How do you create an AI culture for business intelligence?

AI culture is the next step to your data culture. Does your company have an enterprise data hub, a data visualization tool on top and how many of your employees jump on to that tool to make a data driven decision or to gain insights? Find your biggest users and problems and start applying AI to these. Your company will smoothly transition from a data driven culture to an AI culture

A retail company I worked for exemplified this by articulating its AI vision to enhance customer experience and streamline inventory management. By setting clear objectives like reducing checkout times by 30% and optimizing stock levels, they aligned AI initiatives with business goals. Communicating this vision across the organization, they fostered a shared understanding and commitment, inspiring teams to work cohesively towards realizing the transformative potential of AI in retail operations.

Also consider fostering Continuous Innovation and Adaptation. In business intelligence, it's essential to cultivate a culture of continuous innovation and adaptation in AI. This includes: Promoting experimentation with new AI tools and techniques. Supporting a learning environment focused on AI advancements. Viewing failures as valuable learning experiences. Engaging with the broader AI community for insights and trends. Regularly updating AI strategies to stay relevant. Such practices ensure a dynamic, responsive AI culture that leverages current and future AI capabilities in business intelligence.

It's essential to assess the readiness and willingness of your company's senior leadership and teams. You may have impeccable transformation plans and execution capabilities, but if the company isn't prepared or inclined to embrace change, the road ahead can be challenging.

You can't do things backwards. If you already have a future-oriented and motivated culture then it should be easy to introduce new innovations or launch new projects such as AI. But you cannot build an AI culture from scratch if you do not already have a strong culture in general. So you need to explain the benefits of AI with all stakeholders and get them involved in a transparent discussion about how it works, what it will achieve for everyone, what will need to change, what new skills will be required, etc. Then, with everyone on board, explore some potential use cases and develop an AI strategy for a Pilot project. Once that is successful and everyone is happy with it, then build an organization-wide project with an AI Team.

Fostering an AI-driven culture in business necessitates a foundational commitment to AI skills and education. In today's rapidly evolving technological landscape, merely having tools isn't enough; businesses must cultivate the human talent to harness the potential of those tools. Prioritizing AI education ensures that your team isn't just AI-informed but AI-empowered, capable of discerning nuances, asking the right questions, and deriving actionable insights. Whether it's through in-house workshops, collaboration with academic institutions, or tapping into cutting-edge online courses, such an investment goes beyond mere skill acquisition. The ROI is clear: a team that not only understands AI but thrives in its possibilities.

Conspiracies against the laity, a term coined by George Bernard Shaw where he explores that professionals, such as doctors and lawyers, use a difficult language to confuse regular people. This exclusivity helps them to stay in charge, charge high fees while controlling information and influence. In simple terms, Shaw believes some professions make things complicated to keep the average person dependent on them. I feel the same way about how the communication around AI has been. Make it easier to be a part of the discussion.

The idea is to use AI to your advantage and not get bogged down on how to set up a data platform that is AI/ML ready. Organisations need a curriculum that focuses on building AI users and consumers and teams that can think of use cases and available tools.

The hype triggered by generative AI this year and the subsequent discussions in mainstream media led to a lot of misconceptions around this topic. A significant number of employees no doubt feel apprehensive towards AI tools becoming part of their work. Organizations therefore must first and foremost address this mindset by allowing employees to get comfortable with AI tools and understand what they can and cannot do. Providing a safe environment where employees can try out tools for themselves, sandbox like, will help.

It's about keeping pace with technological advancements, staying ahead and harnessing the potential of AI for targeted applications like advanced wound care. A knowledgeable team can extract maximum value from these tools, leading to improved clinical decisions and better patient outcomes. By prioritizing AI education, healthcare facilities can ensure that their staff can adeptly integrate AI solutions into their workflows, interpret AI-driven insights, and utilize them for precision care. For wound care specialists, this means a deeper understanding of predictive analytics for wound progression, tailoring treatments based on AI recommendations, and achieving consistently high standards of patient care.

Agile practices are a perfect fit for companies cultivating an AI culture. Key agile principles for AI projects include: - Cross-functional teams: Collaboration with diverse teams, including technical teams, legal, security, domain experts, and users is crucial for AI. Get this group involved early & often for continuous feedback. - Rapid experimentation: AI projects require testing and validation at every step, so testing quickly is key. AI sprints should be weeks or days, not months. - Fail fast, learn fast: AI isn’t a silver bullet to every problem, and experience is the best teacher. Regular retrospectives foster open discussions on failures and learning.

Again, if you have a cross-functional team culture established and working well, thencollaboration on a new AI project will occur naturally.

In my mind, creating a collaborative and agile AI environment is like assembling a diverse orchestra to create harmonious music. Consider the development of a customer service AI. It isn’t solely a task for data scientists; input from customer service reps, feedback from marketing, and insights from IT are integral. Every department plays a unique note, contributing to a comprehensive, adaptable solution. This synergy, akin to musicians adjusting to each other’s tempo, allows for rapid refinements and innovative solutions, turning potential discord into a melody of progress and ensuring the final composition – the AI outcome – is a masterpiece of collective effort and adaptability.

Creating a collaborative and agile environment is pivotal when integrating AI into advanced wound care. This kind of setting fosters innovation, allowing teams to rapidly adapt to new AI insights and continuously refine care protocols. As AI tools present new findings, an agile environment ensures that clinicians can swiftly incorporate this knowledge into their practices. By promoting open communication and flexibility, healthcare professionals can harness AI's power while ensuring that patient care remains personalized and effective. This synergy between AI and human expertise ultimately elevates the standard of wound care.

Agile and collaborative approach is the key to sustainable growth and success. One suggestion to assist in this process is to highlight and communicate the segments of work that you are not confident about or done using AI tools to be proofread by experts.

Governance and ethics play a crucial role when implementing AI in advanced wound care. As AI-driven solutions analyze and predict wound healing trajectories, there's a responsibility to ensure the technology adheres to ethical standards. Establishing clear governance structures ensures that AI algorithms are transparent, fair, and free from biases. Moreover, safeguarding patient data and respecting their privacy becomes paramount. By prioritizing ethical considerations and instituting robust governance mechanisms, we can harness the transformative potential of AI in wound care, ensuring it serves both clinicians and patients responsibly.

If you are introducing AI business wide, there must be governance - as you would with any new tech implementation. Teams should understand and be comfortable with how and why any AI tool is being used in the business, and how it supports their role.

Like 'Privacy by design' for GDPR, 'Responsible by design' principles and processes need to be designed to build safe and ethical AI systems. Ensuring Fairness is a socio technical challenge, and purposeful design?of models mandates a governance process involving the various stakeholders. Probably a good start for organisations,?especially considering that ???AI regulations are being formulated in various jurisdictions right now, corporates can engage with the supporting bodies for foundational policies and leverage any federal support like Regulatory sandboxes in some countries.

We can empower an entire enterprise, from the bottom upwards, to participate in the ability to properly & ethical use AI systems Engrain (Framework), Automating (Processes) & Democratize (Inclusion). Sense of belonging is a powerful driver. How do we ensure our employees feel like they belong alongside this? This will create positive culture. AI is meant to make life better, more simple and accessible. Inundating employees with a new mountains of frameworks and additional processes isn't the way to do it - Informed Inclusion however. What we want and what we need are two different things - It's often those on the frontlines that understand the true improvements better than managers.

For example, I currently help a healthcare organization implementing AI to predict patient outcomes. Establishing AI governance and ethics is paramount. The organization sets up clear guidelines ensuring data privacy, addressing potential biases, and ensuring transparent decision-making processes. They involve patients, healthcare professionals, and legal advisors to collaboratively address concerns and evaluate solutions. This not only builds trust among stakeholders but also ensures the AI system is aligned with ethical standards and societal values, fostering a culture that puts patient welfare and data integrity at the forefront.

As a leader & practitioner, storytelling is the mantra to spread success. To foster an AI culture, every successful project should be shared across the organisation including the best practices, challenges and risks. 1. Organisation can achieve economies of scale with implementing AI systems in other user journeys with a different context. Educate the context, purposeful design, data used for training, the algorithmic logic, risks involved if any. 2. Also to sustain business value from AI systems, teams to institute vigilant monitoring to check for data drifts and performance drifts, post successful production. 3. And yes, involve the ecosystem like partners & stakeholders in your journey

A successful AI project should be celebrated with all stakeholders. That success will become an automatic element of corporate branding. It will attract digital employees and digital customers.

Here is my take on this: - Exhibit humility in AI successes, prioritizing learning from errors as AI often yields unfavorable results. - Celebrate achievements privately, keeping the team focused on rectifying mistakes and enhancing systems. - Share AI successes with a learning-oriented narrative, emphasizing the journey and lessons learned over mere triumphs. - Fostering a culture of continuous improvement, resilience, and collective growth to navigate the complex, evolving AI landscape. - This approach drives sustainable, long-term success, ensuring teams are better prepared for future AI challenges and opportunities.

This is such a key step, in essence it is Reinforcement Learning for the humans in the enterprise. AI within an enterprise is very much a case of building on the shoulders of giants, one good project leads to another and then another. The more this happens in an open and transparent way within the business the better.

I worked for a retail company. When we had successfully implemented AI to optimize its supply chain, it was a cause for celebration. We recommended the company to threw an appreciation event for the team behind the project, acknowledging their hard work and innovation. They also shared this success story through a detailed case study on their website and press releases, showcasing the measurable impacts and improvements made. This not only acknowledged the team's efforts but also demonstrated the real-world value of AI, inspiring other departments to explore AI solutions and fostering an enthusiastic, AI-driven organizational culture.

I would also like to highlight the need to retrain or help people see the value in their skills they have in order to be able to migrate into the field of #AI. For example a traditionally trained architect can retrain in AWS or Azure architecture. Any developer can retrain in Python. A gepspatial person understands multidimensional data and may have applied AI in their work with datasets. There are many opportunities for people to come from non traditional paths into AI and we need to make sure they can step across.

Culture isn’t just created because AI is here. Culture in an organization covers everything from external brand perception, marketing, hiring, security practices, and ultimately the way employees interact with each other. If employees don’t understand or have a collective hatred/misunderstanding of AI, well then you guessed it the culture will become on of the community. Culture isn’t a few people fighting for an idea, culture is a community of ideas, stories, behaviors amongst a group of people. So it’s a false sense of understanding as AI becomes a normal conversation to assume everyone is on the same page.

In God we trust , rest all should bring Data - that's my starting quote in any of my Ai implementations across the company. It's often said that " Culture eats strategy for breakfast", hence to change the culture or the way of life or simply say " BAU" we need to strategise the womb to the tomb of data handling first and then see the impact gradually set in. How do we do this?? Well start with best practices like having weekly retrospection based on dashboards fed by insights. Then build an interplay or a layer of " Data Quality" and finally let there be a stage gate for feasibility of forthcoming projects based on insights driven by Ai or ML algorithms to qualify as doable or not. That is when the culture will be set...

AI technology, tools and processes need to be treated similar to implementing a new platform. Just like you would build a robust process, train and educate teams on why there is a new platform, and why... bring people on the journey. AI tools are here to help, enhance and support the work we do.