Results time – what to look for in the performance of IT companies
The 2nd fortnight of October will see most of the IT services companies come to the market and talk about their Quarterly results and indicate what the future looks like from their vantage point. I am attempting to give pointers on what to look for to gain a better understanding of the state of the companies / industry.
Additionally, for most of the Industry Apr-September is the best part of the year for business demand and the performance for the year 23-24 is kind of decided for the most part by this time.
I suggest we look for the following 3 pointers, besides the normal revenue/margin performance and large deal momentum, to understand the larger trends:
Head Count Movement: For most of the companies the headcount has been dropping and this is because of: a) excess supply in earlier years, b) unexpected project closures / budget cuts and c) lower demand in 2023 / 2024. I believe most of the excess supply is now dealt with. From here head count movement will indicate the strength & confidence the companies see from future demand.
Also, it would be good if companies started reporting growth numbers for ‘Billed persons’ to better understand the trends in business movement (today in most cases people have to derive these numbers from utilization and headcount numbers).
Budget Cuts & Ramdowns from Clients: In most cases there is no published information on this, but we should look for commentary on if the Budget Cuts of 2023 have stopped. For the growth numbers to pick up the bad news on Budget cuts has to stop.
Leadership stability: Last 2 years has seen a significant leadership churn in most of the Tier-1 companies (including CEO changes) and we should now watch out for companies where the churn is now settled and that the leadership teams have stabilized.
I have noticed that over the last 5-6 quarters Investors and larger public has not been able to reconcile the narrative / strength around AI / Large deal wins with the low growth and headcount reductions the companies have reported QonQ or YonY.
Large deal wins vs Revenue Growth: In 2023 and 24 most Analysts/Investors have lost confidence in the large wins reported because of their divergence with growth numbers. There are 2-3 reasons for this divergence:
While large deals wins indicate new business won, there is no corresponding numbers being reported on big Ramp-downs/Budget cuts and 2023 saw some large clients for most companies cut down drastically. So some or most of the new wins helped fill the holes created by ramp downs. That begs the question, should ‘large business losses’ be part of disclosure for companies?
Most companies report TCV won in a quarter and with the absence of the deal tenures its difficult to really calculate impact on current or next financial year. Compare a 500Million 10 yr deal vs 200Million 3yr deal to see how we can reach wrong conclusions. Is there a case for companies to standardize and report ACV (annual contract value) for wins and not TCV?
Lastly, the time lag between large deal wins and full revenue realization makes it difficult to predict revenue impact in current year. In most cases, such revenue realization is < 10% of deal value and that too if won in the 1sthalf the year.
Impact of AI on Growth and Jobs: The expectations from AI have been exaggerated both for good and bad outcomes thanks to the massive excitement which has gotten created since chat-GPT has been launched.
AI & Job losses: Till this point there have been no job losses on account of AI and linking AI to lower growth is incorrect. The productivity gains being spoken about so far have all been anecdotal and there is yet no magic which has been discovered which will take away jobs for IT services industry or their clients.
AI & Revenue growth: Even here, the industry is yet to see any large-scale revenue upside on account of new AI programs being started. The business enterprises are all still in Pilot/POC stage and yet to move to large scale leverage of AI. We already hear of how Enterprise AI adoption will now spread over a 10–15-year period.
AI preparedness: On this front most Service companies have ben ahead of the curve and are ready to ride the wave when the enterprise adoption picks up. Companies have been active on capability building, building POCs and in some cases finding internal use cases to demonstrate the power of AI.
All in all the Industry continues to grow into a mature stage with enough muscle and strengths now to navigate to evolving technology and business landscape. Of course, like all business the cyclical nature of this can not be avoided.