Artificial intelligence (AI) has been around since the late 50's. Its development and the interest around its different forms (is it Strong AI or Weak AI, Symbolic Reasoning or Machine Learning on Artificial Neural Networks) has been a rollercoaster.
Super hyped in the 60's and 70's then almost abandoned until the late 80's and 90's and now again in the spotlight.
Being a hot topic there is also a lot of confusion around AI. I have been personally working with and in companies that thought that Machine learning and Big Data were the same thing (they are not), or that overlapped the role of data scientist, data mining and machine learning.
Getting the foundation right is fundamental to make sense of AI and to understand how AI could help your organisation but it might be not easy as AI is still an open matter.
It has been said that AI practical application in improving business and processes is increasing at a pace that is well beyond most companies’ ability to adopt it. And this could be as well due to a potential misunderstanding of the nature of AI.
The history of AI is after all full of pitfalls and traps: don't assume that the latest wave represents always the best option.
It would be counterproductive, for example if you need to program robotics service in a retirement home, to opt for machine learning on a neural network despite the fact that this is the field that has been pulling AI latest development, probably some "Good All Fashioned AI" on symbolic reasoning would work better.
We will get to the specific in some other article, the key here is one: to benefit from AI, C-Suite executives will need to first understand what type of AI they really need based on the nature of their business, their audience and the industries evolutionary trends.
Also they will need to create the right culture (the cross-functional coordination and mid-manager sponsorship) required for the enterprise adoption. Ultimately, the value of AI is not about AI operating models themselves, but in companies’ abilities to understand them.
Design thinking introduces proven methods to boost all the above: achieve cross-disciplinary pollination that encourages employees to question, observe, network and experiment in an open and collaborative environment that diminishes hierarchy, removes biases and egos, challenges the status quo and encourages smart risk-taking.
Executives who promote design thinking in their organizations can accelerate AI adoption, drive alignment within the organisation and create commitment to their goals while reducing resistance to change.
Design thinking boost companies' Innovation Ability and allows to pivot quickly when market conditions change because alignment around a set of agile decision-making principles are already part of the culture of the company.
Using design thinking principals to accelerate enterprise adoption of AI requires five organizational shifts:
Realign technology and innovation. In many companies, innovation is something concerning technologists. They come up with an idea, and they run it independently in a vacuum. and this is a problem. Design thinking positions the responsibility for innovation across an organization, best accomplished by interdisciplinary teams with an end-to-end view of its impact. In today’s enterprises, AI must not be the confined in IT; business unit leaders need to understand it and promote it at a business level.
your first step is to create business unit, functional and change leaders who best understand their operating challenges. IT role is not to own AI but to enable groups to experiment, test, prove and deploy it.
Create inter-functional teams. Design thinking can bring technologists, executives, analysts, risk management, compliance, audit, operations, sales and customer-facing advisors together to generate new ideas and customer centricity.
These teams share learning across the enterprise. A strategy for enterprise adoption of AI based on design thinking pushes an organization beyond the implementation of a Center of Excellence (CoE), which can become the sole owner of knowledge, inhibiting innovation and creating unhealthy internal competition.
“Design” enterprise Innovation Ability. Making Innovation a regular and common target of a company culture helps a company scale its existing AI offerings and solutions, instead of always looking ahead to the next big thing. Organizational change is mostly feared because leaders try to reshape the destiny of their companies limiting the participation of employees. The result is fear and resistance. Design thinking practices encourages emplo to be engaengaged in shaping their destinoes. This is how leaders can turn resistance and other change barriers into competitive advantages.
Match smart employees with smart technology. Artificial intelligence presents patterns without making sense of them. When adopting AI we need to realise this, AI is often able to perceive patterns human can’t perceive. And the reason of these patterns is lost in t making sense of the Machine processing. It’s like black boxes agrees that that pattern makes sense for some
reason we can’t understand. They simply present the patterns. Expert will be still crucial to make sense out of these patterns. Matching The right expert with the right smart techno-logo based on objective observation will be a great source of advantage for organisations. If this sounds like a brave new world, it is, and it requires adoption of design thinking principles to quick adopt and embrace it.
Listen to real user needs and finding a purpose. Core to design thinking is the end-user experience. The ability of listening to real issues will allow organisations to come up with better solutions and new business directions. This could also Ben applies internally. Consider setting up a conference room to simulate the experience of working with AI-enabled digital workers, visiting other business units or companies to see how design thinking teams interact or using a third-party advisor to facilitate the design thinking environment.
So, what can you do to introduce design thinking into your digital transformation initiatives? Here are three recommendations:
First, conduct a self-assessment. Is innovation in your organization product-centric or user-centric? Are participants from multiple disciplines involved in representing the user viewpoint? Are you creating environments that encourage and reward the adoption of Innovation?
Second, select a reasonably high-value project and a team to approach it with the design thinking framework. Reduce risk by making sure the outcomes are well defined, so you can measure the impact. Observe how design thinking changes the team dynamics, innovation and results.
Third, when design thinking demonstrates value in one project, expand it across the enterprise. Share small successes, case examples and shout outs across the organization. Identify change agents who demonstrate a knack for design thinking and use them to facilitate other teams.
What I like about this is that it all makes sense: we need to quit treating innovation as a set of separate modules. Innovation it’s a process and it starts with understanding and it depends on the creation of a culture in which this process could thrive.