Artificial Intelligence has been the talk of the town for a while. But, why does AI matter? How can an organization successfully scale AI? And, what role does professionalization play in the process of successful AI deployment? Read this blog to know more about all things the data-driven AI landscape.
In the past three years, we have seen companies spend more than $300B on AI applications, and this has turned a spotlight on the AI landscape, making it a high-stakes business priority.
- According to the Forrester report, organizations that scale AI are 7x more likely to be the fastest-growing businesses in their industry.
- In a study by Accenture, it was found that 75% of global executives believe that if they don’t scale AI, they risk going out of business in just 5 years.
While scaling AI is crucial, most companies are still at the stage of running pilots and experimenting and struggling in achieving the value they expected.
So, why does AI matter?
In a study by Accenture, 84% of C-suite executives recognize the need to leverage AI to achieve their business goals. AI applications, with the help of machine learning and deep learning, can utilize the data in real-time and adapt to new changes to ensure that the business benefit is compounded. In this way, AI enables businesses to ensure agility with a regular stream of insights to drive innovation and competitive advantage.
As the innovation in the AI landscape progresses, we are inching towards an era where algorithms tell us all about our taste and preferences. Looking at this, we can say that AI in a leadership position doesn’t seem like a wild fantasy anymore.
The Covid-19 pandemic has left organizations vulnerable and have exposed their daily operations. This has alleviated the need for real-time insights and has opened our eyes to the gaps in the capabilities to access, mobilize and utilize data. Additionally, the air around AI is still not clear and this is causing challenges for business leaders who are geared up to scale with AI but are yet to introduce their teams to the “scale or fail” approach.
How do we scale AI?
To scale business processes, organizations must cultivate confidence in AI and design the right governance structure to allow an ethical collaboration between humans and machines. Additionally, it is important to define business and technical challenges that AI can help solve, and the efficiencies for stakeholders across organizations that AI can help achieve. Based on these, C-suite executives should prioritize the following technology and human capital investments to achieve their long-term goals:
1. Establish a Data-Driven Culture and AI Strategy
Here, we talk about one of the critical investments for an organization – Human Capital. It is necessary to create a company of believers and for that, an organization needs to leverage its goal of data-driven reinvention.
Organizations should work with Data Architect, Business owners and Solution architect to develop their AI strategy underpinned by Data strategy, Data Taxonomy and analyzing the value that their company can and wish to create. For
“Establishing a Data Driven culture is the key—and often the biggest challenge—to scaling artificial intelligence across your organization.”
While your technology enables business, your workforce is the essential driving force. It is crucial to democratize data and AI literacy by encouraging skilling, upskilling, and reskilling. Resources in the organization would need to change their mindset from experience-based, leadership driven decision making to data-driven decision making, where employees augment their intuition and judgement with AI algorithms’ recommendations to arrive at best answers than either humans or machines could reach on their own.
My recommendation would be to carve out “System of Knowledge & Learning” as a separate stream in overall Enterprise Architecture, along with System of Records, Systems of Engagement & Experiences, Systems of Innovation & Insight.
AI and data literacy will help in increasing employee satisfaction because the organization is allowing its workforce to identify new areas for professional development. This culture aims to educate employees to adopt an “out of the box” approach to facing rapid and unprecedented changes.
2. Choose a simple technological ecosystem
Clients, today, need organizations that value simplification of their system and vendor ecosystem. Enterprises should prioritize choosing the right AI/ML Technology provider partner, like Microsoft, with a capable partner and ISV ecosystem. To simplify these ecosystems, an organization needs to identify the functional gaps that exist, evaluate the applications that align the business strategy and streamline the infrastructure for ongoing operations.
3. Leveraging a “Universal language of business”
Who doesn’t hate a typical case of Chinese whispers? Organizations need to define a common taxonomy of business terms, including the KPI, ORA, leading indicators, and domain model. This should be implemented to avoid the need of an interpreter between 2 different users, so that everyone in the business (including extended partner ecosystem in the Supply Chain) speaks the same language and makes the right decision without any confusion. This Unified taxonomy should be pushed through consistently across “System of Knowledge & Learning”, System of Records, Systems of Engagement & Experiences and Systems of Innovation & Insight.
4. Reduce data “noise” to capture the right information
More data is not always better. In a world where data is proliferating and data begets more data, it can be tempting to gather more and more. Having a strong data strategy ensures you’re curating the right data to deliver the desired outcome and then capturing its insights to fuel an AI strategy that delivers that outcome at speed and scale.
5. Recognizing the need to professionalize AI
In a study by Accenture, three out of four C-suite leaders believed that if they fail to scale AI in the coming years, they will risk their business. As professionalization is the precursor to successful AI scaling, this has encouraged organizations to employ professionalization techniques like establishing multidisciplinary teams and clear lines of accountability.
To fuel the need for AI scaling, the pandemic has sharpened the contrast between those who have professionalized their AI capabilities and those who have not. Businesses are competing against each other to embrace new data capabilities to return to sustainable growth, which is possible through successful professionalization.
Explore the benefits of professionalization:
- When organizations adopt a professionalized approach of deploying trained, interdisciplinary teams, to work on these applications, you can successfully maximize the value of your AI investment.
- Professionalization helps organizations to achieve consistency in results when performing the same or similar actions in the future. Trained data practitioners build cutting-edge technologies across use cases by leveraging repeatability.
- Professionalization of AI processes contributes to making technological applications more ethical and transparent. This helps in building a culture that encourages trust. Companies need accountable processes to leverage successful responsible AI.
6. Leadership Training
There is a lack of consensus between our world leaders and we are not paying enough attention to training our leaders. This includes good leadership education for our business leaders, our political leaders, and our societal leaders. While scaling AI, many executives struggle when it comes to making sense of the business cases for how AI can bring value to their organizations. In the current world, these leaders are following a herd of their contemporaries who have referred to surveys that highlight the importance of engaging in AI adoption. But building a unique business case is not headlining their priority. The need of the hour dictates that our leaders can adapt and be agile to cope with the unprecedented circumstances. Leadership need to “Define AI value for today—with a vision for tomorrow”
7. Exploring Composite AI
AI will become the new co-worker. It will be critical for organizations to clearly define wherein the loop of the business process should they automate, where should the depend solely on machines, and where should they ensure collaboration between humans and machine to make sure that automation and the use of AI don’t lead to a work culture where humans don’t feel like they are the subordinates of the machines. Humans believe in building a culture where they communicate and represent the values of the company to create business value.
Leadership is about dealing with change. You need to understand what it means to be a human – you can have human concerns, taking into account that you can be compassionate, and you can be humane. At the same time, leaders should be able to imagine strategies for collaboration between machines and humans. This collaboration will be used to build strategies to combat the unprecedented and to brainstorm ways in which processes can be adjusted to create the same value. A leader needs to be able to make an abstraction of this, and AI is not able to do this.
With a long-term view, some of the other aspects that organization needs to plan for Scaling AI are:
- Transition from siloed work to interdisciplinary collaboration, where business, operational, IT and analytics experts work side by side, by bringing a diversity of perspectives and ensure initiatives address organizational priorities.
- Establish strong AI Ops practice for managing processes for developing, deploying and governance
- Shift from traditional leader-only decisions, rigid and risk averse to agile, experimental, and adaptable mindset by creating a minimum viable product in weeks rather than months and embracing the test-and-learn mindset. ● Define and follow Ethical AI framework and principles
- Ensure Data Security and Trust in the data
- Organize for scale– divide key roles between a central “Analytics Hub” (typically led by a chief data officer) and “spokes” (business units, functions, or geographies). ● Reinforce the change – With most AI transformations taking 2-3 years to complete, leaders must also take steps to keep the momentum for AI going during the journey by tracking the adoption, celebrating small success, and providing incentives for change.
The AI landscape is dynamic thanks to the constant technological innovations and C-suite executives recognize the need to leverage AI for a data-driven reinvention. The secret to scaling AI is cultivating confidence in AI and designing the right governance structure to allow an ethical collaboration between humans and machines. Professionalization is an integral part of scaling your AI and data practices. Enterprises that have leveraged professionalization to scale their AI processes are leading their industry when compared to their contemporaries who are still deliberating over ways to adopt responsible AI. By a clear understanding of what professionalization can do for the AI landscape, exploring the benefits, and employing correct leadership who can successfully delegate composite AI, an organization can make a considerable mark in the field of technological innovations.
How does your organization employ professionalization to scale AI processes?
