Ruchir Nath is a leader at Dell Technologies, with experience leading and leading global teams.
With the advent of artificial intelligence (AI) and machine learning (ML), as well as significant corporate investments in hardware and employee training, business operations/strategies and planning functions are on the point of experiencing a major transformation. These technologies promise to significantly improve productivity and decision-making capabilities.
At the highest level, business operations encompasses several essential pillars, each intertwined with the others. However, due to human limitations, time constraints, and the need for parallel execution of these activities, decisions often do not consider the bigger picture. Here’s how AI/ML can revolutionize each pillar.
Planning
Traditionally, financial planning involves the analysis of historical data, market projections, high-level sales productivity KPIs, attrition rates and product mix. This process typically goes through several iterations and involves interweaving with corporate FP&A, business unit financing, and sales financing. AI/ML can streamline this by accessing all historical numbers and market data, predicting at the rep level (e.g. attrition risk, better attrition predictions), and analyzing GTM models and coverage of existing representatives.
These technologies can process large amounts of data in parallel, providing well-thought-out plans based on clear assumptions. They can also provide scenario analysis, allowing finance and business leaders to adjust variables and see the impact, avoiding the “black box” problem that hinders adoption.
Release to market (GTM)
GTM is one of the most complex problems due to the large number of variables including account sets (large, medium, small and medium), representatives (tenure, type: inside, outside, specialty, location) and the territory and neighborhood planning. The goal is to design the most optimal GTM strategy that can be executed rather than a perfect strategy. AI/ML can connect all of these data points more efficiently and quickly, providing scenarios and options that can be reviewed and adjusted as needed.
Manage the business
Management reviews typically provide performance updates and analyzes to identify trouble spots or problems and suggest actions to ensure goals are met. AI/ML can analyze recent data, conversions, account patterns, sales activity, attrition, and GTM changes to provide a comprehensive understanding of underlying issues and potential solutions. This can shift the focus of assessments from 70-80% towards the past to 70-80% towards the future, highlighting risks and opportunities.
Business partnership/financial control
This role encompasses performing all of the above activities in the field, enabling sales calls/prospects, pricing, paying reps, managing transactions, inspecting pipeline and conducting programs/games with the sales team. AI/ML can enhance these functions by providing real-time insights and predictive analytics, ensuring decisions are data-driven and aligned with overall business goals.
For example, AI/ML can improve predictive analytics on transaction management and opportunities based on quotas, account sets, and outlier transactions. This can help create data-driven guidelines for rep compensation reviews and predict potential cascading deals. Additionally, many CRM companies like Sales force And Oracle are already integrating AI/ML into their tools, which could further increase seller productivity.
Challenges Organizations Will Face With Implementing AI/ML
Adopting AI/ML technologies is a significant transformation, often complicated by the need for both cross-functional and function-specific solutions. Challenges include executive vision/sponsorship, change management, talent, data quality/democratization, explainable and reliable results, and training.
To effectively manage transformation projects, it is not enough to define a vision and objectives at the top. These should cascade down to the lowest level, ensuring that each level understands how the vision and end goals relate to their specific roles. Leaders should not just reiterate the high-level vision, but explain its importance and impact on each role and what is expected of them.
Leaders at all levels must establish informal relationships with frontline workers or the two or three levels below to gain honest insight into the transformation’s progress, risks, and obstacles. This is crucial because formal reporting can mask problems. Access to honest feedback from stakeholders at lower levels makes a significant difference.
Conduct multiple training sessions at different levels to ensure full understanding and adoption. Repeated training is necessary, as initial sessions may not reach everyone or be withheld entirely. Training must be organized and its impact explained according to the level of organization at which it is provided. Patience and perseverance are essential because change takes time.
The Future of AI/ML in Sales Operations
In my opinion, this is just the beginning. Currently, AI/ML is being applied to individual functions such as sales operations, corporate FP&A, supply chain, product engineering, and marketing. The real magic will happen when all of these functions are connected, providing end-to-end predictive analytics across the entire enterprise. This integration will allow each decision maker to see the impact of a change or decision on other functions.
For example, AI solutions for CRM can make salespeople more productive in managing their accounts, allowing them to close deals faster and with higher conversions. This information can be used by AI models in supply chain management to predict upcoming trends and optimize supply chain operations. Likewise, product performance information can be shared in real time with pricing teams and product line managers, allowing them to adjust prices, discounts or create new competitive strategies based on current data.
Final Thoughts
I am personally excited about the potential of this technology. If used correctly, AI/ML can drive incredible productivity and enable decision-making at unprecedented speeds. However, it will also require new business processes, new management cadences and new skill sets.
Future professionals will need to have a broad understanding of multiple functions, such as finance, supply chain and product management, rather than expertise in a single area. Those with varied skills will be better suited to this transformation, and those with experience in a single domain will play a vital role as an SME to help data scientists implement and test the models based on their domain expertise.
In conclusion, AI/ML has the potential to significantly improve the efficiency and effectiveness of business operations. By leveraging these technologies, organizations can make more informed decisions, optimize their GTM strategies, and drive better business results. The key to successful adoption lies in ensuring transparency and understanding of the assumptions used by AI/ML models, overcoming traditional barriers to adoption.
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