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Winning with Artificial Intelligence · Global Business insights

• 0:05
  SPEAKER_00: Winning with artificial intelligence will not just happen even after making the required investment in new tools.
• 0:13
  SPEAKER_00: Tosin Fachi, a results-focused supply chain leader with global experience, speaks on getting it right.
• 0:23
  SPEAKER_01: The question is not if AI would change the world.
• 0:29
  SPEAKER_01: The real question is do we have the wisdom and the boldness and the humility to change along with it?
• 0:39
  SPEAKER_01: For the past three years, artificial intelligence has been presented as the best technology transformation of our generation and probably for all time.
• 0:51
  SPEAKER_01: We are told AI will revolutionize business, transform productivity, reduce costs, create new industries never seen before, change the way we work.
• 1:04
  SPEAKER_01: And to be fair, much of this excitement was justified.
• 1:11
  SPEAKER_01: For the first time, millions of people could interact with machines capable of writing, analyzing, summarizing, reasoning and generating ideas real time.
• 1:23
  SPEAKER_01: The possibility seemed endless and unlimited.
• 1:27
  SPEAKER_01: Companies rushed to AI strategies.
• 1:31
  SPEAKER_01: Executives were falling all over themselves trying to say they are using AI.
• 1:38
  SPEAKER_01: Employees were mandated and coerced to use AI.
• 1:44
  SPEAKER_01: AI pilots and agents appeared everywhere.
• 1:48
  SPEAKER_01: Then something happened.
• 1:51
  SPEAKER_01: The question changed from what can AI do to what is the return on investment?
• 1:58
  SPEAKER_01: What are the savings?
• 2:00
  SPEAKER_01: What are the productivity?
• 2:03
  SPEAKER_01: Why are still many projects still in better or better phase?
• 2:08
  SPEAKER_01: And why are companies spending millions on AI and are still struggling to show tangible benefits?
• 2:19
  SPEAKER_01: So here's the question that I'm gonna answer again.
• 2:24
  SPEAKER_01: Is the AI on the moon over?
• 2:27
  SPEAKER_01: My answer is perhaps it is.
• 2:30
  SPEAKER_01: I don't believe the end of AI on the moon necessarily means it's bad news.
• 2:36
  SPEAKER_01: You see, at the end of AI Honey Moon may really represent something far more important, and that is the maturation of AI.
• 2:48
  SPEAKER_01: Now to understand why, there are a few topics we'll talk about.
• 2:54
  SPEAKER_01: The first one is what happened to the AI on the moon?
• 2:57
  SPEAKER_01: What's the story?
• 2:59
  SPEAKER_01: Secondly, why are organizations expressing a reality check?
• 3:04
  SPEAKER_01: Then thirdly, the fundamental mistakes many organizations make.
• 3:11
  SPEAKER_01: And then we'll talk about what needs to change.
• 3:13
  SPEAKER_01: What's the different way of looking at things?
• 3:17
  SPEAKER_01: Now, everything we have starts with imagination, so is technology revolution.
• 3:24
  SPEAKER_01: Every technology revolution starts with imagination.
• 3:29
  SPEAKER_01: For example, when internet technology came in from the late 90s, people imagined an interconnected world.
• 3:39
  SPEAKER_01: When mobile technology came, people imagined a digital world in our pockets, an entire digital world available in our pockets.
• 3:51
  SPEAKER_01: Now, when generative AI exploded into our consciousness, we imagined a world where intelligence can be available on demand.
• 4:02
  SPEAKER_01: Now, this excitement, this anticipation created enormous momentum, and for a good reason.
• 4:10
  SPEAKER_01: Organizations began asking, how quickly can we adopt AI?
• 4:15
  SPEAKER_01: How many AI tools can we purchase?
• 4:18
  SPEAKER_01: How many employees are adopting AI?
• 4:21
  SPEAKER_01: How many pilots can we launch?
• 4:24
  SPEAKER_01: And these questions are understandable because no organization wanted to be left behind in this great thing.
• 4:32
  SPEAKER_01: Nobody wanted to be the company that ignored the internet.
• 4:37
  SPEAKER_01: Nobody wanted to become the company that missed the mobile technology.
• 4:42
  SPEAKER_01: Certainly, nobody wants to be the company that ignored AI or missed it.
• 4:48
  SPEAKER_01: So they moved quickly.
• 4:50
  SPEAKER_01: They moved with a great sense of urgency.
• 4:53
  SPEAKER_01: But in that rush, something happened, which happens every time.
• 4:59
  SPEAKER_01: People began confusing activity with progress.
• 5:07
  SPEAKER_01: A company can announce 20 AI pilots and call it innovation.
• 5:15
  SPEAKER_01: A company can buy thousands of AI licenses and be termed as technologically advanced.
• 5:23
  SPEAKER_01: A company can report that 70% of their employees are using AI, which is a very high adoption rate, and say they are being transformed.
• 5:33
  SPEAKER_01: But here's a fundamental problem: usage is not value.
• 5:41
  SPEAKER_01: Adoption is not transformation.
• 5:45
  SPEAKER_01: You can be extremely busy with AI and create little value.
• 5:52
  SPEAKER_01: We want to move from AI adoption to AI value.
• 5:59
  SPEAKER_01: This is not an abandonment of AI.
• 6:02
  SPEAKER_01: Rather, it's a reset in what we see with AI.
• 6:07
  SPEAKER_01: And that reset is set in four principles I'll talk about.
• 6:13
  SPEAKER_01: The first one is start with the problem and not the technology.
• 6:19
  SPEAKER_01: So when you look at your your value stream or your workflow, the first set of questions you ask yourself is where's the leakage?
• 6:27
  SPEAKER_01: What is the problem statement?
• 6:29
  SPEAKER_01: Where why is where why do we have a problem?
• 6:34
  SPEAKER_01: Why is it taking so long to go through this process?
• 6:37
  SPEAKER_01: The interesting is when you go through those questions, they start discovering the root cause of the problem.
• 6:45
  SPEAKER_01: Leave technology aside.
• 6:47
  SPEAKER_01: Focus on the problem statement or the value statement.
• 6:53
  SPEAKER_01: Then the second part, you just don't say, identify what's the problem.
• 6:57
  SPEAKER_01: You need to redesign the workflow.
• 6:59
  SPEAKER_01: You need to remove inefficiencies from the value chain.
• 7:04
  SPEAKER_01: You need to transform the process.
• 7:07
  SPEAKER_01: AI works best when you have a transformed process.
• 7:11
  SPEAKER_01: AI can expose defects in the system, but to get the best out of AI, you need to transform the process first, then adopt AI later.
• 7:25
  SPEAKER_01: Then thirdly, you need to measure outcomes, not usage.
• 7:30
  SPEAKER_01: All this, oh, there's an 80% adoption.
• 7:34
  SPEAKER_01: They are good metrics, but they're not outcomes.
• 7:37
  SPEAKER_01: 80% of your employees using AI does not mean transformation.
• 7:43
  SPEAKER_01: It just means that they are using it, that people are complying.
• 7:47
  SPEAKER_01: Compliance doesn't mean transformation.
• 7:49
  SPEAKER_01: Compliance is important on the path of transformation, but that doesn't mean transformation.
• 7:55
  SPEAKER_01: Keep human judgment where it matters.
• 7:59
  SPEAKER_01: Some things in our process still requires human intervention.
• 8:03
  SPEAKER_01: Keep it there.
• 8:04
  SPEAKER_01: Don't force AI into a space where human judgment is required.
• 8:10
  SPEAKER_01: And ultimately the winning goal is human judgment we've multiplied by AI capacity.
• 8:18
  SPEAKER_01: The AI on the moon began to change when executives started asking the question they should ask, which is a reasonable question, and that is what are we getting for our investment?
• 8:31
  SPEAKER_01: We've spent a lot of money in this technology, we know it works, we know it's efficient, but how does it affect our bottom line?
• 8:40
  SPEAKER_01: And then the conversation became more complicated.
• 8:44
  SPEAKER_01: Because while individuals, and I speak for myself personally, are becoming more productive with AI, organizations struggle to turn individual productivity into enterprise value.
• 8:58
  SPEAKER_01: Let's give an example.
• 9:01
  SPEAKER_01: For example, an employee who can write draft a quick email that could take 10 minutes in three minutes.
• 9:11
  SPEAKER_01: That's efficient, that's productive.
• 9:14
  SPEAKER_01: But here's the question: did the organization lose reduce costs as a result of that?
• 9:21
  SPEAKER_01: Did they increase revenue?
• 9:23
  SPEAKER_01: Did they improve customer satisfaction?
• 9:26
  SPEAKER_01: Did they increase capacity?
• 9:28
  SPEAKER_01: Did they emit unnecessary work?
• 9:31
  SPEAKER_01: Now the remaining seven minutes the employee saved.
• 9:37
  SPEAKER_01: Did he or she use it for something else?
• 9:40
  SPEAKER_01: More productive.
• 9:41
  SPEAKER_01: So this is an important distinction.
• 9:44
  SPEAKER_01: Individual productivity does not translate automatically to organizational productivity.
• 9:52
  SPEAKER_01: For organization to capture value, there must be a system, it has to be at the systemic level.
• 10:00
  SPEAKER_01: Efficient processes, cost must be reduced, bottleneck must be reduced, capacity must increase, revenue must grow, the risk must be managed.
• 10:13
  SPEAKER_01: Because the benefit might be real but an economically invisible.
• 10:19
  SPEAKER_01: Mark my words, real benefit, but economically it's invisible.
• 10:25
  SPEAKER_01: And that's why organizations encounter what I can call value gap.
• 10:32
  SPEAKER_01: Because somewhere between the pilot and the income statement, the value is missing.
• 10:40
  SPEAKER_01: Why?
• 10:41
  SPEAKER_01: Because enterprise transformation is not easy.
• 10:45
  SPEAKER_01: Because this same AI technology is not acting in isolation, is dealing with legacy systems, poor quality data, requirements, restrictions, employee resistance, complex workflows, um and um regulation.
• 11:03
  SPEAKER_01: So the AI model may be powerful, but if the organization is not ready, then you can't get the true value of it.
• 11:17
  SPEAKER_01: I can always think about a biblical reference, which is new wine in old wineskins.
• 11:25
  SPEAKER_01: Imagine a clunky 10-step process that had existed decades ago, and in this process, you have unnecessary approvals, duplicated activities, manual data entry, disconnected systems, and offs here and there.
• 11:45
  SPEAKER_01: And then somebody says, Let's transform this process with AI.
• 11:50
  SPEAKER_01: And all you do is you take AI, you put it in step five, and everybody rejoices.
• 11:57
  SPEAKER_01: Yes, we've brought in AI to this process.
• 12:00
  SPEAKER_01: But guess what has happened?
• 12:03
  SPEAKER_01: You've just made a bad process faster.
• 12:07
  SPEAKER_01: The process is still bad, it's just faster.
• 12:11
  SPEAKER_01: And that is the challenges we have.
• 12:14
  SPEAKER_01: Putting a 21st transformative technology into a 20th century process.
• 12:23
  SPEAKER_01: So when you want to look back, the question is not where should we put AI into this process?
• 12:30
  SPEAKER_01: The question is to look at the value chain completely and ask yourself the question: with this AI technology, what processes are to be designed?
• 12:42
  SPEAKER_01: What processes should still exist?
• 12:45
  SPEAKER_01: Why are five people touching the same transaction?
• 12:49
  SPEAKER_01: Why are highly skilled professionals gathering data instead of analyzing data to make decisions with it?
• 12:59
  SPEAKER_01: While we're asking human beings to do steps that the machines can do.
• 13:04
  SPEAKER_01: And on the flip side, too, you can say, why are we asking machines to make decisions that require human judgment?
• 13:11
  SPEAKER_01: Ultimately, it's not replacing everything with AI.
• 13:15
  SPEAKER_01: So we'll conclude this discussion with a tag from hype to impact.
• 13:23
  SPEAKER_01: So we'll say this.
• 13:42
  SPEAKER_01: The second chapter is saying what value can AI create.
• 13:48
  SPEAKER_01: The first chapter celebrated adoption.
• 13:54
  SPEAKER_01: The second chapter would demand transformation.
• 13:59
  SPEAKER_01: Here is the thing.
• 14:01
  SPEAKER_01: The organizations that succeed are not necessarily the ones that spend the most money on AI, but they are the organizations that have the discipline to ask the fundamental questions.
• 14:14
  SPEAKER_01: What problems are we solving?
• 14:17
  SPEAKER_01: What outcome are we trying to achieve?
• 14:20
  SPEAKER_01: How do we measure success?
• 14:22
  SPEAKER_01: And where should human judgment remain essential?
• 14:27
  SPEAKER_01: The AI on the moon may be over.
• 14:31
  SPEAKER_01: But the marriage between human intelligence and artificial intelligence is just starting.
• 14:40
  SPEAKER_01: And if we can approach it with discipline, with wisdom and courage, there'll be lots of value to create.