288
00:20:23.500 --> 00:20:26.309
letting a number move from a system to a slide,
289
00:20:26.369 --> 00:20:28.450
to a board meeting, to a big plan without telling
290
00:20:28.509 --> 00:20:32.869
people what is missing behind that. So to name the gap,
291
00:20:33.750 --> 00:20:36.970
out loud means to say warning at the same time
292
00:20:37.329 --> 00:20:41.339
you have the numbers. Do not hide it. Say it clearly.
293
00:20:41.839 --> 00:20:45.920
This is based on four of our six regions. Or
294
00:20:46.240 --> 00:20:49.660
this does not include anything before the definition changes. Or
295
00:20:49.960 --> 00:20:53.039
this box is optional, so this trend is helpful but
296
00:20:53.079 --> 00:20:58.259
not exact. See, one sentence can change everything. It helps
297
00:20:58.460 --> 00:21:03.119
people who are distant from your systems make a decision
298
00:21:03.460 --> 00:21:05.619
with their eyes open instead of guessing in the dark.
299
00:21:07.009 --> 00:21:09.289
So you should do this for yourself too, even in
300
00:21:09.329 --> 00:21:11.849
the front of your team. Say, I don't know. Say,
301
00:21:11.930 --> 00:21:15.410
I'm assuming this, but I have not checked. Say, this
302
00:21:15.490 --> 00:21:18.539
is what I think, but it is not proven. See,
303
00:21:18.930 --> 00:21:23.619
some leaders worry that saying what they do not know
304
00:21:24.099 --> 00:21:27.859
will make them look weird. It actually does the opposite.
305
00:21:28.119 --> 00:21:30.960
When people know you are honest about what you do
306
00:21:31.000 --> 00:21:35.349
not know, they are more likely to trust what you
307
00:21:35.369 --> 00:21:38.170
do know. So going back to the story I started
308
00:21:38.210 --> 00:21:41.869
with Anika. So here there is a story. After that
309
00:21:42.069 --> 00:21:45.849
quiet phone call, she did something brave. She went back
310
00:21:45.869 --> 00:21:48.769
to the same board with the same chart and named
311
00:21:48.809 --> 00:21:52.289
the gaps. She said this label means four different things.
312
00:21:52.809 --> 00:21:56.529
So this recommendation is not safe to use. Give me
313
00:21:56.569 --> 00:22:01.519
a few weeks and I'll come back. She literally thought
314
00:22:01.730 --> 00:22:05.890
people might lose trust in her. But what happened was
315
00:22:05.930 --> 00:22:10.170
just a puzzle. For the next two years, people trusted
316
00:22:10.230 --> 00:22:13.589
her numbers because they knew she would tell the truth
317
00:22:13.750 --> 00:22:18.880
about them. So in those six weeks, she used Clean.
318
00:22:20.339 --> 00:22:22.799
She made a list of what they had, fixed the labels,
319
00:22:22.859 --> 00:22:30.079
removed bad information, connected the work on to that one
320
00:22:30.119 --> 00:22:34.720
decision and said what was missing. So the answer they
321
00:22:34.779 --> 00:22:38.759
got at the end of what was different from beautiful
322
00:22:38.799 --> 00:22:41.640
first answer, but this time it was true. They used
323
00:22:41.700 --> 00:22:45.309
it and it worked. So let me make the line
324
00:22:45.349 --> 00:22:49.809
clear because we do this in every episode in this set, right?
325
00:22:49.829 --> 00:22:52.329
So when you are using AI to help you think,
326
00:22:53.369 --> 00:22:55.970
here is what AI should do. And here is what
327
00:22:56.039 --> 00:22:59.119
people still need to do. Give the machine the jobs.
328
00:22:59.960 --> 00:23:05.509
It is great at it. Sorting information, combining lists, finding
329
00:23:05.549 --> 00:23:09.450
repeat records, looking for patterns, walking through more rows than
330
00:23:09.490 --> 00:23:13.049
a person could read in a lifetime. AI can do
331
00:23:13.089 --> 00:23:18.160
these jobs quickly without getting tired. Now, once the information
332
00:23:18.200 --> 00:23:23.640
is clean enough, let AI help. But keep the most
333
00:23:23.799 --> 00:23:27.160
important jobs for people. People must decide what is true,
334
00:23:27.960 --> 00:23:32.730
what a particular definition is, what the information belongs, what
335
00:23:32.789 --> 00:23:36.009
decision this work is for. People also have to be
336
00:23:36.089 --> 00:23:41.549
honest about what they do not know. AI can process
337
00:23:41.630 --> 00:23:44.369
almost anything you give it and that is exactly why
338
00:23:44.410 --> 00:23:46.930
we must be careful. A person has to decide what
339
00:23:47.089 --> 00:23:50.619
should go into the machine in the first place. A
340
00:23:50.670 --> 00:23:53.960
person also has to tell the room when a number
341
00:23:53.980 --> 00:23:59.279
has a missing piece. So here's a new way I
342
00:23:59.319 --> 00:24:03.259
want you to think about this. In today's age of AI,
343
00:24:03.960 --> 00:24:09.480
almost everyone can get a smart sounding answer very fast, right?
344
00:24:10.089 --> 00:24:13.269
The answer is not the rare thing anymore. Answers can
345
00:24:13.309 --> 00:24:17.369
be quick, free, nice looking, whatever. What is rare now
346
00:24:17.509 --> 00:24:21.430
is being honest about what you gave the machine in
347
00:24:21.450 --> 00:24:26.289
the first place. The biggest question is not just, is
348
00:24:26.430 --> 00:24:30.960
this answer good? Many answers look good. The real question is,
349
00:24:31.930 --> 00:24:34.869
What did I put in? And do I really know
350
00:24:34.910 --> 00:24:39.730
where it came from? See, that is the work. Catalog, label, eliminate, align,
351
00:24:39.930 --> 00:24:43.220
and name the gap. That is why I want you
352
00:24:43.240 --> 00:24:45.380
to take Purpose Factor Assessment. It is free and takes
353
00:24:45.440 --> 00:24:49.740
about 25 minutes. It helps you to see what truly
354
00:24:49.779 --> 00:24:54.319
gives you energy, what often takes energy away, and what
355
00:24:54.460 --> 00:24:58.410
others make notice about you that you do not see yet.
356
00:24:58.430 --> 00:25:03.109
Think of it as using clean on yourself. You're cataloging
357
00:25:03.150 --> 00:25:07.069
what is really there, labeling it honestly, removing the noise,
358
00:25:07.509 --> 00:25:10.430
aligning your choices to what matters, and naming the gap
359
00:25:10.890 --> 00:25:14.920
that you have that you end up seeing through. So
360
00:25:15.200 --> 00:25:18.460
this week, try this. Make a list of where one
361
00:25:18.519 --> 00:25:22.859
important number comes from in your life, either work or personal.
362
00:25:22.880 --> 00:25:28.160
Make one important word clear. Remove one messy input. Connect
363
00:25:28.200 --> 00:25:32.170
the work to one real decision. and make one missing
364
00:25:32.250 --> 00:25:36.299
piece out loud, even if it is uncomfortable. If this
365
00:25:36.359 --> 00:25:38.980
episode helps you, share it with one leader who is
366
00:25:39.119 --> 00:25:42.460
trusting a beautiful chart, but has not checked where the
367
00:25:42.519 --> 00:25:45.039
information came from. You might save them a lot of
368
00:25:45.079 --> 00:25:48.440
time and take the purpose factor assessment, your next step
369
00:25:48.599 --> 00:25:51.740
in the show notes. Thank you for spending this time
370
00:25:51.779 --> 00:25:54.759
with me. Keep breathing, keep telling the truth about what
371
00:25:54.799 --> 00:25:59.009
you actually have. I'm saying You're listening to Mindful Leadership,
372
00:25:59.329 --> 00:26:02.210
Transforming Lives on the Transformation Network. I'll see you in
373
00:26:02.230 --> 00:26:03.390
the next episode. Thank you.
374
00:26:07.690 --> 00:26:11.759
Thank you for watching Mindful Leadership, Transforming Lives. With me,
375
00:26:11.819 --> 00:26:15.220
your host, Singh. We are honored to have you join
376
00:26:15.240 --> 00:26:19.559
us on this journey to explore the transformative power of mindfulness,
377
00:26:19.960 --> 00:26:26.640
emotional intelligence, and authentic leadership. Tune in every week on TransformationTalkRadio.com
378
00:26:26.720 --> 00:26:29.980
at noon Pacific on Tuesdays, where we will continue to
379
00:26:30.019 --> 00:26:33.779
explore how mindful leadership can create a ripple effect of
380
00:26:33.859 --> 00:26:37.720
positive changes in your life and lives of those you influence.
381
00:26:38.680 --> 00:26:43.269
This show is dedicated to helping you lead with purpose, presence,
382
00:26:43.430 --> 00:26:47.890
and passion. Whether you're navigating the complexities of business, building
383
00:26:47.930 --> 00:26:50.849
strong connection with your team, or striving to make a
384
00:26:50.910 --> 00:26:54.890
meaningful impact in the world around you, To learn more
385
00:26:54.930 --> 00:27:02.299
about the show, access additional resources or dive deeper, visit singhpowerupcoach.com. Remember,
386
00:27:02.880 --> 00:27:04.819
leadership isn't just about what you do.
387
00:27:05.500 --> 00:27:06.440
It's about who you are.
388
00:27:07.039 --> 00:27:10.869
Let's continue to transform lives together one mindful moment at
389
00:27:10.910 --> 00:27:11.230
a time.
390
00:27:12.210 --> 00:27:12.750
See you next week.
1
00:00:00.220 --> 00:00:03.980
Welcome to Mindful Leadership, Transforming Lives, with your host, Singh.
2
00:00:04.419 --> 00:00:07.540
This is where leadership meets purpose, presence, and passion. In
3
00:00:07.559 --> 00:00:11.720
today's fast-paced, high-pressure world, we face a leadership crisis. Too often,
4
00:00:12.099 --> 00:00:15.519
teams are led by managers who lack empathy, clarity, and vision,
5
00:00:16.039 --> 00:00:20.980
leaving individuals uninspired, disconnected, and undervalued. But it doesn't have
6
00:00:21.000 --> 00:00:23.390
to be this way. Great leadership isn't born, it's built.
7
00:00:23.750 --> 00:00:27.730
The endgame? Empower your leadership, inspire the world. Every week,
8
00:00:27.769 --> 00:00:30.510
join me, your host Singh, as we tackle the gaps
9
00:00:30.690 --> 00:00:35.329
in leadership and life coaching head-on, learning how mindfulness, emotional intelligence,
10
00:00:35.429 --> 00:00:40.130
and compassion can redefine success. This isn't just another podcast.
11
00:00:40.729 --> 00:00:43.229
It's a mission to fill the void led by an
12
00:00:43.270 --> 00:00:46.450
effective leadership. And it's not just about leadership in business.
13
00:00:46.929 --> 00:00:50.310
Life coaching for leaders and new executives is at heart
14
00:00:50.350 --> 00:00:53.490
of this journey. Ready to rise above the noise and
15
00:00:53.530 --> 00:00:56.850
lead with clarity, purpose, and impact? Join a global movement
16
00:00:57.259 --> 00:01:01.159
dedicated to reshaping the future of leadership one mindful moment.
17
00:01:01.020 --> 00:01:07.590
At a time. Let's begin. Welcome back to the Mindful
18
00:01:07.609 --> 00:01:11.969
Leadership Podcast on the Transformation Network. This is the show
19
00:01:12.030 --> 00:01:16.079
where we turn stress into plans, too much thinking into
20
00:01:16.109 --> 00:01:20.859
clear thinking, and big problems into next steps. I'm your host, Singh.
21
00:01:21.500 --> 00:01:27.700
I've seen a lot of fancy dashboards, give answers that
22
00:01:27.959 --> 00:01:31.450
look just right, but we're actually wrong in real life.
23
00:01:32.109 --> 00:01:35.209
So grab something warm to drink, put your phone face down,
24
00:01:35.569 --> 00:01:39.450
and let's spend the next few minutes talking about clean data,
25
00:01:39.909 --> 00:01:44.939
clear thinking, and AI. I want to open with a story,
26
00:01:45.099 --> 00:01:47.780
and I want you to listen for the moment you
27
00:01:47.799 --> 00:01:52.599
recognize yourself in it. Our Chief Operating Officer, Anika, called
28
00:01:52.620 --> 00:01:55.560
me on Thursday afternoon last week. She was excited. Her
29
00:01:55.599 --> 00:01:59.719
company had just turned on an AI tool that looked
30
00:02:00.439 --> 00:02:03.719
at four years of customer information. The email gave a
31
00:02:04.019 --> 00:02:07.959
clear answer. Stop spending money on one group of customers.
32
00:02:08.280 --> 00:02:11.719
Spend more on another group of customers. The chart looked good.
33
00:02:11.759 --> 00:02:15.340
The answer sounded exact. It even had a decimal point,
34
00:02:15.870 --> 00:02:20.889
which made it feel extra smart. Anika had already shown
35
00:02:20.930 --> 00:02:23.460
it to her co-workers. And they loved it because it
36
00:02:23.599 --> 00:02:27.879
looked like proof. And I was glad for her, genuinely.
37
00:02:28.400 --> 00:02:32.490
Because for years, her team had been arguing about this
38
00:02:32.669 --> 00:02:37.610
exact question with nothing but instincts and slight dicks. So
39
00:02:37.729 --> 00:02:41.789
I said, that's a real step forward. And then I
40
00:02:41.939 --> 00:02:46.879
asked her one question. Anika, where does the segment label
41
00:02:47.080 --> 00:02:52.219
actually come from? So over the next 10 days, she
42
00:02:52.280 --> 00:02:55.919
found the problem. Three different teams had used the same
43
00:02:55.979 --> 00:03:01.900
label in three different ways. Sales used it for deal size,
44
00:03:02.620 --> 00:03:08.069
support used it for customer risk, marketing used it for industry. Later,
45
00:03:08.509 --> 00:03:12.430
during a system move, some missing information had been filled
46
00:03:12.469 --> 00:03:16.129
in with the most common answer just to keep things clean.
47
00:03:17.330 --> 00:03:20.129
So the level was not really one clear customer group.
48
00:03:21.409 --> 00:03:26.240
It was several different ideas wearing the same name. The
49
00:03:26.340 --> 00:03:29.840
AI had not lied. It had done exactly what it
50
00:03:29.900 --> 00:03:34.219
was asked to do. But it was using bad information.
51
00:03:34.550 --> 00:03:38.810
So it gave back a bad answer in a beautiful
52
00:03:38.849 --> 00:03:42.389
chart with a lot of confidence. So here is the
53
00:03:42.409 --> 00:03:47.460
big idea. Artificial intelligence or augmented intelligence, AI, no matter
54
00:03:47.500 --> 00:03:52.400
how you take it, does not magically fix the messy information.
55
00:03:54.000 --> 00:03:57.139
AI just makes whatever you give it bigger, faster, and
56
00:03:57.219 --> 00:04:01.009
more believable. So if you give it good information, it
57
00:04:01.030 --> 00:04:04.289
can help you find good answers. But in this case,
58
00:04:04.430 --> 00:04:07.349
if you give it messy information, it may give you
59
00:04:07.449 --> 00:04:11.409
a messy answer that sounds very smart. This is what
60
00:04:11.509 --> 00:04:16.410
makes this new. and tricky. In the past, bad information
61
00:04:16.850 --> 00:04:20.290
often looked bad. A broken report looked broken. A missing
62
00:04:20.350 --> 00:04:26.750
spreadsheet looked empty. But now, a wrong answer can look polished.
63
00:04:27.649 --> 00:04:30.509
It can look like a clean dashboard even when the
64
00:04:30.600 --> 00:04:36.759
information underneath is not clean at all. So AI is
65
00:04:36.819 --> 00:04:40.959
not a fixer, it is an amplifier. feed it a mess,
66
00:04:41.379 --> 00:04:45.740
and it hands the mess back in a proper suit.
67
00:04:46.060 --> 00:04:49.860
Some people say that very few organizations are truly ready
68
00:04:49.899 --> 00:04:54.060
for AI. Now, whether the exact number is right or not,
69
00:04:54.430 --> 00:04:57.829
the lesson is clear. Being ready for AI is not
70
00:04:58.009 --> 00:05:02.370
only about technology. It's about honesty. It's about humanity. You
71
00:05:02.410 --> 00:05:06.230
have to be honest about what information you have, what
72
00:05:06.290 --> 00:05:09.899
it means, and what might be missing or wrong. And
73
00:05:10.180 --> 00:05:12.980
this is not just true for companies. It is true
74
00:05:13.019 --> 00:05:16.759
for solopreneurs, people who are using this. Your mind has
75
00:05:16.879 --> 00:05:24.009
inputs too. When finished conversation, worries you picked from someone else,
76
00:05:25.009 --> 00:05:29.029
stories you have not checked, but guesses that feel like facts.
77
00:05:30.470 --> 00:05:35.069
If you feel and feed all of that into your thinking,
78
00:05:35.449 --> 00:05:38.970
your choices can become cloudy too. You remember the old saying,
79
00:05:40.240 --> 00:05:43.540
Bad input leads to bad output. Now, the difference now
80
00:05:43.959 --> 00:05:48.500
is that the bad output can sound very confident. And
81
00:05:48.560 --> 00:05:52.519
that's where the problem is. It matters right now because
82
00:05:52.800 --> 00:05:57.360
many leaders are being pushed to use AI quickly. They're
83
00:05:57.379 --> 00:06:00.819
being asked to move faster, save time, make smarter choices.
84
00:06:01.920 --> 00:06:08.689
Speed without clean inputs just creates confident mistakes. A leader
85
00:06:08.699 --> 00:06:12.029
can move very fast and still move in the wrong direction.
86
00:06:12.509 --> 00:06:15.670
That is why this episode is not only about data.
87
00:06:15.990 --> 00:06:19.410
It is about slowing down just long enough to make
88
00:06:19.449 --> 00:06:24.250
sure the answer is built on something true. Folks, for
89
00:06:24.300 --> 00:06:27.120
the last four years, we have been building a way
90
00:06:27.160 --> 00:06:31.420
to lead well in the age of AI. In this
91
00:06:31.459 --> 00:06:37.269
set 14, the conscious operator, we started with humans. Hold, understand,
92
00:06:37.629 --> 00:06:41.170
mark the line, apply the leverage and never outsource the
93
00:06:41.209 --> 00:06:45.920
human side to AI. We talked about stopping the noise,
94
00:06:46.860 --> 00:06:50.899
asking the right question and building on something strong. Under
95
00:06:51.480 --> 00:06:56.420
all of that is one simple idea. AI gives everyone speed.
96
00:06:58.110 --> 00:07:02.250
The human job is to bring judgment clear thinking, and
97
00:07:02.329 --> 00:07:06.120
care about what is true in that conversation, in that context.
98
00:07:07.120 --> 00:07:10.560
So just as we are looking at the layer underneath
99
00:07:10.720 --> 00:07:13.980
all of that, the input. Input means that you put
100
00:07:14.040 --> 00:07:17.000
in before you ask for an answer. You can pause,
101
00:07:17.639 --> 00:07:21.319
ask a good answer, and build carefully. But if the
102
00:07:21.379 --> 00:07:25.079
information you give the AI is messy, you will still
103
00:07:25.379 --> 00:07:31.720
see a result that is confidently wrong. So today we
104
00:07:31.759 --> 00:07:36.079
are using one word for leaders who want answers they
105
00:07:36.100 --> 00:07:39.949
can trust, clean. You do not clean up your data
106
00:07:40.069 --> 00:07:44.009
after the answer, you get clean before you ask. So
107
00:07:44.290 --> 00:07:49.509
clean stands for catalog, label, eliminate, align, and name the gap.
108
00:07:49.769 --> 00:07:53.209
So these are five steps that help turn a confident
109
00:07:53.350 --> 00:07:57.250
answer into an answer you can trust. They work for
110
00:07:57.269 --> 00:08:01.009
your company and they work for your own thinking too. Now,
111
00:08:01.250 --> 00:08:05.449
before we start the first letter, let's name what is
112
00:08:05.509 --> 00:08:10.100
really about. What kinds of leaders in the age of AI?
113
00:08:10.920 --> 00:08:15.040
The first kind is the output chaser. This leader wants
114
00:08:15.120 --> 00:08:18.449
fast answer. They ask AI a question, get something that
115
00:08:18.490 --> 00:08:22.720
sounds smart, and believe it because it sounds good. But
116
00:08:22.920 --> 00:08:27.050
he is very good at sounding fresh. So the output
117
00:08:27.079 --> 00:08:30.550
chaser can have trouble telling the difference between a true
118
00:08:30.629 --> 00:08:34.529
answer and an answer that only sounds true. That was
119
00:08:34.570 --> 00:08:40.090
a nicker. Thursday, walking into the boardroom with a fancy
120
00:08:40.110 --> 00:08:47.340
chart and decimal point. The second kind is the input key. Now,
121
00:08:47.419 --> 00:08:51.059
this leader may use the same tools and the same AI,
122
00:08:51.360 --> 00:08:56.309
but they remember something important. When every answer looks polished,
123
00:08:56.970 --> 00:09:00.009
the best place to use your judgment is before the answer.
124
00:09:00.970 --> 00:09:04.009
They pay attention to what goes in, they know where
125
00:09:04.049 --> 00:09:07.429
the numbers come from, and they know which ideas have
126
00:09:07.509 --> 00:09:12.779
been checked and which ones are only guesses. They know
127
00:09:12.820 --> 00:09:17.110
which ideas. They may not be the fastest to any answer,
128
00:09:17.490 --> 00:09:20.149
but they are much faster to an answer that can
129
00:09:20.190 --> 00:09:23.289
survive in the real world, right? So the difference between
130
00:09:23.330 --> 00:09:26.929
these two mindsets is not who is smarter. It is
131
00:09:27.080 --> 00:09:31.279
who has a better technology budget. You think so?
132
00:09:31.500 --> 00:09:31.679
No.
133
00:09:31.960 --> 00:09:36.000
The difference is that one asks a few important questions
134
00:09:36.080 --> 00:09:39.759
before trusting the answer. And I know what some of
135
00:09:39.799 --> 00:09:42.110
you may be thinking. Our data will never be perfect.
136
00:09:43.029 --> 00:09:45.740
If you wait for the perfect data, we will never start.
137
00:09:46.120 --> 00:09:50.639
And I agree. I've been there. Perfect data does not exist.
138
00:09:51.679 --> 00:09:54.720
Waiting for it is just a way to avoid making decisions.
139
00:09:56.039 --> 00:09:59.179
But I'm not asking for perfect data. I'm asking you
140
00:09:59.210 --> 00:10:03.600
to know what you actually have. You can make decisions
141
00:10:04.320 --> 00:10:08.899
with imperfect information. What you cannot do is trust information
142
00:10:09.220 --> 00:10:12.840
when you have never checked where it is missing. So
143
00:10:13.159 --> 00:10:18.450
the real problem and the danger for leadership is not
144
00:10:18.529 --> 00:10:23.429
the messy data. The real danger is messy data that
145
00:10:23.450 --> 00:10:27.450
you think is clean. So the first letter, catalog, right?
146
00:10:27.769 --> 00:10:30.850
Catalog means that you make a clear list of what
147
00:10:30.889 --> 00:10:37.049
you actually have. It is not exciting work, but this
148
00:10:37.110 --> 00:10:39.529
is which is why many people skip it, right? So
149
00:10:39.580 --> 00:10:43.019
to catalog means to take an honest look at your information.
150
00:10:43.639 --> 00:10:46.100
Do not look at what you wish you have. Do
151
00:10:46.120 --> 00:10:49.730
not look at what a chart says you have. Look
152
00:10:49.759 --> 00:10:53.330
at what is really there. Ask simple questions. Where does
153
00:10:53.350 --> 00:10:56.629
this number come from? Who types it in? How often
154
00:10:56.669 --> 00:11:00.250
is it updated? What happens when someone skips it? Which
155
00:11:00.330 --> 00:11:04.889
boxes are really filled in and which ones are mostly blank? See,
156
00:11:05.210 --> 00:11:09.289
many companies have never checked this closely. They may have
157
00:11:09.580 --> 00:11:13.980
neat charts that show how data should work, but the
158
00:11:14.019 --> 00:11:17.730
real life is usually messy. The same thing is true
159
00:11:17.769 --> 00:11:21.309
for your own mind. Catalogue what is really in your head.
160
00:11:22.149 --> 00:11:25.360
Not just what is on your calendar, but what you
161
00:11:25.460 --> 00:11:29.759
are listening to and reading, watching, worrying about. Who is
162
00:11:29.779 --> 00:11:34.779
giving you that information? Where did your worries come from? See,
163
00:11:35.179 --> 00:11:41.129
many leaders carry worries without knowing where those worries started.
164
00:11:42.129 --> 00:11:46.009
Working with an operations director, Rohan, His team spent a
165
00:11:46.070 --> 00:11:50.409
whole year building AI tools using delivery data, but they
166
00:11:50.450 --> 00:11:53.889
had not checked the data first. When they finally did,
167
00:11:54.350 --> 00:11:58.970
it only took four days, one whiteboard session, no fancy technology.
168
00:11:59.909 --> 00:12:04.620
They found that most information, that's important delivery time box,
169
00:12:05.440 --> 00:12:09.940
was optional. Two of their six regions had never filled
170
00:12:10.159 --> 00:12:14.799
in the delivery time box. The system has been guessing
171
00:12:14.899 --> 00:12:20.889
an average number since 2021. So a full year of
172
00:12:20.950 --> 00:12:24.429
work while resting on a blank space that looked like
173
00:12:24.529 --> 00:12:29.029
real number. If they had made a clear list at
174
00:12:29.070 --> 00:12:31.929
the start, they could have saved a lot of time.
175
00:12:33.230 --> 00:12:36.690
The second letter in clean is L. It stands for label. Now,
176
00:12:36.730 --> 00:12:40.740
this step is about making sure words mean what everybody
177
00:12:40.840 --> 00:12:45.960
thinks they mean. Going back to Anika's problem, right? It
178
00:12:45.980 --> 00:12:50.179
was really a labeling problem. One word segment meant different
179
00:12:50.220 --> 00:12:52.279
things to different teams. Sales used it in one way,
180
00:12:52.299 --> 00:12:54.480
support using another way, marketing used it in another way.
181
00:12:55.159 --> 00:12:58.600
Everyone used the same word, so nobody noticed. They were
182
00:12:58.639 --> 00:13:03.059
talking about different things. And then the AI used that
183
00:13:03.139 --> 00:13:06.730
confusing word and treated it like it meant one clear thing.
184
00:13:07.409 --> 00:13:11.169
So to label honestly means to make sure Each important
185
00:13:11.230 --> 00:13:15.990
word has a clear meaning. So what does qualified lead mean?
186
00:13:16.789 --> 00:13:20.750
What does active customer mean? Active since when? What does
187
00:13:21.230 --> 00:13:24.960
done mean on your team? See, these may sound like
188
00:13:25.000 --> 00:13:29.100
small questions, but they're not small. They are the building
189
00:13:29.220 --> 00:13:33.919
blocks of bigger decisions. AI will really stop and ask,
190
00:13:34.639 --> 00:13:37.440
what do you mean this word in three ways, right?
191
00:13:37.600 --> 00:13:39.789
It will not. It will just use the word you
192
00:13:39.809 --> 00:13:42.950
gave it, the label you gave it. So you need
193
00:13:42.990 --> 00:13:46.669
to label honestly in your own mind too. Ask yourself,
194
00:13:47.289 --> 00:13:50.960
is this a fact or is it a fear? Is
195
00:13:50.980 --> 00:13:55.159
this really a decision or I'm just delaying? Is this
196
00:13:56.340 --> 00:13:59.820
person actually difficult or they are saying something I do
197
00:13:59.860 --> 00:14:03.139
not want to hear? See, a label you never check
198
00:14:04.370 --> 00:14:10.190
can quietly shape your choices for years. I'll share about Clara,
199
00:14:10.750 --> 00:14:13.750
another founder I work with. Her leadership team had argued
200
00:14:13.789 --> 00:14:17.929
for eight months about whether the company's growth has slowing down. Now,
201
00:14:18.129 --> 00:14:23.110
both sides on the table had numbers. Both have slides.
202
00:14:24.169 --> 00:14:28.029
They both have sides taught where they're right. Now, the
203
00:14:28.049 --> 00:14:31.570
problem was that they were using the word customer in
204
00:14:31.590 --> 00:14:34.929
two different ways. One group counted the customer from the
205
00:14:35.000 --> 00:14:38.029
day they signed up. The other group counted a customer
206
00:14:38.509 --> 00:14:41.509
from the day they first used the product. See, eight
207
00:14:41.529 --> 00:14:47.600
months of conflict came from one word that nobody clearly defined. Now,
208
00:14:47.840 --> 00:14:50.879
the third letter is E. It stands for eliminate. Now,
209
00:14:50.919 --> 00:14:55.919
this means choosing on purpose. What should not be included? Now,
210
00:14:56.360 --> 00:14:59.279
every organization, and I've been there 25 years, right? So
211
00:14:59.379 --> 00:15:04.059
every organization has missing information hiding inside the system. It
212
00:15:04.080 --> 00:15:06.679
can be copies of the same record. There may be
213
00:15:06.710 --> 00:15:09.629
test entries from a project years ago that no one deleted.
214
00:15:10.009 --> 00:15:12.250
There may be a pilot program data mixed in with
215
00:15:12.289 --> 00:15:15.970
real customer data. There may be notes written when someone
216
00:15:16.090 --> 00:15:21.100
was upset. There may be numbers from before the routes changed. Now,
217
00:15:21.320 --> 00:15:24.419
most of this was not done on purpose. It did
218
00:15:24.460 --> 00:15:27.230
not get built in one day. It built slowly like
219
00:15:27.330 --> 00:15:30.600
dust for years. For years, it may not have caused
220
00:15:30.620 --> 00:15:33.899
a big problem. But when you ask AI to find
221
00:15:34.019 --> 00:15:37.139
patterns in it, the AI may find patterns in the dust.
222
00:15:38.179 --> 00:15:44.750
So to eliminate the contamination means to choose what belongs where.
223
00:15:45.830 --> 00:15:48.210
Do not just give AI everything and hope it will
224
00:15:48.250 --> 00:15:52.789
figure things out. No. More information is not always better information.
225
00:15:53.830 --> 00:15:57.429
If misinformation goes in, it does not disappear. It gets
226
00:15:57.690 --> 00:16:01.500
mixed into the answers. The same thing happens with our
227
00:16:01.620 --> 00:16:06.700
own mind. When your inputs get messy too, think about
228
00:16:07.000 --> 00:16:11.139
scrolling through bad news before bed. Think about sitting in
229
00:16:11.179 --> 00:16:13.250
a meeting that has nothing to do with your work
230
00:16:13.529 --> 00:16:18.190
but still leaves you tense. Think about a co-worker passing
231
00:16:18.230 --> 00:16:23.090
their worry to you like it is useful information. You
232
00:16:23.289 --> 00:16:28.509
probably would not put broken records into an important computer system.
233
00:16:29.360 --> 00:16:35.139
But many of us put misinformation into our minds before
234
00:16:35.259 --> 00:16:40.960
the day has even started. Working with another executive, Tony,
235
00:16:41.559 --> 00:16:44.519
his team could not figure out why the AI forecast
236
00:16:44.600 --> 00:16:47.970
kept saying they would sell more than they really did.
237
00:16:49.000 --> 00:16:53.909
The answer was almost funny. Their old sales table still
238
00:16:53.990 --> 00:16:59.110
included every order from a free sample campaign in 2022.
239
00:16:59.500 --> 00:17:03.120
Thousands of people had taken something for free, but the
240
00:17:03.200 --> 00:17:07.299
system counted them as regular customers. So the AI learned
241
00:17:07.460 --> 00:17:11.839
that the market was much bigger than it really was. Now,
242
00:17:12.160 --> 00:17:17.700
two hours, literally two hours of removing the wrong data
243
00:17:17.960 --> 00:17:21.150
fixed a problem they had spent nine months trying to
244
00:17:21.190 --> 00:17:25.509
solve with more technology. The fourth letter A stands for align.
245
00:17:26.440 --> 00:17:30.579
Alignment's making sure information is connected to a real decision.
246
00:17:31.380 --> 00:17:36.319
This will save you from doing a lot of perfect-looking work.
247
00:17:36.099 --> 00:17:42.250
That actually helps anyone choose what will be next. Data
248
00:17:42.269 --> 00:17:46.670
is not useful just because it is complete. Data is
249
00:17:46.750 --> 00:17:51.190
useful when it helps people make decisions. If cleaning data
250
00:17:51.250 --> 00:17:54.730
becomes its own project, it can go on forever. There's
251
00:17:54.829 --> 00:17:58.839
always another box to fix. Another year to check. Another
252
00:17:58.859 --> 00:17:59.440
system to clean.
253
00:18:00.539 --> 00:18:04.700
I spent months and months and months and a lot
254
00:18:04.740 --> 00:18:09.140
of money getting AI ready without naming one clear decision.
255
00:18:10.140 --> 00:18:12.619
One clear decision they were trying to get ready for.
256
00:18:13.420 --> 00:18:18.599
And when they finish, nothing really changes. So to align
257
00:18:18.759 --> 00:18:23.369
means to start with the decision and work backwards. Name
258
00:18:23.390 --> 00:18:26.529
the real decision. Make it specific. Give it a due date.
259
00:18:26.890 --> 00:18:30.829
Know why it matters. Then ask, what information do we
260
00:18:30.930 --> 00:18:35.849
need to make this decision well? Clean only the information
261
00:18:36.309 --> 00:18:41.170
that touches that decision. This turns a never-ending project of
262
00:18:41.210 --> 00:18:44.029
data cleaning into a project where you have a clear
263
00:18:45.170 --> 00:18:49.730
finish line. Now, the same idea goes for our head too, right?
264
00:18:50.250 --> 00:18:54.839
Not everybody deserves your time. Ask, what decision does this
265
00:18:54.880 --> 00:18:58.599
help you make? If it does not help you make
266
00:18:58.660 --> 00:19:02.420
a real decision, it might just be mental noise. The
267
00:19:02.500 --> 00:19:05.140
one question can clear a lot of space in your mind.
268
00:19:06.559 --> 00:19:09.960
I was coaching her and her team had spent 11
269
00:19:09.960 --> 00:19:13.640
months trying to clean all their data. The process had
270
00:19:13.700 --> 00:19:17.309
no clear end and the team was one up. And
271
00:19:17.349 --> 00:19:21.589
I asked this one question to her. What decisions are
272
00:19:21.630 --> 00:19:24.759
you trying to make? She was honest. She said no
273
00:19:24.819 --> 00:19:28.640
one had asked that in her ear. So she named
274
00:19:28.900 --> 00:19:32.480
one pricing decision with one deadline. It turned out only
275
00:19:32.539 --> 00:19:35.519
needed four tables out of the 99 that they've tried
276
00:19:35.539 --> 00:19:39.430
to fix. They cleaned those four in three weeks, made
277
00:19:39.470 --> 00:19:43.849
a decision, and made it well. Other tables could wait
278
00:19:43.970 --> 00:19:47.170
until someone had a real question for them. And then
279
00:19:47.190 --> 00:19:50.299
the fifth letter is N. That stands for name the gap.
280
00:19:51.539 --> 00:19:54.680
If you forget the other four steps, remember just this one.
281
00:19:55.200 --> 00:20:00.019
Your information will never be perfect. And I agree that
282
00:20:00.420 --> 00:20:04.960
your information that you have will never be perfect. There
283
00:20:05.009 --> 00:20:09.309
will always be a region that did not report. There's
284
00:20:09.430 --> 00:20:12.890
always be a year before the system change or a
285
00:20:12.930 --> 00:20:16.339
box that only some teams fill in. See, that is normal.
286
00:20:16.400 --> 00:20:19.640
It does not mean you fail. The real problem is
287
00:20:19.720 --> 00:20:23.319
pretending the gap is not there. The real problem is
288
00:20:23.500 --> 00:20:26.309
letting a number move from a system to a slide,
289
00:20:26.369 --> 00:20:28.450
to a board meeting, to a big plan without telling
290
00:20:28.509 --> 00:20:32.869
people what is missing behind that. So to name the gap,
291
00:20:33.750 --> 00:20:36.970
out loud means to say warning at the same time
292
00:20:37.329 --> 00:20:41.339
you have the numbers. Do not hide it. Say it clearly.
293
00:20:41.839 --> 00:20:45.920
This is based on four of our six regions. Or
294
00:20:46.240 --> 00:20:49.660
this does not include anything before the definition changes. Or
295
00:20:49.960 --> 00:20:53.039
this box is optional, so this trend is helpful but
296
00:20:53.079 --> 00:20:58.259
not exact. See, one sentence can change everything. It helps
297
00:20:58.460 --> 00:21:03.119
people who are distant from your systems make a decision
298
00:21:03.460 --> 00:21:05.619
with their eyes open instead of guessing in the dark.
299
00:21:07.009 --> 00:21:09.289
So you should do this for yourself too, even in
300
00:21:09.329 --> 00:21:11.849
the front of your team. Say, I don't know. Say,
301
00:21:11.930 --> 00:21:15.410
I'm assuming this, but I have not checked. Say, this
302
00:21:15.490 --> 00:21:18.539
is what I think, but it is not proven. See,
303
00:21:18.930 --> 00:21:23.619
some leaders worry that saying what they do not know
304
00:21:24.099 --> 00:21:27.859
will make them look weird. It actually does the opposite.
305
00:21:28.119 --> 00:21:30.960
When people know you are honest about what you do
306
00:21:31.000 --> 00:21:35.349
not know, they are more likely to trust what you
307
00:21:35.369 --> 00:21:38.170
do know. So going back to the story I started
308
00:21:38.210 --> 00:21:41.869
with Anika. So here there is a story. After that
309
00:21:42.069 --> 00:21:45.849
quiet phone call, she did something brave. She went back
310
00:21:45.869 --> 00:21:48.769
to the same board with the same chart and named
311
00:21:48.809 --> 00:21:52.289
the gaps. She said this label means four different things.
312
00:21:52.809 --> 00:21:56.529
So this recommendation is not safe to use. Give me
313
00:21:56.569 --> 00:22:01.519
a few weeks and I'll come back. She literally thought
314
00:22:01.730 --> 00:22:05.890
people might lose trust in her. But what happened was
315
00:22:05.930 --> 00:22:10.170
just a puzzle. For the next two years, people trusted
316
00:22:10.230 --> 00:22:13.589
her numbers because they knew she would tell the truth
317
00:22:13.750 --> 00:22:18.880
about them. So in those six weeks, she used Clean.
318
00:22:20.339 --> 00:22:22.799
She made a list of what they had, fixed the labels,
319
00:22:22.859 --> 00:22:30.079
removed bad information, connected the work on to that one
320
00:22:30.119 --> 00:22:34.720
decision and said what was missing. So the answer they
321
00:22:34.779 --> 00:22:38.759
got at the end of what was different from beautiful
322
00:22:38.799 --> 00:22:41.640
first answer, but this time it was true. They used
323
00:22:41.700 --> 00:22:45.309
it and it worked. So let me make the line
324
00:22:45.349 --> 00:22:49.809
clear because we do this in every episode in this set, right?
325
00:22:49.829 --> 00:22:52.329
So when you are using AI to help you think,
326
00:22:53.369 --> 00:22:55.970
here is what AI should do. And here is what
327
00:22:56.039 --> 00:22:59.119
people still need to do. Give the machine the jobs.
328
00:22:59.960 --> 00:23:05.509
It is great at it. Sorting information, combining lists, finding
329
00:23:05.549 --> 00:23:09.450
repeat records, looking for patterns, walking through more rows than
330
00:23:09.490 --> 00:23:13.049
a person could read in a lifetime. AI can do
331
00:23:13.089 --> 00:23:18.160
these jobs quickly without getting tired. Now, once the information
332
00:23:18.200 --> 00:23:23.640
is clean enough, let AI help. But keep the most
333
00:23:23.799 --> 00:23:27.160
important jobs for people. People must decide what is true,
334
00:23:27.960 --> 00:23:32.730
what a particular definition is, what the information belongs, what
335
00:23:32.789 --> 00:23:36.009
decision this work is for. People also have to be
336
00:23:36.089 --> 00:23:41.549
honest about what they do not know. AI can process
337
00:23:41.630 --> 00:23:44.369
almost anything you give it and that is exactly why
338
00:23:44.410 --> 00:23:46.930
we must be careful. A person has to decide what
339
00:23:47.089 --> 00:23:50.619
should go into the machine in the first place. A
340
00:23:50.670 --> 00:23:53.960
person also has to tell the room when a number
341
00:23:53.980 --> 00:23:59.279
has a missing piece. So here's a new way I
342
00:23:59.319 --> 00:24:03.259
want you to think about this. In today's age of AI,
343
00:24:03.960 --> 00:24:09.480
almost everyone can get a smart sounding answer very fast, right?
344
00:24:10.089 --> 00:24:13.269
The answer is not the rare thing anymore. Answers can
345
00:24:13.309 --> 00:24:17.369
be quick, free, nice looking, whatever. What is rare now
346
00:24:17.509 --> 00:24:21.430
is being honest about what you gave the machine in
347
00:24:21.450 --> 00:24:26.289
the first place. The biggest question is not just, is
348
00:24:26.430 --> 00:24:30.960
this answer good? Many answers look good. The real question is,
349
00:24:31.930 --> 00:24:34.869
What did I put in? And do I really know
350
00:24:34.910 --> 00:24:39.730
where it came from? See, that is the work. Catalog, label, eliminate, align,
351
00:24:39.930 --> 00:24:43.220
and name the gap. That is why I want you
352
00:24:43.240 --> 00:24:45.380
to take Purpose Factor Assessment. It is free and takes
353
00:24:45.440 --> 00:24:49.740
about 25 minutes. It helps you to see what truly
354
00:24:49.779 --> 00:24:54.319
gives you energy, what often takes energy away, and what
355
00:24:54.460 --> 00:24:58.410
others make notice about you that you do not see yet.
356
00:24:58.430 --> 00:25:03.109
Think of it as using clean on yourself. You're cataloging
357
00:25:03.150 --> 00:25:07.069
what is really there, labeling it honestly, removing the noise,
358
00:25:07.509 --> 00:25:10.430
aligning your choices to what matters, and naming the gap
359
00:25:10.890 --> 00:25:14.920
that you have that you end up seeing through. So
360
00:25:15.200 --> 00:25:18.460
this week, try this. Make a list of where one
361
00:25:18.519 --> 00:25:22.859
important number comes from in your life, either work or personal.
362
00:25:22.880 --> 00:25:28.160
Make one important word clear. Remove one messy input. Connect
363
00:25:28.200 --> 00:25:32.170
the work to one real decision. and make one missing
364
00:25:32.250 --> 00:25:36.299
piece out loud, even if it is uncomfortable. If this
365
00:25:36.359 --> 00:25:38.980
episode helps you, share it with one leader who is
366
00:25:39.119 --> 00:25:42.460
trusting a beautiful chart, but has not checked where the
367
00:25:42.519 --> 00:25:45.039
information came from. You might save them a lot of
368
00:25:45.079 --> 00:25:48.440
time and take the purpose factor assessment, your next step
369
00:25:48.599 --> 00:25:51.740
in the show notes. Thank you for spending this time
370
00:25:51.779 --> 00:25:54.759
with me. Keep breathing, keep telling the truth about what
371
00:25:54.799 --> 00:25:59.009
you actually have. I'm saying You're listening to Mindful Leadership,
372
00:25:59.329 --> 00:26:02.210
Transforming Lives on the Transformation Network. I'll see you in
373
00:26:02.230 --> 00:26:03.390
the next episode. Thank you.
374
00:26:07.690 --> 00:26:11.759
Thank you for watching Mindful Leadership, Transforming Lives. With me,
375
00:26:11.819 --> 00:26:15.220
your host, Singh. We are honored to have you join
376
00:26:15.240 --> 00:26:19.559
us on this journey to explore the transformative power of mindfulness,
377
00:26:19.960 --> 00:26:26.640
emotional intelligence, and authentic leadership. Tune in every week on TransformationTalkRadio.com
378
00:26:26.720 --> 00:26:29.980
at noon Pacific on Tuesdays, where we will continue to
379
00:26:30.019 --> 00:26:33.779
explore how mindful leadership can create a ripple effect of
380
00:26:33.859 --> 00:26:37.720
positive changes in your life and lives of those you influence.
381
00:26:38.680 --> 00:26:43.269
This show is dedicated to helping you lead with purpose, presence,
382
00:26:43.430 --> 00:26:47.890
and passion. Whether you're navigating the complexities of business, building
383
00:26:47.930 --> 00:26:50.849
strong connection with your team, or striving to make a
384
00:26:50.910 --> 00:26:54.890
meaningful impact in the world around you, To learn more
385
00:26:54.930 --> 00:27:02.299
about the show, access additional resources or dive deeper, visit singhpowerupcoach.com. Remember,
386
00:27:02.880 --> 00:27:04.819
leadership isn't just about what you do.
387
00:27:05.500 --> 00:27:06.440
It's about who you are.
388
00:27:07.039 --> 00:27:10.869
Let's continue to transform lives together one mindful moment at
389
00:27:10.910 --> 00:27:11.230
a time.
390
00:27:12.210 --> 00:27:12.750
See you next week.
