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Welcome to the Tech A newsletter Deep-Dive. This is the final part of my state of AI series.
In Part 1 (read here), we uncovered how businesses use AI and where they see value, finding that only 26% of companies generate tangible returns despite massive investments. Part 2 (read here) focused on answering why the majority struggle and what differentiates successful implementations from failures.
This week’s deep dive presents the most effective strategies to implement AI. This playbook combines real use cases with enterprise survey results from more than 3000 companies (conducted by BCG, Deloitte, McKinsey, Dataiku, and more).

⚡ Executive Summary

Companies Scaling AI have achieved an estimated 2x success rate and 3x the return compared to others. However, a significant 76% of C-suite executives admit to struggling with scaling AI, and three out of four believe failing to scale AI within five years could threaten their business.

The winning playbook: The path to achieving this involves five critical steps:

1. Start with Strategy:
   Align AI with business objectives, balancing quick wins and transformative projects. Follow the 10-20-70 rule—10% focus on algorithms, 20% on tech, and 70% on people and processes. Secure leadership buy-in and set realistic 1-2 year timelines for scaling.

2. High-Impact Pilots:
   Begin with small, high-value projects to build momentum and demonstrate ROI. Focus on core business processes that drive revenue and track results meticulously to fuel further AI investments.

3. Build the Foundation:
   Invest in essential infrastructure and quality data. Address data challenges early, ensuring AI tools integrate seamlessly with existing systems. Prepare teams through training and process adaptations.

4. Scale AI:
   Form cross-disciplinary teams, establish AI Centers of Excellence, and create governance frameworks. Address gaps in technology, data, and strategy to support organization-wide scaling efforts.

5. Drive Adoption:
   AI success requires cultural change. Embed AI into workflows, reimagine processes, and foster digital literacy across teams. Use platforms and reusable assets to accelerate deployment and track progress with robust metrics.

Bottom Line: Successful AI initiatives prioritize organizational transformation, focusing on people and processes first. Companies that adopt this structured approach see stronger returns and lasting competitive advantages.

Summarizing it boldly, we know with facts & testimonials that a considerable return on investment is achieved when AI is scaled across organizations. However, most organizations do not attain that stage.

Consider this survey results by Accenture:

Most C-suite executives believe achieving a positive return on AI investments requires scaling across the organization. Yet 76% acknowledge they struggle when it comes to scaling it across the business. Moreover, three out of four C-suite executives believe that they risk going out of business entirely if they don't scale AI in the next five years. (Accenture: Built to Scale)

The majority of C-suite executives who struggle have already started losing enthusiasm. Deloitte's Q3 2024 survey reveals that executive interest has dropped 11 percentage points since Q1. Time is of the essence.

This playbook, built on extensive research from leading consulting firms and real-world implementations, offers a proven path to success. Let's dive into the specific steps that set successful organizations apart.

The promise: if you scale AI across your organization, you get 2x the success rate and 3x the return.

1) Focused strategy & High level commitment

The journey begins with strong foundations. BCG's research reveals that leaders follow the 10-20-70 rule: 10% focus on algorithms, 20% on technology, and 70% on people and processes. This isn't arbitrary - companies following this model are 55% more likely to generate value from their AI initiatives. This pattern also applies to digital transformation efforts in general.

Key focus areas:

• Secure C-suite commitment with clear accountability and dedicated leadership

• Create a focused strategy linking AI to business objectives: You need a balanced approach. Target both quick wins and transformational opportunities [Citation: "67% of organizations are increasing investments in GenAI because they have seen strong value to date" - Deloitte]

• Spread your organization's effort according to the 10-20-70 rule. It's always about people and processes first, then technology.

• Set realistic 1-2 year implementation timelines: while you might see some success in the short run, true AI scaling across your organization takes time.

Why This Works: Deloitte's research shows that organizations with clear strategic alignment achieve 3x better outcomes.

2) Start with High-Impact Pilots

As one global head of AI at a pharmaceutical company notes, "There's a lot of willingness to test and experiment, but people might get disappointed if it's not paying off fast enough." [Deloitte]

Humans run organizations; we need to see signs that it's working in the short term. Then we can commit and trust that the much more significant compound effects, in the long run, will arrive (+2 years)

Successful companies focus on what BCG calls "lighthouse initiatives"—high-impact, achievable projects that demonstrate value quickly. You need those to generate revenue, increase buy-in from C-level executives, and reinvest in scaling AI.

"Leading companies are well on their way to creating significant value...a consumer products company applied GenAI to reduce costs by $300 million through productivity gains" (BCG Report)

Key focus areas:

• Select 2-3 high-impact, low-complexity use cases: I covered those in parts 1 and 2.

• Focus on core business processes, not just support functions.

• Document learnings and ROI meticulously

• Create a standardized implementation playbook

We've covered many AI use cases that deliver real-world value in part 1 and part 2 that deliver business results. Here are a few additional examples by industry:

3) Build Your Foundation

To leverage AI capabilities quickly, you need a minimum setup that you would gradually improve with your growing return on investment.

From a technical point of view, you need to integrate AI with your existing IT system and get access to quality data (otherwise, it's garbage in, garbage out). Start with minimum viable infrastructure and scale while keeping the end in mind.

In the age of AI, data is the new oil. Data challenges can derail even the best AI initiatives. According to Deloitte, key focus areas are:

• Enhanced data security (54% of leaders)

• Improved data quality practices (48%)

• Integration of internal and external data

Many organizations are learning that they can't start with Generative AI until they address their data deficiencies. Activities such as LLM tuning and training require high-quality data that is free of issues related to privacy, confidentiality, and intellectual property (Deloitte).

From a people and processes perspective, adapting your way of work (processes) and training people on AI takes time but needs to start from the early days.

4) Scale AI

At this point, you've used AI and GenAI where appropriate to drive efficiency, productivity, and cost reduction through large-scale deployment—but don't stop there. The organization should consider reinvesting the freed-up time and budget to scale AI across the organization (innovation, product development pipeline, etc.).

Scaling requires a delicate balance of speed and structure. The data shows organizations highly prepared for scaling excel in Technology infrastructure (45%), Data management (41%), Strategy (37%), Risk and governance (23%)

Key focus areas:

• Identify your capabilities gaps when compared to top AI scalers.

• Form multi-disciplinary teams: 92% of strategic scalers leverage them (Accenture)

• Create an AI Center of Excellence to centralize oversight and follow through with the implementation progress.

• Establish a clear governance framework

• Make your Data & Technology infrastructure ready to scale while continuing to work on people & processes long term.

• Set up guardrails to deploy AI responsibly in all initiatives through transparency, control, and accountability to ensure ethical and legal compliance and to manage business risks.

Drive Organization-Wide Adoption

Success requires more than technology—it demands organizational transformation. According to Deloitte, "deeply embedding GenAI into functions and processes" is the top value driver (22% of respondents).

Key focus areas:

• Focus on end-to-end transformation and people and processes: redesigning ways of working, cultivating talent, reimagining processes, strengthening effective decision-making, and addressing reluctance to adopt new solutions

• Adopt a digital platform mindset: through reusable assets on platforms, successive AI programs achieve 3-5X faster to market at lower spend (Accenture)

• Measure: Define both financial and non-financial metrics and set tracking mechanisms

💡 Looking Ahead

The companies that succeed with AI aren't necessarily those with the most significant budgets or advanced technology. They're the ones that follow a systematic approach, focus on business value, and remember that true transformation is about people and processes first, technology second.

One successful AI implementation leader said, "These projects need to demonstrate how they would improve the company either through cost savings, increased operational efficiency, or revenue generation." BCG

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Sources:

• IBM: AI in Action 2024 Report

• Deloitte: State of GenAI Q3 2024 Report

• Accenture: AI: Built to Scale Report

• BCG: Where’s the Value in AI? Oct 2024

Amine Rabehi

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