Winning your AI journey - tune out the noise and tune in to the music!

It is an acknowledged fact that data and Artificial Intelligence (AI) are pivotal levers of digital transformation, which can boost the competitiveness of businesses. But in what is an emerging trend, data and AI now have a new stakeholder in the organisation - the CEO. The cloud makes it possible for enterprises to scale and embed a data-driven approach into every business process. As a result, AI can enable the kind of value generation that CEOs are interested in – not incremental but transformative value. 

Besides the proven business benefits of a data-driven approach, there is one more dimension that is crucial to the CEO agenda. Responsible business is taking centre stage in board rooms and the need to mitigate risks for clients, employees and society is a top priority. This approach extends to how organisations innovate with AI. The responsible use of AI translates to building and deploying AI in an ethical framework that ensures that it is applied fairly and consistently, and in a manner that empowers and engenders trust, and allows companies to scale AI with confidence.

Whilst there is keen interest from CEOs in unlocking value using AI, there are also considerable challenges in getting there. A 2019 Accenture research  found that over 84% C-suite executives believe they won’t achieve their growth objectives unless they scale AI. And yet 76% of the executives said that they struggle with how to scale AI across the business.

So, how does an organisation go about realising this significant opportunity? I see ‘the way’ led by five key elements – AI strategy, deployment, talent, the ‘right’ data, and governance. Here is my take on how the exemplars – the companies that have successfully leveraged data and AI - have managed each of these elements:

1. Strategy

To being with, having a company-wide AI strategy is crucial. Given we live in a world where execution informs strategy, exemplars keep their strategy formation window deliberately short. They take a twin approach- they run AI ‘programs’ in areas where they are proven results, move to scale from design and apply AI ‘projects’ in bespoke areas that truly differentiate their organisation. In parallel, they make the right decisions to build vs. borrow vs. buy. Exemplars strategically stack their ecosystems, keeping them small in number but deep in terms of partnerships.  Exemplars adopt open, inclusive architecture.

2. Deployment

Exemplars have a clear picture of their pain points, the stakeholders impacted by these pain points and ensure that their AI solution is centred around these from the get-go. This guarantees last mile adoption.

Another key best practice is to keep the larger organisation abreast of AI initiatives. While a typical AI program might be focused on transforming one part of the organisation - say finance, it is important to share insights with functions so that there is a wider appreciation of the journey and the value unlock as a result of the program. Constant learning through AI learning sessions, learning pods, bite-size learning and sharing are critical to elevating confidence in AI, and the value that it can bring.

When it comes to operating models, while there isn't any one right answer, exemplars follow one clear principle: they choose sponsor over value. Simply put, they focus on AI deployment where there is great sponsorship. This is essential to building a snowballing momentum towards AI adoption across the entire organisation.

3. Talent

AI is a team sport, and requires cross disciplinary and diverse teams across the organisation to work together. We are starting to witness how talent specialised in areas such as human resources, psychology, legal, etc is providing the right direction to AI programs. This diversity is key to ensuring adequate governance and should be scaled up across AI projects.

With regard to AI technology talent, it is not hard to hire or grow skilled talent to execute AI projects and proof of concepts (PoCs). But it is hard to find talent experienced in building and deploying machine learning models at scale and in production environments. While a lot of platforms and low code environments show promise, we are at least a few years away from having access to mature AI talent.

India is an acknowledged global powerhouse of advanced technology talent. This talent gap presents the country with an opportunity to leapfrog as a hub for AI and data skills by building a multi-dimensional talent base that extends beyond just disciplines such as mathematics, science, statistics, econometrics and operations research.

4. The ‘right’ data

To say that ‘data is the new oil’ is a cliché because your data is valuable only of you have achieved something using it.  Leaders play offence on data.

One of the biggest learnings around data is to execute the data program a sprint or two ahead of the data science program. This has a profound effect on arriving at the ‘right data’ needed. Leaders need to shift the mindset from big data to the ‘right’ data.  

5. Governance

AI technologies are being created at break-neck speed. However, governance around them has not innovated at the same pace. Regulated industries typically have a mature systems of risk controls.  But AI introduces new risks, which are not always widely understood. When it comes to Ethical AI design principles- there are eight different dimensions that need to be carefully orchestrated, namely Soundness, Fairness, Transparency, Accountability, Robustness, Privacy and Sustainability. AI systems can inadvertently contravene laws on bias and discrimination unless the right controls are in place to support scaling with confidence. AI will have a profound effect on mankind, and the next few years will mark how we manage governance and assemble a sound framework of AI standards.

As organisations scale up their use of AI and the data that fuels it all - to power every single process and stakeholder - wrapping their heads around these five elements is critical to winning in this journey.

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